In this paper , we propose a technique about Automatic temporal extraction of chronological information in natural language processing ( NLP ) . Temporal information extraction has value in question-answering , human decision-making and summarization . Time-revealing vocabularies are investigated and grouped into a lexical pattern of temporal expression . This paper proposes two new InformationRetrieval performance metrics based on multigrade relevance , called Q-measure and R-measure , which are akin to Cumulative Gain and Average Weighted Precision but are arguably more reliable . We then show how Qmeasure can be appliedto Question Answering involving ranked lists of exact answers , and discuss its advantages over Reciprocal Rank through an experiment using the QAC1 test collection . The appendices of this paper contain theorem proofs concerning Q-measure and R-measure , as well as a study of Q-measure and R-measure as Information Retrieval evaluation metrics using the runs submitted to the NTCIR-3 CLIR task . Questions are an important trigger of learning as well as social interaction , and question asking is a common technique that teachers and practitioners use in classrooms for motivating and engaging students . In the context of online video-based learning such as MOOCs , however , there remains limited understanding around whether questions benefit online learning and how to systematically use questions to support learners online . Social networking services ( SNS ) have been deployed within enterprises to encourage informal social interactions and information sharing . As such , users have turned to the status message functionality in a SNS for social information seeking by employing it as a medium for question asking . In this paper , we present the results of a qualitative study observing emergent question and answer ( Q&A ) behaviors in an enterprise SNS and then describe user motivations in employing this medium for social information seeking . Named Entity Recognition in Question Answer tasks can help the language understanding . Traditional NER task usually solved by a supervised model , which need a large number of annotated corpora and extract context information as feature for training , however questions are usually short sentences , with little context information , little public annotated corpora . Similar sentences extraction is an essential issue for many applications , such as natural language processing , Web page retrieval , question-answer model , and so forth . Although there are many studies exploring on this issue , most of them focus on how to improve the effectiveness aspect . This paper presents a novel Web Question Answering(WQA ) approach based on the combination of Combinatory Categorial Grammar ( CCG ) , Description Logic ( DL ) ontology and Web Service ( WS ) , in order to promote semantic-level accuracy through deep text understanding and improve the solutions for non-WH questions . Our framework transforms a question answering problem into a service planning problem for which HTN planner-SHOP2 is used . It addresses transition from natural language to semantic expression and dynamical composition of web services via planning , and execution of the web services planning . Directly reading documents and being able to answer questions from them is an unsolved challenge . To avoid its inherent difficulty , question answering ( QA ) has been directed towards using Knowledge Bases ( KBs ) instead , which has proven effective . Unfortunately KBs often suffer from being too restrictive , as the schema can not support certain types of answers , and too sparse , e.g. Wikipedia contains much more information than Freebase . Their model , however , uses the attention weights directly in the output layer and does not take advantage of the attention flow to the modeling layer . section : Question Answering Experiments In this section , we evaluate our model on the task of question answering using the recently released SQuAD rajpurkar2016squad , which has gained a huge attention over a few months . Automation of question generation from a text has been one of the focus of research in recent years . In the education field , question generation can be used to assist in the generation of questions to be used as evaluations of learning outcomes . The process of generating questions with different difficulty levels manually is not easy . Visual question answering ( VQA ) and visual question generation ( VQG ) are two trending topics in the computer vision , but they are usually explored separately despite their intrinsic complementary relationship . In this paper , we propose an end-to-end unified model , the Invertible Question Answering Network ( iQAN ) , to introduce question generation as a dual task of question answering to improve the VQA performance . With our proposed invertible bilinear fusion module and parameter sharing scheme , our iQAN can accomplish VQA and its dual task VQG simultaneously . This paper presents a dialogue system that acquires user 's food preference through a conversation . First , we proposed a method for selecting relevant topics and generating questions based on Freebase , a large-scale knowledge graph . To select relevant topics , using the Wikipedia corpus , we created a topic-embedding model that represents the correlation among topics . Pre-processing module implements Hobbs Algorithm for anaphora resolution and direct to indirect speech conversion for handling quoted statements . In generating questions , question overgeneration and ranking framework of Heilman and Smith is used . The distractor selection module uses collocation extraction and Wordnet-based methods such as Lin 's semantic similarity measure , hypernyms , hyponyms and maximal bipartite graph matching . In Experiment 2 , four reading strategies were tested to find the key aspects of answering question strategy . Three strategies related with question answering - generating questions , generating and answering questions , answering peer generated questions - and rereading strategy were tested . Generating condition yielded the best performance , whereas generating and answering questions yielded the worst performance . The coaches were interviewed with a focus on how informal coaching works by examining coaching challenges , how coaching can be organized , whether coaching should be anonymous or personal , which tools can be used and how informal math coaching supports learning . Research shows that generating and answering questions are important in the process of understanding and learning , which means that both the students and the coaches can learn math through this type of project . The coaches perceive informal math coaching as complementing online math forums . Unlike existing pre-training methods , ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data , which enhances the correlation between encoder and decoder . Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks , including abstractive summarization ( Gigaword and CNN/DailyMail ) , question generation ( SQuAD ) , dialogue generation ( Persona-Chat ) and generative question answering ( CoQA ) . The proposed approach is based on the representation of the main domain concepts as a set of attributes and relating these attributes to the user models and to a syntactico-semantic taxonomy , that describes the general relationships between conceptual and linguistic knowledge . This taxonomy is used for both generating questions and answers and also for extracting data from the web . The data extracted from the web documents is represented as instances of the domain concepts . The proposed approach is based on the representation of the main domain concepts as a set of attributes and relating these attributes to the user models and to a syntactico-semantic taxonomy , that describes the general relationships between conceptual and linguistic knowledge . This taxonomy is used for both generating questions and answers and also for extracting data from the web . The data extracted from the web documents is represented as instances of the domain concepts . Therefore , this study aims to introduce a goal-driven process evaluation method based on PM for healthcare processes . The proposed method comprises the following steps : defining goals and questions , data extraction , data preprocessing , log and pattern inspection , PM analysis and generating answers to questions , evaluating results , and initiating proposals for process improvements . The proposed method was applied in a case study on the surgery process of a university hospital in Turkey , which revealed for quantitative insights into the process . In this paper we evaluate the performance of question generation using contextual reasoning in dialogs generated from an Ontology to provide identification , authentication , and access control . Generating relevant questions requires frequent querying after each question , using its context to generate the next one . This process is tedious and might lead to a situation where the interviewee has to wait for a question for an unacceptably long time . Existing efforts at AQG have been limited to short answer lengths of up to two or three sentences . However , several real-world applications require question generation from answers that span several sentences . Therefore , we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers . We construct and release a dataset of 25 , 100 publicly available questions classified into well - formed and non - wellformed categories and report an accuracy of 70.7 % on the test set . We also show that our classifier can be used to improve the performance of neural sequence - to - sequence models for generating questions for reading comprehension . section : Introduction Our model is unable to capture the relationship between “ did ” and “ made ” and thus marks this as a well - formed question . section : Improving Question Generation Automatic question generation is the task of generating questions that ask about the information or facts present in either a given sentence or paragraph . Knowledge-based question answering relies on the availability of facts , the majority of which can not be found in structured sources ( e.g. Wikipedia info-boxes , Wikidata ) . One of the major components of extracting facts from unstructured text is Relation Extraction ( RE ) . Knowledge Base Question Answering(KBQA ) is an important task in NLP domain and equally difficult . Billions of facts can be accessed with KBQA in efficient way . With the rapid growth of data , knowledge base question answering ( KBQA ) is becoming more and more important . However , most existing methods of KBQA take every word in the question into account , leading to serious semantic confusion of the question and low efficiency of the question answering ( QA ) System . Entity linking is a central concern of automatic knowledge question answering and knowledge base population . Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities . In this paper we present the design , architecture and implementation of KNADIA , a conversational dialogue system for intra-enterprise use , providing knowledge-assisted question answering and transactional assistance to employees of a large organization . KNADIA has been deployed in production in TCS , a large organization with over 380,000 employees distributed globally ; the system is currently supporting a few thousand active users making hundreds of queries per day . Pratt and Third 's syllogistic fragments of English can be used to capture , in addition to syllogistic reasoning , many other kinds of common sense reasoning , and , in particular ( i ) knowledge base consistency and ( ii ) knowledge base query answering , modulo their FO semantic representations . We show how difficult , in terms of semantic ( computational ) complexity and data complexity ( i.e. , computational complexity w.r.t . The general upper-level ontology and wide coverage makes the knowledge base a valuable Chinese semantic resource . It not only can be used in Chinese linked data building , the fundamental work for building multi lingual knowledge base across heterogeneous resources of different languages , but also can largely facilitate many useful applications of large-scale knowledge base such as knowledge question-answering and semantic search . The origin and de- velopment of ontology in knowledge engineering , the definition and role in knowledge base system are discussed in the paper . Then , the ap- proach to building the knowledge base system is studied and its application is introduced a question knowledge base system as an example of . The primary mode of interaction is typed dialogue , but COMPS also provides problem-specific affordances for exploring a problem . This paper reports qualitatively on dialogues from students employed in four different activities : two logical reasoning problems in a quantitative literacy class and two different problems in object-oriented Java in an elementary programming class . In all domains we observe behaviors consistent with quality collaborative learning experiences : co-construction of knowledge , mixed initiative dialogue , coming to common agreement , and students adopting different roles in the problem-solving process . Biomolecules inside a microfluidic system can be used to solve computational problems , such as theorem proving , which is an important class of logical reasoning problems . In this article , the Boolean variables ( literals ) were represented using single-stranded DNA molecules , and theorem proving was performed by the hybridization and ligation of these variables into a double-stranded " solution " DNA . The goal of bioinformatics is to develop innovative and practical methods and algorithms for bio- logical questions . In many cases , these questions are driven by new biotechnological techniques , especially by genome and cell wide high throughput experiment studies . Perhaps the most significant consequence of Cox 's work is that probability represents a subjective degree of plausible belief relative to a particular system but is a theory that applies universally and objectively across any system making inferences based on an incomplete state of knowledge . Cox goes well beyond this amazing conceptual advancement , however , and begins to formulate a theory of logical questions through his consideration of systems of assertions -- a theory that he more fully developed some years later . Although Cox 's contributions to probability are acknowledged and have recently gained worldwide recognition , the significance of his work regarding logical questions is virtually unknown . This paper discusses some aspects of the planning of cooperative negative responses for logic-based question-answering systems . In this kind of environment the search for additional information other than the direct answer requires the formulation of logical formulae to be submitted to a theorem prover . Such relations as class inclusion , possession , and active or passive actions are used as definitional material . The resulting structure serves as a powerful vehicle for research on the logic of question answering . Examples of methods of inputting information and answering simple English questions are given . In this paper , we explore how large multi-touch tables can be used by groups of students as an external representation of their group interaction processes . Video data from 24 groups of students working on a logic reasoning problem was examined to identify their levels of reasoning about the task , and how they used the table to support their reasoning . Results indicate that of the 13 groups who solved or nearly solved the problem , 12 used the table to represent their reasoning process , while only four groups who used the table to support their reasoning process did not solve the problem . We present a Question Answering system for technical domains which makes an intelligent use of paraphrases to increase the likelihood of finding the answer to the user 's question . The system implements a simple and efficient logic representation of questions and answers that maps paraphrases to the same underlying semantic representation . Further , paraphrases of technical terminology are dealt with by a separate process that detects surface variants . Some Problems of a Machine Translation Experiment ( by Kirschner , Zdenek ) 23 . Disambiguating Interrogative Sentences through a Logical Analysis of Answers ( by Materna , Pavel ) The logicbased approach allows the formalization of semantics and background knowledge , which play a vital role in deriving answers . We present the functional LogAnswer prototype , which consists of automated theorem provers for logical answer derivation as well as an environment for deep linguistic processing .
In this paper , we propose a technique about Automatic temporal extraction of chronological information in natural language processing ( NLP ) . Temporal information extraction has value in question-answering , human decision-making and summarization . Time-revealing vocabularies are investigated and grouped into a lexical pattern of temporal expression .
This paper proposes two new InformationRetrieval performance metrics based on multigrade relevance , called Q-measure and R-measure , which are akin to Cumulative Gain and Average Weighted Precision but are arguably more reliable . We then show how Qmeasure can be appliedto Question Answering involving ranked lists of exact answers , and discuss its advantages over Reciprocal Rank through an experiment using the QAC1 test collection . The appendices of this paper contain theorem proofs concerning Q-measure and R-measure , as well as a study of Q-measure and R-measure as Information Retrieval evaluation metrics using the runs submitted to the NTCIR-3 CLIR task .
Questions are an important trigger of learning as well as social interaction , and question asking is a common technique that teachers and practitioners use in classrooms for motivating and engaging students . In the context of online video-based learning such as MOOCs , however , there remains limited understanding around whether questions benefit online learning and how to systematically use questions to support learners online .
Social networking services ( SNS ) have been deployed within enterprises to encourage informal social interactions and information sharing . As such , users have turned to the status message functionality in a SNS for social information seeking by employing it as a medium for question asking . In this paper , we present the results of a qualitative study observing emergent question and answer ( Q&A ) behaviors in an enterprise SNS and then describe user motivations in employing this medium for social information seeking .
Named Entity Recognition in Question Answer tasks can help the language understanding . Traditional NER task usually solved by a supervised model , which need a large number of annotated corpora and extract context information as feature for training , however questions are usually short sentences , with little context information , little public annotated corpora .
Similar sentences extraction is an essential issue for many applications , such as natural language processing , Web page retrieval , question-answer model , and so forth . Although there are many studies exploring on this issue , most of them focus on how to improve the effectiveness aspect .
This paper presents a novel Web Question Answering(WQA ) approach based on the combination of Combinatory Categorial Grammar ( CCG ) , Description Logic ( DL ) ontology and Web Service ( WS ) , in order to promote semantic-level accuracy through deep text understanding and improve the solutions for non-WH questions . Our framework transforms a question answering problem into a service planning problem for which HTN planner-SHOP2 is used . It addresses transition from natural language to semantic expression and dynamical composition of web services via planning , and execution of the web services planning .
Directly reading documents and being able to answer questions from them is an unsolved challenge . To avoid its inherent difficulty , question answering ( QA ) has been directed towards using Knowledge Bases ( KBs ) instead , which has proven effective . Unfortunately KBs often suffer from being too restrictive , as the schema can not support certain types of answers , and too sparse , e.g. Wikipedia contains much more information than Freebase .
Their model , however , uses the attention weights directly in the output layer and does not take advantage of the attention flow to the modeling layer . section : Question Answering Experiments In this section , we evaluate our model on the task of question answering using the recently released SQuAD rajpurkar2016squad , which has gained a huge attention over a few months .
Automation of question generation from a text has been one of the focus of research in recent years . In the education field , question generation can be used to assist in the generation of questions to be used as evaluations of learning outcomes . The process of generating questions with different difficulty levels manually is not easy .
Visual question answering ( VQA ) and visual question generation ( VQG ) are two trending topics in the computer vision , but they are usually explored separately despite their intrinsic complementary relationship . In this paper , we propose an end-to-end unified model , the Invertible Question Answering Network ( iQAN ) , to introduce question generation as a dual task of question answering to improve the VQA performance . With our proposed invertible bilinear fusion module and parameter sharing scheme , our iQAN can accomplish VQA and its dual task VQG simultaneously .
This paper presents a dialogue system that acquires user 's food preference through a conversation . First , we proposed a method for selecting relevant topics and generating questions based on Freebase , a large-scale knowledge graph . To select relevant topics , using the Wikipedia corpus , we created a topic-embedding model that represents the correlation among topics .
Pre-processing module implements Hobbs Algorithm for anaphora resolution and direct to indirect speech conversion for handling quoted statements . In generating questions , question overgeneration and ranking framework of Heilman and Smith is used . The distractor selection module uses collocation extraction and Wordnet-based methods such as Lin 's semantic similarity measure , hypernyms , hyponyms and maximal bipartite graph matching .
In Experiment 2 , four reading strategies were tested to find the key aspects of answering question strategy . Three strategies related with question answering - generating questions , generating and answering questions , answering peer generated questions - and rereading strategy were tested . Generating condition yielded the best performance , whereas generating and answering questions yielded the worst performance .
The coaches were interviewed with a focus on how informal coaching works by examining coaching challenges , how coaching can be organized , whether coaching should be anonymous or personal , which tools can be used and how informal math coaching supports learning . Research shows that generating and answering questions are important in the process of understanding and learning , which means that both the students and the coaches can learn math through this type of project . The coaches perceive informal math coaching as complementing online math forums .
Unlike existing pre-training methods , ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data , which enhances the correlation between encoder and decoder . Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks , including abstractive summarization ( Gigaword and CNN/DailyMail ) , question generation ( SQuAD ) , dialogue generation ( Persona-Chat ) and generative question answering ( CoQA ) .
The proposed approach is based on the representation of the main domain concepts as a set of attributes and relating these attributes to the user models and to a syntactico-semantic taxonomy , that describes the general relationships between conceptual and linguistic knowledge . This taxonomy is used for both generating questions and answers and also for extracting data from the web . The data extracted from the web documents is represented as instances of the domain concepts .
The proposed approach is based on the representation of the main domain concepts as a set of attributes and relating these attributes to the user models and to a syntactico-semantic taxonomy , that describes the general relationships between conceptual and linguistic knowledge . This taxonomy is used for both generating questions and answers and also for extracting data from the web . The data extracted from the web documents is represented as instances of the domain concepts .
Therefore , this study aims to introduce a goal-driven process evaluation method based on PM for healthcare processes . The proposed method comprises the following steps : defining goals and questions , data extraction , data preprocessing , log and pattern inspection , PM analysis and generating answers to questions , evaluating results , and initiating proposals for process improvements . The proposed method was applied in a case study on the surgery process of a university hospital in Turkey , which revealed for quantitative insights into the process .
In this paper we evaluate the performance of question generation using contextual reasoning in dialogs generated from an Ontology to provide identification , authentication , and access control . Generating relevant questions requires frequent querying after each question , using its context to generate the next one . This process is tedious and might lead to a situation where the interviewee has to wait for a question for an unacceptably long time .
Existing efforts at AQG have been limited to short answer lengths of up to two or three sentences . However , several real-world applications require question generation from answers that span several sentences . Therefore , we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers .
We construct and release a dataset of 25 , 100 publicly available questions classified into well - formed and non - wellformed categories and report an accuracy of 70.7 % on the test set . We also show that our classifier can be used to improve the performance of neural sequence - to - sequence models for generating questions for reading comprehension . section : Introduction
Our model is unable to capture the relationship between “ did ” and “ made ” and thus marks this as a well - formed question . section : Improving Question Generation Automatic question generation is the task of generating questions that ask about the information or facts present in either a given sentence or paragraph .
Knowledge-based question answering relies on the availability of facts , the majority of which can not be found in structured sources ( e.g. Wikipedia info-boxes , Wikidata ) . One of the major components of extracting facts from unstructured text is Relation Extraction ( RE ) .
Knowledge Base Question Answering(KBQA ) is an important task in NLP domain and equally difficult . Billions of facts can be accessed with KBQA in efficient way .
With the rapid growth of data , knowledge base question answering ( KBQA ) is becoming more and more important . However , most existing methods of KBQA take every word in the question into account , leading to serious semantic confusion of the question and low efficiency of the question answering ( QA ) System .
Entity linking is a central concern of automatic knowledge question answering and knowledge base population . Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities .
In this paper we present the design , architecture and implementation of KNADIA , a conversational dialogue system for intra-enterprise use , providing knowledge-assisted question answering and transactional assistance to employees of a large organization . KNADIA has been deployed in production in TCS , a large organization with over 380,000 employees distributed globally ; the system is currently supporting a few thousand active users making hundreds of queries per day .
Pratt and Third 's syllogistic fragments of English can be used to capture , in addition to syllogistic reasoning , many other kinds of common sense reasoning , and , in particular ( i ) knowledge base consistency and ( ii ) knowledge base query answering , modulo their FO semantic representations . We show how difficult , in terms of semantic ( computational ) complexity and data complexity ( i.e. , computational complexity w.r.t .
The general upper-level ontology and wide coverage makes the knowledge base a valuable Chinese semantic resource . It not only can be used in Chinese linked data building , the fundamental work for building multi lingual knowledge base across heterogeneous resources of different languages , but also can largely facilitate many useful applications of large-scale knowledge base such as knowledge question-answering and semantic search .
The origin and de- velopment of ontology in knowledge engineering , the definition and role in knowledge base system are discussed in the paper . Then , the ap- proach to building the knowledge base system is studied and its application is introduced a question knowledge base system as an example of .
The primary mode of interaction is typed dialogue , but COMPS also provides problem-specific affordances for exploring a problem . This paper reports qualitatively on dialogues from students employed in four different activities : two logical reasoning problems in a quantitative literacy class and two different problems in object-oriented Java in an elementary programming class . In all domains we observe behaviors consistent with quality collaborative learning experiences : co-construction of knowledge , mixed initiative dialogue , coming to common agreement , and students adopting different roles in the problem-solving process .
Biomolecules inside a microfluidic system can be used to solve computational problems , such as theorem proving , which is an important class of logical reasoning problems . In this article , the Boolean variables ( literals ) were represented using single-stranded DNA molecules , and theorem proving was performed by the hybridization and ligation of these variables into a double-stranded " solution " DNA .
The goal of bioinformatics is to develop innovative and practical methods and algorithms for bio- logical questions . In many cases , these questions are driven by new biotechnological techniques , especially by genome and cell wide high throughput experiment studies .
Perhaps the most significant consequence of Cox 's work is that probability represents a subjective degree of plausible belief relative to a particular system but is a theory that applies universally and objectively across any system making inferences based on an incomplete state of knowledge . Cox goes well beyond this amazing conceptual advancement , however , and begins to formulate a theory of logical questions through his consideration of systems of assertions -- a theory that he more fully developed some years later . Although Cox 's contributions to probability are acknowledged and have recently gained worldwide recognition , the significance of his work regarding logical questions is virtually unknown .
This paper discusses some aspects of the planning of cooperative negative responses for logic-based question-answering systems . In this kind of environment the search for additional information other than the direct answer requires the formulation of logical formulae to be submitted to a theorem prover .
Such relations as class inclusion , possession , and active or passive actions are used as definitional material . The resulting structure serves as a powerful vehicle for research on the logic of question answering . Examples of methods of inputting information and answering simple English questions are given .
In this paper , we explore how large multi-touch tables can be used by groups of students as an external representation of their group interaction processes . Video data from 24 groups of students working on a logic reasoning problem was examined to identify their levels of reasoning about the task , and how they used the table to support their reasoning . Results indicate that of the 13 groups who solved or nearly solved the problem , 12 used the table to represent their reasoning process , while only four groups who used the table to support their reasoning process did not solve the problem .
We present a Question Answering system for technical domains which makes an intelligent use of paraphrases to increase the likelihood of finding the answer to the user 's question . The system implements a simple and efficient logic representation of questions and answers that maps paraphrases to the same underlying semantic representation . Further , paraphrases of technical terminology are dealt with by a separate process that detects surface variants .
Some Problems of a Machine Translation Experiment ( by Kirschner , Zdenek ) 23 . Disambiguating Interrogative Sentences through a Logical Analysis of Answers ( by Materna , Pavel )
The logicbased approach allows the formalization of semantics and background knowledge , which play a vital role in deriving answers . We present the functional LogAnswer prototype , which consists of automated theorem provers for logical answer derivation as well as an environment for deep linguistic processing .
Automatic temporal event recognition from Thai news New Performance Metrics Based on Multigrade Relevance: Their Application to Question Answering Questionization of Peer-Generated Comments for Supporting Online Video-based Learning What Are You Working On? Status Message Q&A in an Enterprise SNS Fine-Grained Named Entity Recognition in Question Answering with DBpedia Towards Efficient Similar Sentences Extraction Combining CCG Parsing and DL for Chinese Question Answering Key-Value Memory Networks for Directly Reading Documents Bidirectional Attention Flow for Machine Comprehension Indonesian Question Generation Based on Bloom's Taxonomy Using Text Analysis Visual Question Generation as Dual Task of Visual Question Answering Exploiting a Large-scale Knowledge Graph for Question Generation in Food Preference Interview Systems Multiple-Choice Item Generator for Narrative and Declarative Texts Effects of reading strategies on comprehension and metacomprehension Informal math coaching by instant messaging: Two case studies of how university students coach K-12 students ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation Personalized Questions, Answers and Grammars: Aiding the Search for Relevant Web Information Personalized questions, answers and grammars: aiding the search for relevant web information A Goal-Driven Evaluation Method Based On Process Mining for Healthcare Processes Performance of Generating Dialogs from Ontology and Context Towards Automatic Generation of Questions from Long Answers Identifying Well-formed Natural Language Questions Simple Large-scale Relation Extraction from Unstructured Text Improved Knowledge Base Question Answering System Using Re-ranking Approach Intent Identification for Knowledge Base Question Answering A collective entity linking algorithm with parallel computing on large-scale knowledge base KNADIA: Enterprise KNowledge Assisted DIAlogue Systems Using Deep Learning The Data Complexity of the Syllogistic Fragments of English Building a Large Scale Knowledge Base from Chinese Wiki Encyclopedia Research on Knowledge Base System Based on Ontology Observations of Collaborative Behavior in COMPS Computer Mediated Problem Solving Biomolecular theorem proving on a chip: a novel microfluidic solution to a classical logic problem. Context based bioinformatics The Algebra of Probable Inference COOPLAN: a logic-based framework for cooperative response generation Storage and retrieval of aspects of meaning in directed graph structures Creating joint representations of collaborative problem solving with multi-touch technology Exploiting Paraphrases in a Question Answering System Prague studies in mathematical linguistics LogAnswer - A Deduction-Based Question Answering System (System Description)
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NTCIR CCHI ECSCW IDEAL EMNLP ICLR IUI Companion CogSci INLG AICCSA EMNLP TAAI ICDE Amsterdam Colloquium on Logic, Language and Meaning JIST MAICS IWP@ACL
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