# A Machine Learning Approach to Quantitative Prosopography

Aayushee Gupta, Haimonti Dutta, Srikanta Bedathur, Lipika Dey

###### Abstract

Prosopography is an investigation of the common characteristics of a group of
people in history, by a collective study of their lives. It involves a study
of biographies to solve historical problems. If such biographies are
unavailable, surviving documents and secondary biographical data are used.
Quantitative prosopography involves analysis of information from a wide
variety of sources about “ordinary people”. In this paper, we present a
machine learning framework for automatically designing a people gazetteer
which forms the basis of quantitative prosopographical research. The gazetteer
is learnt from the noisy text of newspapers using a Named Entity Recognizer
(NER). It is capable of identifying _influential_ people from it by making use
of a custom designed Influential Person Index (IPI). Our corpus comprises of
14020 articles from a local newspaper, “The Sun”, published from New York in
1896. Some influential people identified by our algorithm include Captain
Donald Hankey (an English soldier), Dame Nellie Melba (an Australian operatic
soprano), Hugh Allan (a Canadian shipping magnate) and Sir Hugh John McDonald
(the first Prime Minister of Canada).

###### Index Terms:

Gazetteer, Text Mining, Information Retrieval, OCR, Spelling Correction,
Historical data, Influential people detection.

##  1 Introduction

Historical newspaper archives provide a wealth of information. They are of
particular interest to historians [1], genealogists (e.g. Genealogy
Bank111http://www.genealogybank.com/gbnk/,
Ancestry222http://www.ancestry.com/) and scholars. An important use of
historical newspapers is for People Search[2, 3] – the process of finding
information about a person and reconnecting them with others they are likely
to know. The goal is to determine who knows whom and how. This is often
achieved by studying biography. In historical groups, however, biographies may
be largely untraceable. In such cases, secondary biographical information is
studied by examination of the individual’s experience and personal
testimonies, some of which may be reported in newspaper articles.
Identification of this group of individuals and studying the stories of their
life is an important tool in the research historian’s arsenal - called
prosopography. It can be used to learn social structure such as analysis of
the roles of a certain group of people, holders of titles, members of
professional and occupational groups or economic classes. Quoting
prosopographer Katharine Keats-Rohan,

> …prosopography is about what the analysis of the sum of data about many
> individuals can tell us about the different types of connection between
> them, and hence about how they operated within and upon the institutions –
> social, political, legal, economic, intellectual – of their time.

The nature of prosopographical research has evolved over time. Lawrence
Stone[4] discusses an “older” form of prosopography which was principally
concerned with well-known social elites, many of whom were influential people.
Their genealogies were well-researched, and social webs and kinship linking
could be traced, allowing a prosopography of a “power elite” to emerge. This
older prosopography can be contrasted with a newer form called _quantitative
prosopography_ , which studied much wider populations including “ordinary
people”.

In this paper, we present a framework to develop a people gazetteer which
forms the basis of prosopographical research. The gazetteer is built from the
text of historical newspapers subjected to Optical Character Recognition (OCR)
and is capable of identifying influential people. Our paper has the following
novel contributions: (1) Development of the People Gazetteer – an organized
dictionary of people names and a list of newspaper articles in which the name
occurs. (2) Identification of Influential People: we define an Influential
Person Index (IPI) which helps in identification and ranking of influential
people.

To the best of our knowledge, the development of a framework for doing
prosopographical research using machine learning has not been studied before.
This exercise, however, opens up a wide range of possibilities – for example,
to build influential people networks that can learn about entities involved in
historical events. Such applications can immensely help historians working on
prosopography[5] and scholars in learning events related to historically
significant people interactively.

Paper Organization: This paper is organized as follows: Section 2 discusses
related work; the machine learning framework is discussed in Section 3; the
characteristics of the data used for this research are presented in Section 4.
Sections 5 and 6 present the development of the gazetteer and the influential
people detection process; empirical results and discussions are presented in
Section 7 and 8 and Section 9 concludes the paper.

##  2 Related Work

In this section, we review two types of related literature - digital
humanities projects which build gazetteers from text and the process of
identification of influential people from data.

###  2.1 Gazetteers for Digital Humanities Projects

Newspaper archives have been studied extensively for the design of search and
retrieval algorithms ([6, 7, 8, 9]), summarization([10, 11, 12, 13, 14]),
sentiment analysis ([15, 16, 17]), topic modeling ([18, 19, 20, 21, 22]),
clustering([23]), classification ([24] and visualization([25],[26]). The
National Digital Newspaper Program (NDNP) in the United States333This is a
partnership between the National Endowment for the Humanities (NEH) and the
Library of Congress (LC)., is a long-term effort to develop a searchable
database of U.S. newspapers. Historical newspapers available from this project
(named Chronicling America444http://chroniclingamerica.loc.gov/), have been
used for topic modeling[27]. Newman et. al[28] use a combination of
Statistical Topic Modeling and Named Entity Recognition for analyzing entities
and topics. Lloyd et. al [29] discuss their approach for designing a news
analysis system555http://www.textmap.com where information about several types
of entities can be searched. They perform temporal and spatial analysis and
present time series popularity graphs based on the number of reference and co-
reference names for the entity.

Several digital humanities projects that have used machine learning and
natural language processing techniques to learn from historic newspaper
archives are relevant to this work – the libraries of Richmond and Tufts have
examined the Richmond Times Dispatch during the civil war years for more than
two decades and their work focuses on automatic identification and analysis of
full OCR text in newspapers to provide advanced searching, browsing and
visualization[30, 31, 32, 33].

Developing gazetteers from news articles is a well established technique -
different types of gazetteers are discussed under the General Architecture for
Text Engineering (GATE666http://gate.ac.uk/sale/tao/splitch13.html) framework.
It defines a gazetteer as a set of lists containing names of entities (such as
cities, organizations, days of the week, etc) which can be used to find
occurrences in the text. We use this definition to develop our People
Gazetteer that finds person name entities from a news article repository and
associates each unique person entity with the list of articles in which they
occur.

Gazetteer lists are also discussed in [34] where they are used for learning
named entity taggers using partial perceptron and aid in performing better NER
compared to CRF based entity taggers. Zhang et. al[35] discuss automatic
generation of gazetteer lists by finding entities with similar labels from
Wikipedia articles. Allen et. al [5] describe an exploratory study for
developing an interactive directory for the town of Norfolk, Nebraska for the
years 1899 and 1900. Their work focuses on providing structured and richer
information about the person entities by linking their occurrences with
associated events described in historical newspapers.

###  2.2 Influential People Detection

In prosopography, identification of the “social elite” plays an important
role. Their experience and personal testimonies may be reported at length in
newspaper articles.

In the context of machine learning, influential people detection has been
mostly done in the field of social networks, marketing and diffusion research.
Kempe et. al [36] present work on choosing the most influential set of nodes
in a social network in order to maximize user influence in the network. Lerman
et. al [37] define popularity of a news story in terms of number of reader
votes received by it. Popularity over time is based on voting history and the
probability that a user in a list will vote. To identify influential bloggers,
Agarwal et. al[38] quantify influence of each blogger by taking the maximum of
the influence scores of each blog posted by the blogger. The influence score
is calculated using the number of posts that refer to the blog, number of
comments on the blog, number of other posts that the blog refers to and length
of the blog. Influential blogger categories are also created based on the
temporal patterns of blog posting. Cha et. al[39] describe another set of
measures for detection of top influential users on Twitter using number of
retweets, mentions and followers for an individual. They perform ranking based
on each measure separately and use Spearman’s rank correlation coefficient to
find correlation among ranks and effect of each measure contributing to a
person’s influence. The influence ranks of topmost influential users on
Twitter are presented across various topics as well as time. In all of the
above, the goal is to measure influence or popularity – however, these cannot
be directly adapted to the gazetteer or newspaper articles.

##  3 Machine Learning Framework for Prosopographical Research

Figure 1: Research Framework showing components of proposed solution

Figure 1 presents the framework for machine learning to aid prosopographical
research. It has the following components: (1) Data Gathering:
Prosopographical studies involve research on biographies of a group of people
and is therefore severely limited by the quantity and quality of data
accumulated about the past. Often in historical groups, a lot of information
is available about some people, and almost nothing about some others. Studies
are severely affected by lack of information and hence secondary sources of
information are resorted to including demographic sources (such as parish
registers), economic sources (such as deeds of sales), fiscal sources (such as
tax lists), financial sources (such as city accounts), administrative sources
(such as company records), religious sources (such as membership lists of
fraternities), judicial sources (such as sentences), family archives and
photographs, publicly available information (such as newspaper archives). The
context of the research is sketched based on the available literature and has
to be sufficient, relevant and easily accessible. For this research, the
primary source are digitized newspaper archives. In order to make a newspaper
available for searching on the Internet, the following processes [23] must
take place: (1) the microfilm copy of original paper is scanned; (2) master
and Web image files are generated; (3) metadata is assigned for each page to
improve the search capability of the newspaper; (4) OCR software is run over
high resolution images to create searchable full text and (5) OCR text,
images, and metadata are imported into a digital library software program. (2)
Data Pre-processing:  The images obtained from the OCR software are manually
segmented to obtain article level data. Following this, several preprocessing
steps are applied on the text of the news articles including spelling
correction and evaluation using a novel algorithm presented in [40]. (3)
Development of the People Gazetteer: This component describes the process of
development of the people gazetteer which involves Named Entity Recognition
(NER) in order to find person entities. This is followed by topic detection
using Latent Dirichlet Allocation (LDA) to find the primary topic(s) of news
articles and both are linked to obtain an organized structure. (4) Detection
of Influential People:  This component defines an Influential Person Index
(IPI) that incorporates several criteria for identifying and ranking of
influential people. Details about IPI, ranking and final results with some
case studies are discussed in Section 7.

##  4 Dataset Description

Our prosopographical research is based on historical newspapers obtained from
Chronicling America777http://chroniclingamerica.loc.gov/. Under this program,
institutions such as libraries receive an award to select and digitize
approximately 100,000 newspaper pages representing that state’s regional
history, geographic coverage, and events of the particular time period being
covered. The scanned newspaper holdings of the New York Public Library are a
source of prosopographical studies.

Figure 2: Scanned Image of a Newspaper article (left) and its OCR raw text
(right)

###  4.1 Characteristics

The newspapers are scanned on a page-by-page basis and article level
segmentation is poor or non-existent; the OCR scanning process is far from
perfect and the documents generated from it contain a large amount of garbled
text. An individual OCR text article has at least one or more of the following
types of spelling errors: 1) Real word errors include words that are spelled
correctly in the OCR text but still incorrect when compared to the original
newspaper article image. 2) Non-real word errors include words that have been
misspelled due to some insertion, deletion, substitution or transposition of
characters from a word. 3) Non-word errors include words that have been
spelled incorrectly and are a combination of alphabets and numerical
characters. 4) New Line errors include words that are separated by hyphens
where part of a word is written on one text line and remaining part in the
next line. 5) Word Split and Join errors include words that either get split
into one or more parts or some words in a sentence get joined to a make a
single word. For example, in Figure 2 the word “coil” has been correctly
spelled in the OCR text but should have been “and” according to the original
newspaper article (real word error); the word “tnenty” in the OCR text has a
substitution error (‘n’ should have been ‘w’) which is actually “twenty”
according to the original newspaper article (Non-real word error); the word
“4anrliteii” is a combination of alphabets and number and should have been
“confident” as per the original newspaper article(Non-word error); the word
“ex-ceptionally” where “ex” occurs on one line while “ceptionally” in the next
and due to no punctuation in the text, they are treated as separate words in
OCR text(New Line error); the word “Thernndldntesnra” in the OCR text is
actually a combination of three words “The candidates are” while the words “v
Icrory” are actually equivalent to a single word “victory” when compared with
the original news article(Word Split and Join error).

###  4.2 Statistics

Article level segmentation of text is available for only two months – since
this requires human intervention. Articles of “The Sun” newspaper from
November-December 1894 consisting of 14020 news articles are used in our
study. A total of 8,403,844 tokens are generated from a bag-of-words
extraction. The text from the articles do not have any punctuation and contain
a large amount of garbled text containing above mentioned OCR errors.

###  4.3 Preprocessing

The garbled OCR text makes data preprocessing mandatory before application of
any text mining algorithms. We,therefore, use edit distance algorithm based on
Levenshtein distance to perform spelling correction on the OCR text articles.
The algorithm is chosen because of its speed and ability to correct OCR errors
compared to the n-gram approach [41]. Our edit distance algorithm also uses an
enhanced person names dictionary for look up to give significance to personal
names spelling correction in the dataset. The results of spelling correction
and data preprocessing are presented in [40].

##  5 People Gazetteer

People Gazetteer as defined in Section 1 consists of tuples of person names
along with list of documents in which they occur. The primary goal of
developing the gazetteer is to have an organized list of person names from
which influential people can be identified. This section describes the two-
step process involved in the construction of the People Gazetteer: a)
Extraction of person names from the news articles using Named Entity
Recognition (described further in Section 5.1) and b) Assignment of topics to
news articles using a Latent Dirichlet Allocation (LDA)-based topic detector
(described further in Section 5.2).

###  5.1 Person Named Entity Recognition (PNER)

Named Entity Recognition (NER) refers to classification of elements in text
into pre-defined categories such as the names of persons, organizations,
locations, expressions of times, quantities, monetary values, and percentages.
Person Named Entity Recognition (PNER) is Named Entity Recognition that marks
up only person names occurring in the text. It involves _chunking_ or
segmentation of the text for name detection followed by _classification_ of
the name by entity type ( for e.g. person, organization, and location).

In this work, the Stanford CRF-NER888http://nlp.stanford.edu/software/CRF-
NER.shtml is used for Person Named Entity Recognition. It identifies three
classes – Person, Organization and Location and is based on linear chain
Conditional Random Field (CRF)([42, 43, 44]) sequence models. The following
modifications are made on the output of the Stanford CRF-NER – if the software
recognizes single term person entities, we ignore those and consider only
multi-term person entities and if a person entity’s name is repeated multiple
times, then we consider it only once. For example, if the person names “John”,
“John Smith”, “Smith” are recognized during PNER process, then we only
consider “John Smith” as as a potential person entity for our People
Gazetteer. A total of 36364 person entities are extracted from our corpus of
14020 news articles. The person name entities are binned into the following
categories based on the number of news articles of their occurrence: (1)Not
Influential: Person entities with occurrence in less than 4 news articles.
(36004 person entities ) (2)Popular: Person entities with occurrence from 4 to
15 news articles. (344 person entities) (3)Elite : Person entities with
occurrence in 16 or more news articles. (16 person entities) The categories
defined above have been chosen manually and simply provide a mechanism for
grouping people. It does not lead directly to the conclusion that a person
entity with large number of articles is influential. Also, slight
perturbations to these values (4 and 15) does not change the final results of
ranked influential people.

###  5.2 Topic Detection

Topic detection involves the process of identifying topics from a document
collection using a topic model[45]. The following algorithm is run over all
the news articles in our repository.

Latent Dirichlet Allocation (LDA) LDA is a generative probabilistic model in
which each document is modeled as a finite mixture over an underlying set of
topics and each topic, in turn, is modeled as an infinite mixture over an
underlying set of topic probabilities[46]. Given an input corpus of $D$
documents with $K$ topics, each topic being a multinomial distribution over a
vocabulary of $W$ words, the documents are modeled by fitting parameters
${\Phi}$ and ${\Theta}$. ${\Phi}$ is a matrix of size $D\times K$ in which
each row is a multinomial distribution of document $d$ indicating the relative
importance of words in topics. ${\Theta}$ is the matrix of size $W\times K$
with each column a multinomial distribution of topic $j$ and corresponds to
the relative importance of topics in documents. Given the observed words x =
${x_{i}}_{j}$, inference in LDA is done by computing the posterior
distribution over the latent topic assignments z = ${z_{i}}_{j}$, the mixing
proportions ${\Theta_{j}}$ and the topics ${\Phi_{k}}$. The inferencing is
either done using variational bayesian methods or Gibbs sampling which
involves integration and sampling of latent variables. However, LDA is a
compute intensive algorithm and it can take several days to run over a large
corpora.

Distributed LDA Model:  In practice, to scale LDA a parallel algorithm is
used. The data is partitioned among processors and inference is done in
parallel. The Approximate Distributed LDA (AD–LDA) model ([47]) assumes the
dataset $D$ is distributed equally among $P$ processors. A random assignment
of topics is made to each processor so that it has its own copy of words
$x_{p}$, topics $z_{p}$, word topic counts ${{{N_{w}}_{k}}_{p}}$ and topic
counts ${{{N_{k}}_{j}}_{p}}$. Gibbs sampling is used for inferencing local
topic models on each processor for a given number of iterations and topic
probabilities $z_{p}$, word topic ${{N_{w}}_{k}}_{p}$ and topic counts
${{N_{k}}_{j}}_{p}$ are reassigned. Global update is performed after each pass
by using a reduce-scatter operation on word topic count ${{N_{w}}_{k}}_{p}$ to
get a single set of counts and obtain final topic assignments.

How good are these topic models? Topic models can be evaluated using
_perplexity_ ([47, 46]) which expresses how surprised a trained model is, when
given unseen test data. Formally, perplexity can be calculated using the
following formula:

$Perplexity=\exp(-\dfrac{\text{Log Likelihood of unseen test
data}}{\text{Number of tokens in the test set}})$

Perplexity is a decreasing function of the log likelihood of the unseen
documents and lower the perplexity, better is the topic model.

###  5.3 People Gazetteer Output

Figure 3: Procedure for development of People Gazetteer

The procedure of development of the people gazetteer can be seen in Figure 3.
The list of articles obtained for each person entity after application of PNER
(Person-Article List) and the highest scoring topic assigned to it during
topic detection (Article-Topic List) are combined to obtain an entry in the
People Gazetteer. A snapshot is illustrated in Table I.

PERSON ENTITY NAME |  DOCUMENT LIST (Document ID $\rightarrow$ Document Topic ID)
---|---
Thomas Murphy |  (61720.txt$\rightarrow$16, 62002.txt$\rightarrow$11, 65905.txt$\rightarrow$19, 71341.txt$\rightarrow$28, 68024.txt$\rightarrow$16)
George Eliot |  (74151.txt$\rightarrow$5, 61627$\rightarrow$15)
Charles L Thompson |  (68836.txt$\rightarrow$9)
Thomas Jefferson |  (67874.txt$\rightarrow$19, 67209.txt$\rightarrow$28, 63996.txt$\rightarrow$6, 73835.txt$\rightarrow$6, 71155.txt$\rightarrow$6, 65440.txt$\rightarrow$5, 66997.txt$\rightarrow$20)
Jacob Schaefer |  (70205.txt$\rightarrow$21, 63936.txt$\rightarrow$22, 68554.txt$\rightarrow$21, 73420.txt$\rightarrow$21, 74550.txt$\rightarrow$21, 74922.txt$\rightarrow$21, 64577.txt$\rightarrow$21, 74759.txt$\rightarrow$21, 67340.txt$\rightarrow$0, 67924.txt$\rightarrow$2)
Queen Victoria |  (68231.txt$\rightarrow$5, 74775.txt$\rightarrow$5, 75097.txt$\rightarrow$5, 72221.txt$\rightarrow$2, 62731.txt$\rightarrow$5, 62616.txt$\rightarrow$17, 68368.txt$\rightarrow$17)
Thomas Gallagher |  (64397.txt$\rightarrow$28, 65793.txt$\rightarrow$21, 72591.txt$\rightarrow$0, 73420.txt$\rightarrow$21)
Samuel S Seely |  (70365.txt$\rightarrow$2, 64670.txt$\rightarrow$23, 65615.txt$\rightarrow$23, 67198.txt$\rightarrow$19, 73545.txt$\rightarrow$23, 74816.txt$\rightarrow$16)
Matthew Parker |  (64363.txt$\rightarrow$11)
Daniel Frohman |  (63704.txt$\rightarrow$5, 66992.txt$\rightarrow$25, 69668.txt$\rightarrow$4, 68743.txt$\rightarrow$5, 67554.txt$\rightarrow$25, 67450.txt$\rightarrow$5, 72274.txt$\rightarrow$24, 69444.txt$\rightarrow$4)
TABLE I: Snapshot of People Gazetteer with Person names, Document list of
occurrence and their corresponding Topic ID. The table shows Person entity
recognized by PNER on the left along with a list of text articles in which
s/he occurs on the right with the topic labels obtained for each of the text
article during Topic Detection. The text articles in our corpus are indexed
from 61102.txt to 74150.txt while the 30 topics are indexed from 0 to 29. The
list of these topics can be viewed in Appendix Table VII.

##  6 Influential People Detection

To find _influential_ people from the text in news articles, we define a score
corresponding to each person entity in the gazetteer. This score is called the
_Influential Person Index (IPI)_. To calculate IPI, we first define a
_Document Index (DI)_ to measure how each document in the person’s associated
list of documents affects his influence score. The choice of features for
detecting whether a person is influential is motivated by following questions:
(a) Are people mentioned frequently in the newspaper _influential_? (b) How to
measure frequency of occurrence(s) of a person - article by article or across
the complete dataset? (c) Do longer documents talk more about important
persons? (d) Is a person discussed in varied contexts (and over multiple
topics in news) more influential than one who is consistently talked about in
articles belonging to a single topic? The following discussion describes the
features chosen for calculation of DI and IPI of a person, followed by the
complete algorithm for detection of influential persons.

Document Index (DI):  The Document Index (DI) of an article in the people
gazetteer helps to measure a person’s influence score. This is calculated for
all the articles that a person entity is associated with in the People
Gazetteer as follows:

Normalized Document Length (NDL):  Document Length is defined as the number of
tokens contained in a news article. It is further normalized by dividing with
the maximum length of any news article across the corpus. Thus,
$NDL=\frac{\text{Document Length}}{\text{Maximum Document Length in the
dataset}}$

Normalized Person Name Frequency (NPNF):  Person Name Frequency (PNF) accounts
for the number of occurrences of a person’s name in the news article. A high
value of PNF makes the document more important. It is normalized as follows:

$NPNF=1+\log$(PNF)

The important questions that arise when dealing with Person Name Frequency
are: (a) Coreference resolution of person names: (for e.g., names “William
Schmittberger”,“Captain Williams” in a text article are same but recognized as
separate persons) and (b) Named Entity Disambiguation: This refers to the
occurrence of different persons with similar name in news articles. For e.g.,
the person “John Smith” detected in two different articles may or may not
refer to the same person. These issues are not dealt with by the PNER and need
to be addressed separately. The following section explains the approach to
coreference resolution.

Coreference Resolution:  The coreference resolution aims to find all
expressions that refer to the same person in the text. The algorithm used in
this work uses a multi-pass sieve for coreference resolution[48] and consists
of three steps: (a) Mention Detection: The goal is to detect nouns, pronouns
and occurrences of named entities from the text (b) Coreference Resolution:
This is performed by using a combination of ten independent sieves applied
from highest to lowest precision with global information sharing so that each
sieve builds on the previously clustered mentions followed by post processing
which removes singleton mentions. An implementation of this is available from
the Deterministic Coreference Resolution System of the CoreNLP
toolkit999http://nlp.stanford.edu/software/dcoref.shtml. During resolution,
all mentions that refer to the same entity are clustered together to form a
coreference chain (c) Identification of the most representative mention: The
most representative mention from each coreference chain is found and checked
to ensure it is a valid person entity in the People Gazetteer. If it is found,
then the count of mentions in its coreference chain is considered in the
person name frequency determination.

Table II illustrates an example of coreference resolution when applied to an
article text from our corpus and its effect on PNF for a person entity from
the People Gazetteer. It is also observed that due to lack of punctuation in
the dataset, several meaningless coreference mentions are also detected which
are not person named entities. However, since the persons entities list is
available from the People Gazetteer, frequencies of only those person names
are replaced through Coreference Resolution.

Text: Mr Eugene Kelly bead of the banking house of Eugene Kelly A Co Is dying
at his home 33 West Fiftyfirst street He became ill on Dee 4 and was forced to
lake to his bed
---
Mentions Extracted: [“Mr Eugene Kelly bead of the banking house of Eugene
Kelly A Co”], [“Eugene Kelly”],[“the banking house of Eugene Kelly A Co”],
[“Eugene Kelly A Co”], [“Eugene Kelly”],[“his home 33 West Fiftyfirst street
He became ill on Dee 4 and was forced to lake to his bed”], [“his”], [“33”],
[“He”], [“Dee 4”], [“4”], [“his bed”], [“his”]
Coreferred Entity Chains with most representative entity in bold: [“Mr Eugene
Kelly bead of the banking house of Eugene Kelly A Co”], [“Eugene Kelly”,
“Eugene Kelly”, “his”, “He”, “his”], [“the banking house of Eugene Kelly A
Co”], [“Eugene Kelly A Co”], [“his home 33 West Fiftyfirst street He became
ill on Dee 4 and was forced to lake to his bed”], [“33”], [“Dee 4”], [“4”],
[“his bed”]
Count of coreference mentions for most representative entity which is also a
Person Named Entity: Eugene Kelly: 5
TABLE II: Table illustrating change in PNF for person named entities on using
coreference resolution on an article text. The text has a person named entity
“Eugene Kelly” with name frequency=2. During coreference resolution, all
mentions are extracted from the text first and coreference chains are
obtained. Most representative entity from each coreference chain is then
identified (which may or may not be a person named entity) and if it matches
any person named entity from the People Gazetteer, then the PNF for this
entity is replaced by the count of mentions found in its coreference chain.
Here, out of 9 coreference chains, “Eugene Kelly” is the most representative
mention in one of the chains with 5 coreferences and since “Eugene Kelly” is
also a person entity as verified from the People Gazetteer, we replace its PNF
from 2 to 5.

Number of similar articles (NSIM):  This parameter is used in the calculation
of the DI by finding articles belonging to the same topic. For a document $d$
whose DI is to be calculated, let SIM= Number of articles with the same topic
as $d$ in the document list of a person. This measure is normalized by the
total number of articles in the document list of the person. Formally,

$NSIM=\frac{\text{SIM}}{\text{Total number of articles in the person's
document list}}$ NSIM is equivalent to the proportion of topic similar
articles that document $d$ has.

The Document Index is a function of the above mentioned parameters and is
calculated by using the following formula:

$DI=w_{a}.NDL+w_{b}.NSIM+w_{c}.NPNF$

where, $w_{a}$, $w_{b}$ and $w_{c}$ are the weights for NDL, NSIM and NPNF
respectively. DI is a heuristic measure – each of the parameters can be
weighted as per dataset characteristics and user requirements. For example, a
higher value to $w_{a}$ and lower to $w_{b}$ and $w_{c}$ indicates documents
with longer lengths are considered more important for influencing a person’s
IPI. On the other hand, a higher value to $w_{b}$ and lower to $w_{a}$ and
$w_{c}$ indicates a document with larger proportion of topic similar articles
influences the person’s IPI more suggesting assignment of high influence score
to a person entity occurring repeatedly in a specific news topic.

###  6.1 Influential Person Index (IPI)

Once DI is calculated, the IPI is estimated as follows:

$IPI=\text{max }DI(d_{1},d_{2},...,d_{n})+UniqT$

where, max DI $(d_{1},d_{2}...d_{n})$ is the maximum DI found from a person’s
list of n articles, $UniqT=\frac{\text{Number of Unique Article Topics in a
person entity's document list}}{\text{Total Number of Topics in the corpus}}$.
The parameter $UniqT$ is used to account for the fact that a single person
entity can be talked about multiple news topics in the news articles and to
include its effect on the person entity’s influence score. To rank people, the
IPI are sorted in decreasing order.

###  6.2 Algorithm for Detection of Influential People (ADIP)

Algorithm 1 depicts the steps for measuring influence and ranking of
influential people from the gazetteer. It starts with calculation of the
Document Index for each news article in a person’s document list. The weights
$w_{a}$,$w_{b}$,$w_{c}$ are taken as inputs and multiplied with parameters
NDL, NPNF and NSIM to get the final estimate for DI. The list of DI scores is
then sorted to find the maximum value amongst all news articles in the
person’s document list. The maximum DI score is then added to the UniqT
parameter to get the final IPI for each person entity. Sorting the IPIs
results in a ranked list of influential person entities.

function CalculateIPI

Input: $PeopleGazetter(PersonName,(DocList,TopicList))$,
$w_{a}$,$w_{b}$,$w_{c}$

Result: Ranked List of Person Names

$NPNF\leftarrow$0 $NDL\leftarrow$0 $NSIM\leftarrow$0 $DI\leftarrow$0
$UniqT\leftarrow$0 $IPI\leftarrow$0;

for  _(String PersonName : Persons)_ do

for  _(String doc : DocList)_ do

$NPNF=1+\log(GetPNF(doc))$;

$NDL=GetDocLength(doc)/GetMaxDocLength()$;

$NSIM=GetTopicSimilarArticles(doc,DocList)$;

$DI=w_{a}.NDL+w_{b}.NSIM+w_{c}.NPNF$;

$DIScoreList.add(DI)$;

end for

$Sort(DIScoreList)$;

$UniqT=GetUniqueTopics(Person,TopicList)$;

$IPI=Max(DIScoreList)+UniqT$;

$IPIScores.put(PersonName,IPI)$;

end for

$Sort(IPIScores)$;

$PrintPersonNameandIPI(IPIScores)$;

end function

Algorithm 1 Algorithm for Detection of Influential People (ADIP) Function Name | Description
---|---
GetPNF(doc) | Calculates name frequency of the person entity
| in document $doc$
GetDocLength(doc) | Calculates number of tokens in $doc$.
GetMaxDocLength() | Calculates maximum number of
| tokens in any document.
GetTopicSimilarArticles(doc,DocList) | Calculates normalized number
| of topic similar articles for $doc$ in the $DocList$.
Sort(DIScoreList) | Sorts the $DIScoreList$
Max(DIScoreList) | Finds the maximum score from $DIScoreList$.
GetUniqueTopics(Person,TopicList) | Calculates normalized unique
| topics for $Person$ in its $TopicList$.
Sort(IPIScores) | Sorts the $IPIScores$ by IPI values.
PrintPersonNameandIPI(IPIScores) | Prints $Person$ name with its
| IPI in decreasing order of IPI value.
TABLE III: Description of the functions used in Algorithm 1 Figure 4:
Comparison of the Average IPI for two ranked lists $L_{1}$ and $L_{2}$ using
$30$ and $100$ topics respectively.

##  7 Results

Category | No. of | Avg. No. of | Avg. Doc. | Avg. Person
---|---|---|---|---
| People | Documents | Length | Name
|  |  |  | Frequency
Not Influ. | 36004 | 1.04 | 2119.6 | 1.07
Popular | 344 | 5.75 | 1976.3 | 6.68
Elite | 16 | 22.8 | 2971.5 | 29.870

TABLE IV: Table illustrating average statistics for each Person Category of
the People Gazetteer.

We present statistics pertaining to each category (not influential, popular
and elite) of the people gazetteer in Table IV. It is observed that, on
average the _elite_ are discussed very often in the news articles. Document
Length need not be high for a person to be more influential – average document
length obtained for the popular group is high in spite of their average IPI
being low. This indicates that the number of similar articles for each
document as well as the person name frequency play an important part in
measuring influence.
Effect of varying the number of topics in the topic model:  Our objective is
to study the sensitivity of the ranked lists to parameters of the ADIP
algorithm. We study the effect of the number of topics used in the LDA Model
on the influential people list. Two ranked lists of influential people,
$L_{1}$ and $L_{2}$ are compared by using 30 and 100 topics101010Several topic
models are built by varying parameters of AD-LDA algorithm including number of
iterations, topics and processors and their _perplexity_ is measured. Models
with less perplexity are used for the study. respectively. The weights
$w_{a}$, $w_{b}$ and $w_{c}$ are set to 1 to ensure that all parameters have
equal importance during calculation of DI and IPI. Figure 4 shows the average
IPI from the two ranked lists – it appears that the average IPI for highly
influential people is more susceptible to changes in number of topics.

Person Name | IPI |  Number of Articles |  Person Category | NDL | NPNF | NSIM |  TOPIC WORDS | UniqT | Rank
---|---|---|---|---|---|---|---|---|---
capt creeten | 3.32 |  10 |  Popular | 0.55 | 1.9 | 0.8 |  mr court police judge justice case yesterday street district | 0.06 | 1
capt hankey | 3.05 |  5 |  Popular | 0.68 | 1.69 | 0.6 |  club game team play football half ball left college back | 0.06 | 2
capt pinckney | 2.93 |  3 |  Not Inf. | 0.38 | 1.84 | 0.67 |  man ho men night back wa room left house told bad | 0.03 | 3
john martin | 2.89 |  14 |  Popular | 0.55 | 1.6 | 0.57 |  mr court police judge justice case yesterday street district witness | 0.16 | 4
ann arbor | 2.87 |  44 |  Elite | 0.19 | 1.77 | 0.63 |  dr book st story books cloth author cure free work york blood illustrated remedy goods medical library health price | 0.26 | 5
john macdonald | 2.85 |  3 |  Not Inf. | 0.55 | 2.2 | 0 |  great people life man women good country world american part | 0.1 | 6
aaron trow | 2.81 |  1 |  Not Inf. | 0.7 | 2.07 | 0 |  man ho men night back wa room left house told | 0.03 | 6
mrs oakes | 2.79 |  5 |  Popular | 0.08 | 2.04 | 0.6 |  street mrs mr avenue wife house miss yesterday years home | 0.06 | 7
alexander iii | 2.71 |  31 |  Elite | 0.24 | 2.04 | 0.25 |  great people life man women good country world american part | 0.16 | 9
buenos ayres | 2.7 |  6 |  Popular | 0.49 | 1.47 | 0.67 |  white water indian black long found thu big dog time | 0.06 | 10

TABLE V: Table showing top 10 influential persons of List L1 detected from
People Gazetteer with 30 Topics LDA model. Parameters NDL, NPNF, NSIM and
Topic Words belong to the maximum scoring DI in the person’s document list.

Person Name | IPI |  Number of Articles |  Person Category | NDL | NPNF | NSIM |  Topic Words | UniqT | Rank
---|---|---|---|---|---|---|---|---|---
capt creeten | 3.28 |  10 |  Popular | 0.55 | 1.90 | 0.8 |  mr police witness committee capt asked captain money inspector paid | 0.06 | 1
mrs martin | 3.21 |  8 |  Popular | 0.20 | 2.36 | 0.62 |  mrs mr years wife home house ago woman city died | 0.02 | 2
capt hankey | 2.97 |  6 |  Popular | 0.68 | 1.69 | 0.8 |  game team football play half line ball back yale eleven | 0.02 | 3
alexander iii | 3.05 |  31 |  Elite | 0.49 | 2.04 | 0.45 |  emperor prince french alexander czar london nov government imperial russian | 0.07 | 4
aaron trow | 2.79 |  1 |  Not Inf. | 0.70 | 2.07 | 0 |  day place long great water time feet found good men | 0.01 | 5
john martin | 2.78 |  14 |  Popular | 0.55 | 1.6 | 0.57 |  mr police witness committee capt asked captain money inspector paid | 0.05 | 6
john macdonald | 2.77 |  3 |  Not Inf. | 0.55 | 2.2 | 0 |  people american man great country men world life good english | 0.02 | 7
mrs oakes | 2.74 |  5 |  Popular | 0.08 | 2.04 | 0.6 |  mrs mr years wife home house ago woman city died | 0.02 | 7
ed kearney | 2.63 |  7 |  Popular | 0.16 | 1.6 | 0.85 |  won time race ran mile furlough half lo track fourth | 0.01 | 9
caleb morton | 2.61 |  1 |  Not Inf. | 0.70 | 1.9 | 0 |  day place long great water time feet found good men | 0.01 | 10

TABLE VI: Table showing top 10 influential persons of List L2 detected from
People Gazetteer with 100 Topics LDA model. Parameters NDL, NPNF, NSIM and
Topic Words belong to the maximum scoring DI in the person’s document list.

The top 10 influential people from lists $L_{1}$ and $L_{2}$ are presented in
Tables V and VI respectively. Our results suggest that none of the measures of
NDL, NPNF or NSIM can be used alone to say whether a person is influential
since these value do not decrease or increase consistently although the NPNF
measure does contribute most to the IPI of any person.

The ranked lists $L_{1}$ and $L_{2}$ can be compared in terms of NSIM, UniqT
and topic words to see the effect of 30 and 100 topics LDA models on
influential person detection. If NSIM remains same in $L_{1}$ and $L_{2}$
during influential person detection, then the same highest scoring article DI
is selected for calculation of IPI in both of them. This is why the parameters
NDL and NPNF remain same across both the lists. This can be seen for “capt
creeten”, “capt hankey”, “aaron trow” and “mrs oakes” in Tables V and VI.
However, the value of UniqT for these persons decreases leading to decrease in
their final IPI. This is because if the LDA model with higher number of topics
(100) is used, the proportion of unique topics becomes lower when NSIM does
not change. When the NSIM value changes because of change in number of topics,
a different article with maximum DI score can get selected leading to change
in the values of NDL, NPNF, UniqT and the final IPI. This causes a shift in
the ranking of influential persons across the two lists and can be seen when
the rank of “alexander iii” in the first table moves from 9 to 4 in the second
table. This helps to illustrate how a change in number of topics affects the
ranking of influential people.

Wilcoxon signed rank paired test is also performed on the ranks of influential
people across the two lists $L_{1}$ and $L_{2}$. This is done to test the
hypothesis whether the differences in the ranking of person entities obtained
using the 30 topic and 100 topic LDA models are due to chance or not. The null
hypothesis for the test is: $H_{0}$: the distribution of difference of ranks
of the persons across $L_{1}$ and $L_{2}$ is symmetric about zero. On
performing the normal distribution approximation for 36364 samples of person
ranks from lists $L_{1}$ and $L_{2}$, the results are found to be significant
for both one-tail and two-tail tests.

Case Studies:  To evaluate whether the ranking algorithm indeed finds people
of _influence_ , manual evaluation of results is necessary. A description of
the influential people from lists $L_{1}$ and $L_{2}$ (Table V and VI) are
discussed below: (1) Elite \- This category as defined earlier includes people
with greater than 16 news articles. However, only one person (“alexander iii”)
from this category occurs in the top 10 influential persons. The entry for
“alexander iii” has an IPI of 2.71 and 3.05 respectively in lists $L_{1}$ and
$L_{2}$ . The person occurs in 31 news articles with 5 and 7 different topics
in each of the lists. The most common topic words associated with this person
entity are: “emperor prince french alexander czar london nov government
imperial russian” indicating the importance of this entity in government
related news topics. It is also observed that “ann arbor” occurring in 44
articles is ranked 5 in list $L_{1}$ is a false positive as it is actually a
location and has been wrongly ecognized in the PNER process as a person
entity. (2)Popular \- The top 10 influential entities from Tables V and VI
contain the most number of people from this person category. The person entity
“capt creeten” has been ranked as highest influential (Rank 1) across both the
tables. It occurs in 10 news articles with 9 of them belonging to the same
topic indicating the person influencing news articles of high topic
similarity. Some of the most common topic words for this entity include “mr
police witness committee capt asked captain money inspector paid” indicating
the importance of this entity in a judicial or police related news topic.
Several persons from this category like “mrs martin” , “mrs oakes” although
identified among the top 10 influential persons suffer from the problem of
named entity disambiguation as it is hard to identify which exact person they
refer to due to lack of first names. It is also observed that “buenos ayres”
occurring in 6 articles is ranked 10 in list $L_{1}$ is a false positive as it
is actually a location and has been wrongly recognized in the PNER process as
a person entity. (3)Not Influential \- Person entities belonging to this
category have extremely low occurrence in news articles although the IPI of
topmost influential entities belonging to this category are comparable to
those in the other 2 categories. Several person entities occurring in low
number of news articles like “aaron trow”, “caleb morton”, “john macdonald”
belong to this category. These entities in spite of occurring in very few
articles (1 to 3) have high term frequency in those articles with
comparatively longer article length indicating the importance of these
entities with respect to the articles they occur in. Since each of the
features has been given equal weight during the calculation of IPI, these
person entities with high NDL and NPNF have been identified among the top 10
influential persons.

###  7.1 Evaluation

Due to the unavailability of ground truth consisting of influential people in
the newspaper archives from November-December 1894, there is no way to
validate our results. To broadly evaluate our results, a simple web search
query with the person’s name in the context of 19th century was done on the
Wikipedia website for the top 30 influential persons of Lists $L_{1}$ and
$L_{2}$ detected from the people gazetteer with 30 topics LDA and 100 topics
LDA Model respectively.

Among the top 30, 17 people from List $L_{1}$ and 12 from List $L_{2}$ were
found to be influential and popular in the 19th century across topic
categories like theatre, politics, government, shipping, etc. Some of these
influential people have been found in Wikipedia and are shown in Figure 5.
Most of the false positives, although influential in other respects, were not
influential _person_ entities. This can be attributed to the incorrect PNER
(Person Named Entity Recognition) on noisy OCR data. The ranked list of the
top 30 influential persons with their IPI from Lists $L_{1}$ and $L_{2}$ can
be seen in the Appendix (Tables VIII and IX) where evaluation result for each
person entity is also presented.

Figure 5: Some of the top 30 influential persons obtained from the dataset and
also found on Wikipedia during evaluation

##  8 Discussion

The following issues encountered during algorithm design and empirical
verification are worth discussion: (1) A linear combination of parameters was
used in our experiments for calculation of Document Index and Influential
Person Index. Furthermore, they are weighted equally in experiments performed
with our data. The heuristics can be re-weighted according to user
requirements. (2) The parameters for calculation of DI and IPI can also be
learned by performing regression analysis using a manually developed sample of
influential people and obtaining the complete list of ranked influential
people based on the learned parameters. (3) The Normalized Document Length
(NDL) defined for calculation of DI is normalized using the maximum length of
any document in the dataset. However, there might exist other ways of
normalization of Document Length like using total number of tokens in a
person’s document list or total number of tokens in the complete dataset which
can be tuned based on the requirements of the application. (4) Lists of
influential people contain several false positives. This is due to noise in
the OCR data – several location and organization names have been recognized as
person entities even after performing spelling correction resulting in false
detection during PNER for some influential entities like “van cortlandt”, “ann
arbor” , and “sandy hook”. We recognize that there there is no gold standard
data to measure the accuracy in our case therefore, we rely on the NER
software’s accuracy for recognizing person names. (5) We have used the topic
with highest probability for topic detection in the People Gazetteer. However,
there could be other topics of interest. The algorithm can be modified to
assign top N (N can be chosen depending on the topic distribution
probabilities in the corpus) topics to a document by updating NSIM as follows:
$NSIM=\frac{\sum_{i=1}^{N}\text{SIM}_{i}}{N\times n}$ Here, $N$=set of topics
assigned to a document during topic detection, $SIM_{i}$ =Number of articles
with topic $i$ in the person’s document list and $n$= total number of
documents in which person entity occurs in the People Gazetteer. (6) The
choice of parameters for topic detection also affects the detection of
influential people which is evident from the fact that we get different
ranking of influential people for the two different LDA topic model settings
used.

An alternative approach to detection of influential people:  A heuristics
based approach for finding influential people has been discussed in the
previous section. It can be easily seen that the gazetteer comprises of a list
of person names and a _bag_ of articles for each person from which his/her
influence score can be learnt. This motivates discussion of whether an
alternative approach involving multiple instance clustering (such as the BAg
Level Multi-Instance Clustering (BAMIC [49]) can be used for the problem. The
procedure works as follows: Cluster person entities into _influential_ or
_non-influential_ categories by considering each person entity as having a bag
of news articles in which their names occur. The parameters used for
calculating DI in the previous section i.e. NDL,NTF and NSIM can be used as
features associated with each article instance in the bag. The group of
_influential_ people identified can then be ranked using an appropriate
ranking score. The open source version of the BAMIC algorithm was used to
compare results with the heuristic based approach proposed in this paper.
However, the clustering algorithm does not scale very well. Our dataset
consisted of roughly 40000 person named entities – it was estimated that it
would take around 200 days to get the clusters of influential and non-
influential people. Hence these results of comparison are not presented.

##  9 Conclusion

The problem of finding influential people from a historical OCR news
repository has been studied to aid quantitative prosopography research. The
solution framework comprises of development of a people gazetteer for
facilitating the process of influential person detection. A novel algorithm
for detecting influence has been presented which examines spelling correction
of noisy OCR, person name entity recognition, topic detection and heuristics
to measure influence and rank of people mentioned in the articles. Our
algorithm has been tested on approximately 40000 people discussed in historic
newspapers. The Tsar of Russia (Alexander III), the first and fourth Prime
Ministers of Canada (John McDonald and John Thompson), and English soldiers
serving in World War I (Captain Hankey) are among the influential people
identified by the algorithm.

## Acknowledgements

This work was initially supported by the National Endowment of Humanities
grant no. NEH HD-51153- 10. The authors would like to thank Barbara Taranto
and Ben Vershbow from the NYPL Labs and Manoj Pooleery, Deepak Sankargouda and
Megha Gupta for setting up the database used in this research.

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|  Aayushee Gupta is an Assistant Professor in the Department of Computer
Science and Information Technology at Jaypee Institute of Information
Technology, India. She completed her M.Tech in Computer Science with
specialization in Data Engineering from IIIT-Delhi. Her research interests
include data mining and machine learning with applications in databases and
information retrieval.
---|---
|  Haimonti Dutta is an Assistant Professor at the Department of Management
Science and Systems, University at Buffalo,USA. Prior to her current role, she
was an Associate Research Scientist at the Center for Computational Learning
Systems, Columbia University, NY where she headed the Scalable Analytics
Research Group. Her research focuses on mining big data, machine learning and
distributed optimization.
---|---
|  Srikanta Bedathur received the Ph.D. degree from the Indian Institute of
Science in Bangalore, India in 2005. He is currently a researcher at IBM
Research India, and before that he was an Assistant Professor at IIIT-Delhi,
where he was leading the Max-Planck Partner group on large-scale graph search
and mining. He also held positions at Max-Planck Institute for Informatics and
Saarland University in Germany. His current research primarily revolves around
problems of scalable graph management and mining arising in large-scale
knowledge repositories.
---|---
|  Lipika Dey is a Senior Consultant and Principal Scientist at Tata
Consultancy Services, India where she heads the Web Intelligence and Text
Mining research group. Lipika did her Integrated M.Sc. in Mathematics, M.Tech
in Computer Science and Data Processing and Ph.D. in Computer Science and
Engineering - all from IIT Kharagpur. Prior to joining TCS in 2007, she was a
faculty at the Department of Mathematics at IIT Delhi from 1995 to 2007.
Lipika’s research interests are in the areas of content analytics from social
media and other consumer-generated text, social network analytics, predictive
modeling, sentiment analysis and opinion mining, and semantic search of
enterprise content. Her focus is on seamless integration of social
intelligence and business intelligence using multi-structured data analytics.
---|---
TABLE VII: Table showing Topic ID and words obtained from the 30 Topics LDA
model.

TOPIC ID |  TOPIC WORDS
---|---
1 |  total ii won club score night ran furlough alleys tournament time mile fourth rolled curling scores race national game
2 |  la lu ot lo tu au tb ta ha tea day al aa ut ar uu wa tt te
3 |  iii lie tin nail tn lit hut ill ii nn thu tu anti thin inn hit lu lo nut
4 |  line street feet point western easterly northerly feel southerly distance place distant lo fret hue beginning laid early felt
5 |  opera theatre music company week play stage evening night performance concert mme audience manager season de orchestra house miss
6 |  great people life man women good country world american part ot ha made la years make long place bad
7 |  election mr party republican state district vote democratic county senator elected city committee mayor political candidate majority york democrats
8 |  time ho work tn men city bo lie anti day thin long thu made part ago lot york make
9 |  st room av sun wife board front lo december rent lot november sunday ht west ar house private si
10 |  dr book st story books cloth author cure free work york blood illustrated remedy goods medical library health price
11 |  church dr father funeral school st college sunday year rev catholic pastor services late service held society holy clock
12 |  horse race class horses won racing years prize record year show ring track mile money jockey trotting trotter ran
13 |  cent year week pf market total net stock today central st ft lit sales short cotton ohio lot month
14 |  white water indian black long found thu big dog time ground wild tree killed birds bird day great lake
15 |  price black silk goods prices ladies worth dress fine white full tea quality style wool made fancy cloth fur
16 |  street mrs mr avenue wife house miss yesterday years home woman night ago husband found died daughter children mother
17 |  war american government army chinese japanese china japan foreign united nov emperor states prince minister military french port navy
18 |  feet north minutes avenue boundary seconds degrees west york minute degree point east south feel city angle county laid
19 |  man ho men night back wa room left house told bad door found turned place ran lie front morning
20 |  water feet building boat company car train road fire miles railroad island work line city great river built bridge
21 |  club game team play football half ball left college back yale played harvard line eleven men match yacht field
22 |  ii iii ill lit ll si ti il im vi st iv ft mi li till lull lui oil
23 |  bank money national gold amount notes banks hank business treasury account cent paid bonds note currency company stock estate
24 |  mr john william york henry charles james club city ii george dec dr thomas smith jr brooklyn van held
25 |  piano st rooms car york daily chicago city sunday upright parlor furnished broadway hotel av west train brooklyn monthly
26 |  york daily steamship nov directed letter dec fur orleans al steamer walls letters close australia china japan city london
27 |  mr court police judge justice case yesterday street district witness jury charge asked attorney trial arrested lawyer told office
28 |  mr law present made public year state committee president secretary bill report states con tin united number meeting york
29 |  air ran ur fur ui full tt al tl late mr ant liar art lay told met ti tr
30 |  company york trust bonds city cent railroad mortgage interest wall bond stock street st central january coupon committee jan

TABLE VIII: Table representing top 30 influential person entities detected from people gazetteer with 30 Topics LDA Model along with evaluation results and comments. Person Name | IPI |  Whether found on Wikipedia |  Comments
---|---|---|---
capt creeten | 3.33 |  no |  spelled incorrectly;capt creedon
capt hankey | 3.05 |  yes |
capt pinckney | 2.93 |  yes |
john martin | 2.89 |  yes |
ann arbor | 2.87 |  no |  location name
john macdonald | 2.85 |  yes |
aaron trow | 2.81 |  yes |  fictional character
mrs oakes | 2.79 |  no |  false positive
alexander iii | 2.71 |  yes |
buenos ayres | 2.70 |  no |  location name
mrs martin | 2.70 |  no |  false positive
caleb morton | 2.63 |  no |  fictional character
anthony comstock | 2.63 |  yes |
john thompson | 2.61 |  yes |
nat lead | 2.61 |  no |  false positive
van cortlandt | 2.54 |  no |  location
ed kearney | 2.54 |  yes |  name of horse
louis philippe | 2.52 |  yes |
mrs talboys | 2.52 |  yes |  fictional character
jim hooker | 2.50 |  yes |  false positive
marie clavero | 2.49 |  no |  false positive
charley grant | 2.45 |  no |
james mccutcheon | 2.43 |  no |  part of an organization name
hugh allan | 2.43 |  yes |
william i | 2.42 |  yes |
marie antoinette | 2.41 |  yes |
mr john | 2.39 |  no |  false positive
schmitt berger | 2.39 |  no |  spelled incorrectly;max f schmittberger
jacob schaefer | 2.39 |  yes |
phil king | 2.38 |  yes |
TABLE IX: Table representing top 30 influential person entities detected from people gazetteer with 100 Topics LDA Model along with evaluation results and comments. Person Name | IPI |  Whether found on Wikipedia |  Comments
---|---|---|---
capt creeten | 3.28 |  no |  spelled incorrectly; capt creedon
mrs martin | 3.21 |  no |  false positive
capt hankey | 3.19 |  yes |
alexander iii | 3.05 |  yes |
aaron trow | 2.79 |  yes |
john martin | 2.78 |  yes |
john macdonald | 2.77 |  no |
mrs oakes | 2.74 |  no |  false positive
ed kearney | 2.63 |  yes |  name of horse
caleb morton | 2.61 |  no |  fictional character
nat lead | 2.58 |  no |  false positive
john ward | 2.57 |  yes |
van cortlandt | 2.50 |  no |  location
mrs talboys | 2.49 |  yes |  fictional character
ann arbor | 2.49 |  no |  location
buenos ayres | 2.49 |  no |  location
john thompson | 2.48 |  yes |
louis philippe | 2.47 |  yes |
marie clavero | 2.47 |  no |  false positive
hardy fox | 2.45 |  no |
charley grant | 2.42 |  no |
mme melba | 2.41 |  yes |
charles weisman | 2.40 |  no |  false positive
hugh allan | 2.40 |  yes |
henry w dreyer | 2.39 |  no |
schmitt berger | 2.37 |  no |  spelled incorrectly
phil king | 2.36 |  yes |
henry a meyer | 2.35 |  no |
north orlich | 2.35 |  no |  false positive
james mccutcheon | 2.34 |  no |  part of organization name