An experimental analysis of the performance of these symbolic transducer and neural preference machines is presented . It is demonstrated that each approach can be successfully used for information mining and news classification using the Reuters news corpus . Symbolic transducer machines can be used to manually encode relevant knowledge quickly in a data-driven approach with no training , while trained neural preference machines can give better performance based on additional training . It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text . Furthermore , the design concept proposed is promising , which can be utilized in other real-world applications such as news classification and public opinion monitoring . Data were taken from as many as 1350 pieces www.kompas.com as training data and 150 as test data . Testing is done by classifying feature election news without Chi Square and classifying news by applying the Chi Square feature selection with significance level α 0:05 , 0:01 , 0.005 , and 0.001 . Next will be evaluated by the evaluation method of recall , precision , f-measure and accuracy . In the latter case there are no needs for natural language processing that depends on a language such as morphological analysis . Past studies showed that the character-level ConvNets worked well for news category classification and sentiment analysis / classification tasks in English and romanized Chinese text corpus . In this article we apply the character-level ConvNets to Japanese text understanding . We also attempt to reuse meaningful representations that are learned in the ConvNets from a large-scale dataset in the form of transfer learning , inspired by its success in the field of image recognition . As for the application to the news category classification and the sentiment analysis and classification tasks in Japanese text corpus , the ConvNets outperformed N-gram-based classifiers . In addition , our ConvNets transfer learning frameworks worked well for a task which is similar to one used for pre-training . Inputs to the system are news text picked up by software agents from selected Internet economic sites , and the outputs are those same news classified by topics of interest in at most six classes . The classification is based on an artificial neural network that runs inside the classification agent , especially trained with patterns that allow carrying out the desired news analysis and classifications . Epidemiological surveillance is an important issue of public health policy . In this paper , we describe a method based on knowledge extraction from news and news classification to understand the epidemic evolution . Descriptive studies are useful for gathering information on the incidence and characteristics of an epidemic . The developed text classification system is designed to automate news classification process . Text corpus for training and testing the system is formed from Azerbaijani news articles . The system will be useful for online news categorization and automatic news labeling for news agencies . Naive Bayes , Support Vector Machines , and Artificial Neural Networks have been implemented to solve the news classification problem . Moreover , a number of approaches such as stemming , stop word removal , feature reduction have been implemented for both performance and accuracy improvements . JSP News Release System , adopting JDK 1.5.0_01,as the development environment , MYSQL as the database server , RESIN-2.1.as the testing server , has finished all functions of a news release system , such as news classification setup , news release and deletion , frontpage buildup , and background management . Different classification algorithms are applied on News data set . A comparative study of these algorithms is done based on Accuracy , Time , Errors and ROC to predict the best algorithm for news data set classification . Findings : Present study analyzed result on the basis of accuracy , time , error and ROC curve . In order to solve the problem of data inconsistency in parallel computing , a method of dynamically acquiring cluster increments and random delays is adopted to improve the Single-Pass algorithm . In order to validate the performance of the proposed method , a case study of news events classification is carried out . Simulation results show that the proposed algorithm can effectively improve the cluster repetition in clustering results and greatly improve the accuracy and efficiency of clustering compared with the traditional Single-Pass algorithm . We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments . This paper also utilizes 2D convolution to sample more meaningful information of the matrix . Experiments are conducted on six text classification tasks , including sentiment analysis , question classification , subjectivity classification and newsgroup classification . Compared with the state - of - the - art models , the proposed models achieve excellent performance on 4 out of 6 tasks . Cross lingual entity linking means linking an entity mention in a background source document in one language with the corresponding real world entity in a knowledge base written in the other language . The key problem is to measure the similarity score between the context of the entity mention and the document of the candidate entity . These approaches often suffer from the uneven quality of translations between languages . While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment , a significant number of attributes remain largely unexplored . In this paper , we propose a joint attribute-preserving embedding model for cross-lingual entity alignment . To stimulate research in cross-language entity linking , we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages . This paper describes an efficient way to create and curate such a collection , judiciously exploiting existing language resources . In this paper , we propose a novel M ulti- M odal S emantic M atching approach , called MMSM , to enrich cross-lingual links for online Wikis . Specifically , MMSM jointly trains two novel end-to-end neural matching models , Entity Description Matching Model and Entity Image Matching Model , which can utilize entity description and images for the cross-lingual entity matching . To the best of our knowledge , it is the first work to utilize multi-modal information to enrich cross-lingual entity links . Our models can be trained on partially aligned graphs , where just a small portion of triples are aligned with their cross-lingual counterparts . The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results , with some variants consistently outperforming others on different tasks . We also explore how MTransE preserves the key properties of its monolingual counterpart TransE. This paper presents HITS ’ system for monolingual and cross-lingual entity linking at TAC 2012 . We propose a joint system for entity disambiguation , recognition of NILs and clustering using Markov Logic . This paper presents HITS ’ system for monolingual and cross-lingual entity linking at TAC 2013 . The system is an extended version of our last year ’s joint entity disambiguation and clustering system based on Markov Logic Networks . This paper describes the University of Sheffield ’s entry in the 2011 TAC KBP entity linking and slot filling tasks ( Ji et al. , 2011 ) . We chose to participate in the monolingual entity linking task , the monolingual slot filling task and the temporal slot filling tasks . Our team consisted of five MSc students , two PhD students and one more senior academic . One is based on cross-lingual information networks , aligned based on monolingual information extraction , and the other uses topic modeling to ensure global consistency . We enhance a strong baseline system derived from a combination of state-of-the-art machine translation and monolingual entity linking to achieve 11.2 % improvement in B-Cubed+ F-measure . Our system achieved highly competitive results in the NIST Text Analysis Conference ( TAC ) Knowledge Base Population ( KBP2011 ) evaluation . RSVM is used to build cross-lingual links between Chinese entities and English knowledge graphs . The experiments show that our approach can achieve a high precision of 82.3 % for the task of finding cross-lingual entities on a test dataset . Our experiments for the sub task of finding missing cross-lingual links show that our approach has a precision of 89.42 % with a recall of 80.47 % . Using these cross-lingual DPCs , we can solve problems in one , possibly resource-poor , language using a knowledge source from another , possibly resource-rich , language . I show that the approach is also useful in tasks that inherently involve two or more languages , such as machine translation and multilingual text summarization . The proposed approach is computationally inexpensive , it can estimate both semantic relatedness and semantic similarity , and it can be applied to all parts of speech . Cross-language document summarization is defined as the task of producing a summary in a target language ( e.g. Chinese ) for a set of documents in a source language ( e.g. English ) . Existing methods for addressing this task make use of either the information from the original documents in the source language or the information from the translated documents in the target language . The task of cross-language document summarization aims to produce a summary in a target language ( e.g. , Chinese ) for a given document set in a different source language ( e.g. , English ) . Previous studies focus on ranking and selection of translated sentences in the target language . The main target of cross-language summarization is to generate a summary in a different language from the language of the source document or documents . In this paper , it is proposed a Textual Energy approach to monodocument summarization plus the use of a Machine Translation online system to translate the source file from English into Spanish . Cross-language summarization is the novel topic which is extremely practical and necessary for capturing , tracing , and retrieving the huge data . Especially , for many low-resource languages as Vietnamese , Chinese , ... , there are not any previous works to solve this problem as well as datasets . Abstract — Cross-language summarization is the task ofgenerating a summary in a language different from the languageof the source documents . In this paper , we propose a graph-basedapproach to multi-document summarization that integratesmachine translation quality scores in the sentence extractionprocess . Aiming to solve the problems of insufficient semantic understanding and low statement accuracy in the automatic text summarization of natural language processing , in this paper we present a novel neural network method for extractive summarization by Quadratic Check . The sentence extractor extracts the primary sentences by a scoring and selecting module , among which it selects the final sentences based on impact factor computed by the transformer model and builds the output summary by the quadratic check . In this work , we use Hidden Markov Models ( HMM ) , Conditional Random Field ( CRF ) , Gaussian Mixture Models ( GMM ) and Mathematical Methods of Statistics ( MMS ) for Chinese and Japanese text summarization . The purpose of this work is to study the applicability of mentioned three trainable models for cross-language text summarization . For model training , we use several training features such as sentence position , sentence centrality , number of Name Entity and so on . Except for one case , our method gave better results than the method using only BMWEs grouping . Even though this paper does not directly address the issues in Cross-Lingual Information Retrieval ( CLIR ) , it discusses an approach of direct relevance to the field . This approach could be viewed as the opposite of current trends in CLIR on semantic space that incorporate a notion of order in the bag-of-words model ( e.g. co-occurences ) . Comparing Multiple Approaches to the Cross-Lingual 5W Task . Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation ( MT ) . This year , we have participated in the Ad-Hoc Robust Multilingual track with the aim of evaluating two important issues in Cross-Lingual Information Retrieval ( CLIR ) systems . This paper first describes the method applied for query expansion in a multilingual environment by using web search results provided by the Google engine in order to increase retrieval robustness . In this paper , we describe the methods we used for the Cross-Lingual Evaluation Forum CLEF 2002 , and more specifically for the GIRT Task . The methods are based on ( 1 ) the extraction of two bilingual lexicons , one from parallel corpora and the other one from comparable corpora , ( 2 ) the optimal combination of these bilingual lexicons in Cross-Language Information Retrieval and ( 3 ) the combination with monolingual IR on parallel corpora . This paper describes the NRC submission to the Spanish Cross-Lingual Word Sense Disambiguation task at SemEval-2013 . Since this word sense disambiguation task uses Spanish translations of English words as gold annotation , it can be cast as a machine translation problem . This article deals with the problem of Cross-Lingual Text Categorization ( CLTC ) , which arises when documents in different languages must be classified according to the same classification tree . We describe practical and cost-effective solutions for automatic Cross-Lingual Text Categorization , both in case a sufficient number of training examples is available for each new language and in the case that for some language no training examples are available . In this paper , we describe our submission for FIRE 2018 Shared Task on Named Entity Recognition in Indian Languages[3 ] . Identification of Named Entities is important in several higher language technology systems such as Information Extraction systems , Machine Translation systems , and Cross-Lingual Information Access systems . The system makes use of the different contextual information of the words along with the Word-Level and Character-Level features that are helpful in predicting the various Named Entity Classes .
An experimental analysis of the performance of these symbolic transducer and neural preference machines is presented . It is demonstrated that each approach can be successfully used for information mining and news classification using the Reuters news corpus . Symbolic transducer machines can be used to manually encode relevant knowledge quickly in a data-driven approach with no training , while trained neural preference machines can give better performance based on additional training .
It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text . Furthermore , the design concept proposed is promising , which can be utilized in other real-world applications such as news classification and public opinion monitoring .
Data were taken from as many as 1350 pieces www.kompas.com as training data and 150 as test data . Testing is done by classifying feature election news without Chi Square and classifying news by applying the Chi Square feature selection with significance level α 0:05 , 0:01 , 0.005 , and 0.001 . Next will be evaluated by the evaluation method of recall , precision , f-measure and accuracy .
In the latter case there are no needs for natural language processing that depends on a language such as morphological analysis . Past studies showed that the character-level ConvNets worked well for news category classification and sentiment analysis / classification tasks in English and romanized Chinese text corpus . In this article we apply the character-level ConvNets to Japanese text understanding . We also attempt to reuse meaningful representations that are learned in the ConvNets from a large-scale dataset in the form of transfer learning , inspired by its success in the field of image recognition . As for the application to the news category classification and the sentiment analysis and classification tasks in Japanese text corpus , the ConvNets outperformed N-gram-based classifiers . In addition , our ConvNets transfer learning frameworks worked well for a task which is similar to one used for pre-training .
Inputs to the system are news text picked up by software agents from selected Internet economic sites , and the outputs are those same news classified by topics of interest in at most six classes . The classification is based on an artificial neural network that runs inside the classification agent , especially trained with patterns that allow carrying out the desired news analysis and classifications .
Epidemiological surveillance is an important issue of public health policy . In this paper , we describe a method based on knowledge extraction from news and news classification to understand the epidemic evolution . Descriptive studies are useful for gathering information on the incidence and characteristics of an epidemic .
The developed text classification system is designed to automate news classification process . Text corpus for training and testing the system is formed from Azerbaijani news articles . The system will be useful for online news categorization and automatic news labeling for news agencies . Naive Bayes , Support Vector Machines , and Artificial Neural Networks have been implemented to solve the news classification problem . Moreover , a number of approaches such as stemming , stop word removal , feature reduction have been implemented for both performance and accuracy improvements .
JSP News Release System , adopting JDK 1.5.0_01,as the development environment , MYSQL as the database server , RESIN-2.1.as the testing server , has finished all functions of a news release system , such as news classification setup , news release and deletion , frontpage buildup , and background management .
Different classification algorithms are applied on News data set . A comparative study of these algorithms is done based on Accuracy , Time , Errors and ROC to predict the best algorithm for news data set classification . Findings : Present study analyzed result on the basis of accuracy , time , error and ROC curve .
In order to solve the problem of data inconsistency in parallel computing , a method of dynamically acquiring cluster increments and random delays is adopted to improve the Single-Pass algorithm . In order to validate the performance of the proposed method , a case study of news events classification is carried out . Simulation results show that the proposed algorithm can effectively improve the cluster repetition in clustering results and greatly improve the accuracy and efficiency of clustering compared with the traditional Single-Pass algorithm .
We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments .
This paper also utilizes 2D convolution to sample more meaningful information of the matrix . Experiments are conducted on six text classification tasks , including sentiment analysis , question classification , subjectivity classification and newsgroup classification . Compared with the state - of - the - art models , the proposed models achieve excellent performance on 4 out of 6 tasks .
Cross lingual entity linking means linking an entity mention in a background source document in one language with the corresponding real world entity in a knowledge base written in the other language . The key problem is to measure the similarity score between the context of the entity mention and the document of the candidate entity .
These approaches often suffer from the uneven quality of translations between languages . While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment , a significant number of attributes remain largely unexplored . In this paper , we propose a joint attribute-preserving embedding model for cross-lingual entity alignment .
To stimulate research in cross-language entity linking , we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages . This paper describes an efficient way to create and curate such a collection , judiciously exploiting existing language resources .
In this paper , we propose a novel M ulti- M odal S emantic M atching approach , called MMSM , to enrich cross-lingual links for online Wikis . Specifically , MMSM jointly trains two novel end-to-end neural matching models , Entity Description Matching Model and Entity Image Matching Model , which can utilize entity description and images for the cross-lingual entity matching . To the best of our knowledge , it is the first work to utilize multi-modal information to enrich cross-lingual entity links .
Our models can be trained on partially aligned graphs , where just a small portion of triples are aligned with their cross-lingual counterparts . The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results , with some variants consistently outperforming others on different tasks . We also explore how MTransE preserves the key properties of its monolingual counterpart TransE.
This paper presents HITS ’ system for monolingual and cross-lingual entity linking at TAC 2012 . We propose a joint system for entity disambiguation , recognition of NILs and clustering using Markov Logic .
This paper presents HITS ’ system for monolingual and cross-lingual entity linking at TAC 2013 . The system is an extended version of our last year ’s joint entity disambiguation and clustering system based on Markov Logic Networks .
This paper describes the University of Sheffield ’s entry in the 2011 TAC KBP entity linking and slot filling tasks ( Ji et al. , 2011 ) . We chose to participate in the monolingual entity linking task , the monolingual slot filling task and the temporal slot filling tasks . Our team consisted of five MSc students , two PhD students and one more senior academic .
One is based on cross-lingual information networks , aligned based on monolingual information extraction , and the other uses topic modeling to ensure global consistency . We enhance a strong baseline system derived from a combination of state-of-the-art machine translation and monolingual entity linking to achieve 11.2 % improvement in B-Cubed+ F-measure . Our system achieved highly competitive results in the NIST Text Analysis Conference ( TAC ) Knowledge Base Population ( KBP2011 ) evaluation .
RSVM is used to build cross-lingual links between Chinese entities and English knowledge graphs . The experiments show that our approach can achieve a high precision of 82.3 % for the task of finding cross-lingual entities on a test dataset . Our experiments for the sub task of finding missing cross-lingual links show that our approach has a precision of 89.42 % with a recall of 80.47 % .
Using these cross-lingual DPCs , we can solve problems in one , possibly resource-poor , language using a knowledge source from another , possibly resource-rich , language . I show that the approach is also useful in tasks that inherently involve two or more languages , such as machine translation and multilingual text summarization . The proposed approach is computationally inexpensive , it can estimate both semantic relatedness and semantic similarity , and it can be applied to all parts of speech .
Cross-language document summarization is defined as the task of producing a summary in a target language ( e.g. Chinese ) for a set of documents in a source language ( e.g. English ) . Existing methods for addressing this task make use of either the information from the original documents in the source language or the information from the translated documents in the target language .
The task of cross-language document summarization aims to produce a summary in a target language ( e.g. , Chinese ) for a given document set in a different source language ( e.g. , English ) . Previous studies focus on ranking and selection of translated sentences in the target language .
The main target of cross-language summarization is to generate a summary in a different language from the language of the source document or documents . In this paper , it is proposed a Textual Energy approach to monodocument summarization plus the use of a Machine Translation online system to translate the source file from English into Spanish .
Cross-language summarization is the novel topic which is extremely practical and necessary for capturing , tracing , and retrieving the huge data . Especially , for many low-resource languages as Vietnamese , Chinese , ... , there are not any previous works to solve this problem as well as datasets .
Abstract — Cross-language summarization is the task ofgenerating a summary in a language different from the languageof the source documents . In this paper , we propose a graph-basedapproach to multi-document summarization that integratesmachine translation quality scores in the sentence extractionprocess .
Aiming to solve the problems of insufficient semantic understanding and low statement accuracy in the automatic text summarization of natural language processing , in this paper we present a novel neural network method for extractive summarization by Quadratic Check . The sentence extractor extracts the primary sentences by a scoring and selecting module , among which it selects the final sentences based on impact factor computed by the transformer model and builds the output summary by the quadratic check .
In this work , we use Hidden Markov Models ( HMM ) , Conditional Random Field ( CRF ) , Gaussian Mixture Models ( GMM ) and Mathematical Methods of Statistics ( MMS ) for Chinese and Japanese text summarization . The purpose of this work is to study the applicability of mentioned three trainable models for cross-language text summarization . For model training , we use several training features such as sentence position , sentence centrality , number of Name Entity and so on .
Except for one case , our method gave better results than the method using only BMWEs grouping . Even though this paper does not directly address the issues in Cross-Lingual Information Retrieval ( CLIR ) , it discusses an approach of direct relevance to the field . This approach could be viewed as the opposite of current trends in CLIR on semantic space that incorporate a notion of order in the bag-of-words model ( e.g. co-occurences ) .
Comparing Multiple Approaches to the Cross-Lingual 5W Task . Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation ( MT ) .
This year , we have participated in the Ad-Hoc Robust Multilingual track with the aim of evaluating two important issues in Cross-Lingual Information Retrieval ( CLIR ) systems . This paper first describes the method applied for query expansion in a multilingual environment by using web search results provided by the Google engine in order to increase retrieval robustness .
In this paper , we describe the methods we used for the Cross-Lingual Evaluation Forum CLEF 2002 , and more specifically for the GIRT Task . The methods are based on ( 1 ) the extraction of two bilingual lexicons , one from parallel corpora and the other one from comparable corpora , ( 2 ) the optimal combination of these bilingual lexicons in Cross-Language Information Retrieval and ( 3 ) the combination with monolingual IR on parallel corpora .
This paper describes the NRC submission to the Spanish Cross-Lingual Word Sense Disambiguation task at SemEval-2013 . Since this word sense disambiguation task uses Spanish translations of English words as gold annotation , it can be cast as a machine translation problem .
This article deals with the problem of Cross-Lingual Text Categorization ( CLTC ) , which arises when documents in different languages must be classified according to the same classification tree . We describe practical and cost-effective solutions for automatic Cross-Lingual Text Categorization , both in case a sufficient number of training examples is available for each new language and in the case that for some language no training examples are available .
In this paper , we describe our submission for FIRE 2018 Shared Task on Named Entity Recognition in Indian Languages[3 ] . Identification of Named Entities is important in several higher language technology systems such as Information Extraction systems , Machine Translation systems , and Cross-Lingual Information Access systems . The system makes use of the different contextual information of the words along with the Word-Level and Character-Level features that are helpful in predicting the various Named Entity Classes .
Symbolic state transducers and recurrent neural preference machines for text mining Semi-Supervised Convolutional Neural Network for Law Advice Online PEMILIHAN FEATURE DENGAN CHI SQUARE DALAM ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI BERITA Japanese Text Classification by Character-level Deep ConvNets and Transfer Learning Internet economic news gathering and classification: a neural network software agent based approach Mining Web Data for Epidemiological Surveillance Empirical Study of Online News Classification Using Machine Learning Approaches JSP News Release System News Classification: A Data Mining Approach A Storm-Based Parallel Clustering Algorithm of Streaming Data Explicit Interaction Model towards Text Classification Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Cross Lingual Entity Linking with Bilingual Topic Model Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding Creating and Curating a Cross-Language Person-Entity Linking Collection Enrich cross-lingual entity links for online wikis via multi-modal semantic matching Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment HITS' Monolingual and Cross-lingual Entity Linking System at TAC 2012: A Joint Approach HITS' Monolingual and Cross-lingual Entity Linking System at TAC 2013 USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding Analysis and Refinement of Cross-Lingual Entity Linking Cross-Lingual Entity Query from Large-Scale Knowledge Graphs Measuring semantic distance using distributional profiles of concepts Using Bilingual Information for Cross-Language Document Summarization Cross-language document summarization via extraction and ranking of multiple summaries A New Cross-Lingua Automatic Summerization Approach Based on Textual Energy Phrase-Based Compressive Summarization for English-Vietnamese A Graph-based Approach to Cross-language Multi-document Summarization Extractive Summarization Based on Quadratic Check A study on cross-language text summarization using supervised methods Multi-Word Expression-Sensitive Word Alignment Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task SINAI at CLEF 2006 ad hoc robust multilingual track: query expansion using the Google search engine XRCE Participation in CLEF 2002 NRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10) Cross-Lingual Text Categorization HiLT@IECSIL-FIRE-2018: A Named Entity Recognition System for Indian Languages
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2003 2019 2015 2017 2002 2012 2018 2007 2016 2018 2019 2016 2013 2017 2012 2020 2016 2012 2013 2011 2012 2015 2008 2011 2019 2016 2011 2019 2009 2010 2009 2006 2002 2013 2003 2018
ICAART (2) SBRN PAKDD Workshops ICONIP (4) AAAI COLING IJCAI LREC TAC TAC TAC CLEF APWeb Workshops ACL IUKM CSAE NLPKE ACL/IJCNLP CLEF (Working Notes) CLEF (Working Notes) SemEval@NAACL-HLT ECDL FIRE (Working Notes)
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