- The Oxford Handbook of Computational Linguistics
- Pragmatics and Dialogue
- Formal Grammars and Languages
- Text Segmentation
- Part-of-Speech Tagging
- Word-Sense Disambiguation
- Anaphora Resolution
- Natural Language Generation
- Speech Recognition
- Text-to-Speech Synthesis
- Finite-State Technology
- Statistical Methods
- Machine Learning
- Lexical Knowledge Acquisition
- Sublanguages and Controlled Languages
- Corpus Linguistics
- Tree-Adjoining Grammars
- Machine Translation: General Overview
- Machine Translation: Latest Developments
- Information Retrieval
- Information Extraction
- Question Answering
- Text Summarization
- Term Extraction and Automatic Indexing
- Text Data Mining
- Natural Language Interaction
- Natural Language in Multimodal and Multimedia Systems
- Natural Language Processing in Computer-Assisted Language Learning
- Multilingual On-Line Natural Language Processing
- Notes on Contributors
- Index of Authors
- Subject Index
Abstract and Keywords
Textual Question Answering (QA) identifies the answer to a question in large collections of on-line documents. By providing a small set of exact answers to questions, QA takes a step closer to information retrieval rather than document retrieval. A QA system comprises three modules: a question-processing module, a document-processing module, and an answer extraction and formulation module. Questions may be asked about any topic, in contrast with Information Extraction (IE), which identifies textual information relevant only to a predefined set of events and entities. The natural language processing (NLP) techniques used in open-domain QA systems may range from simple lexical and semantic disambiguation of question stems to complex processing that combines syntactic and semantic features of the questions with pragmatic information derived from the context of candidate answers. This article reviews current research in integrating knowledge-based NLP methods with shallow processing techniques for QA.
Keywords: textual question answering, information retrieval, document retrieval, question-answer system, natural language processing, lexical and semantic disambiguation, syntactic and semantic features
Sanda Harabagiu is an Associate Professor and holds the Jonsson School Research Initiation Chair Professorship in the Department of Computer Science at the University of Texas at Dallas. Professor Harabagiu earned a second Ph.D. from the University of Southern California, Los Angeles in 1997, having gained her first from the University of Rome, Italy in 1994. She has held faculty positions at the University of Texas at Austin and Southern Methodist University. Dr Harabagiu was a recipient of an NSF CAREER award for studying reference resolution, and led the DARPA TIDES Roadmapping Committee on Question-Answering in 2000.
Dan Moldovan is Professor in the Department of Computer Science at the University of Texas at Dallas. Professor Moldovan earned a Ph.D. degree in Electrical Engineering and Computer Science from Columbia University, New York, in 1978. He has held faculty positions at the University of Southern California and Southern Methodist University. Professor Moldovan's current research interests are in natural language processing, particularly in question-answering technology, knowledge acquisition, and knowledge bases. He has published over 150 research papers, one textbook, and several book chapters.
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