Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using plans to understand natural language
ACM '76 Proceedings of the 1976 annual conference
The process of question answering.
The process of question answering.
Ranking suspected answers to natural language questions using predictive annotation
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A question answering system supported by information extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Linguistic knowledge can improve information retrieval
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Experiments with open-domain textual Question Answering
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Toward semantics-based answer pinpointing
HLT '01 Proceedings of the first international conference on Human language technology research
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Question answering using maximum entropy components
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Decision tree parsing using a hidden derivation model
HLT '94 Proceedings of the workshop on Human Language Technology
Reading comprehension programs in a statistical-language-processing class
ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
A rule-based question answering system for reading comprehension tests
ANLP/NAACL-ReadingComp '00 Proceedings of the 2000 ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems - Volume 6
A machine learning approach to answering questions for reading comprehension tests
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Terminological variants for document selection and question/answer matching
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Reading comprehension tests for computer-based understanding evaluation
Natural Language Engineering
Improving the performance of question answering with semantically equivalent answer patterns
Data & Knowledge Engineering
QUESTION ANSWERING USING QUESTION CLASSIFICATION AND DOCUMENT TAGGING
Applied Artificial Intelligence
Learning textual entailment using SVMs and string similarity measures
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Paraphrase recognition using machine learning to combine similarity measures
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
A metric-based framework for automatic taxonomy induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
VENSES – a linguistically-based system for semantic evaluation
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Classification-based contextual preferences
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Taxonomy induction using hierarchical random graphs
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Constructing task-specific taxonomies for document collection browsing
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Textual question answering is a technique of extracting a sentence or text snippet from a document or document collection that responds directly to a query. Open-domain textual question answering presupposes that questions are natural and unrestricted with respect to topic. The question answering (Q/A) techniques, as embodied in today's systems, can be roughly divided into two types: (1) techniques for Information Seeking (IS), which localize the answer in vast document collections; and (2) techniques for Reading Comprehension (RC) that answer a series of questions related to a given document. Although these two types of techniques and systems are different, it is desirable to combine them for enabling more advanced forms of Q/A. This paper discusses an approach that successfully enhanced an existing IS system with RC capabilities. This enhancement is important because advanced Q/A, as exemplified by the ARDA AQUAINT program, is moving towards Q/A systems that incorporate semantic and pragmatic knowledge enabling dialogue-based Q/A. Because today's RC systems involve a short series of questions in context, they represent a rudimentary form of interactive Q/A which constitutes a possible foundation for more advanced forms of dialogue-based Q/A.