Question Answering Using Word Associations

Question Answering Using Word Associations

Ganesh Ramakrishnan (IBM India Research Labs, India) and Pushpak Bhattacharyya (IIT Bombay, India)
Copyright: © 2009 |Pages: 33
DOI: 10.4018/978-1-59904-990-8.ch033
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Abstract

Text mining systems such as categorizers and query retrievers of the first generation were largely hinged on word level statistics and provided a wonderful first-cut approach. However systems based on simple word-level statistics quickly saturate in performance, despite the best data mining and machine learning algorithms. This problem can be traced to the fact that, typically, naive, word-based feature representations are used in text applications, which prove insufficient in bridging two types of chasms within and across documents, viz. lexical chasm and syntactic chasm . The latest wave in text mining technology has been marked by research that will make extraction of subtleties from the underlying meaning of text, a possibility. In the following two chapters, we pose the problem of underlying meaning extraction from text documents, coupled with world knowledge, as a problem of bridging the chasms by exploiting associations between entities. The entities are words or word collocations from documents. We utilize two types of entity associations, viz. paradigmatic (PA) and syntagmatic (SA). We present first-tier algorithms that use these two word associations in bridging the syntactic and lexical chasms. We also propose second-tier algorithms in two sample applications, viz., question answering and text classification which use the first-tier algorithms. Our contribution lies in the specific methods we introduce for exploiting entity association information present in WordNet, dictionaries, corpora and parse trees for improved performance in text mining applications.
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1. Introduction

A QA system responds to queries like Who is the Greek God of the sea? with a precise answer like Poseidon. A slightly less ambitious goal is to identify short snippets or passages of up to several words which contain the answer.

QA has roots in classic AI-style inference engines, but in this work we focus on recent open-domain systems closer to the Information Retrieval (IR) community. Falcon Harabagiu et aI., 2000a), Webclopedia (Hovy et aI., 2000), AnswerBus (Zheng,2002) and AskMSR (Dumais et al., 2002) are some well-known research systems, as are those built at the University of Waterloo (Clarke et al., 2000; Clarke et al., 200lb), and Ask Jeeves (http://ask.com).

Most QA systems are substantial team efforts, involving the design and maintenance of question taxonomies, question classifiers, and passage scoring heuristics. The intensity of human effort involved has limited state-of-the-art QA system development to a handful of groups. The current lack of clear separation between algorithms and knowledge bases makes it hard to gage the benefits of new algorithmic ideas, and to generalize the tuning experience to new domains, new corpora, and new languages.

The description of a QA system is almost exclusively about how questions and passages are processed, how they are matched and scored, and how external knowledge bases (question taxonomy, dictionary, and thesauri) are exploited. Even if some strategies make intuitive sense, the treatment is predominantly operational (how) rather than declarative (what): it is rare to find a system-independent discussion of general properties that make one passage better answer a question than another.

Compare the QA situation with IR engines, which are available off the shelf (e.g., Lucene (Group, 2002)), require essentially no tuning, and can be deployed in minutes. The basics of the vector space model and tfidf ranking can be taught in an hour. In contrast, QA systems contain large pieces of software, lashed together in diverse ways with customized “glue” and many crucial knobs to turn. Naturally, these knobs are best turned by QA specialists rather than the end-user, which might explain in part why off-the-shelf QA packages are rare.

The broad architecture of QA systems (Clarke et aI., 2000; Harabagiu et aI., 2000a; Hovy et al., 2000; Zheng, 2002; Clarke et al., 2001b; Radev et aI., 2002) has become standard. The corpus is indexed at the level of documents or passages chopped-up at a suitable size. A shallow entity extractor, supported by a large gazette, is sometimes run on the passages to identify people, places, organizations, etc. (Abney et al., 2000; Dill et aI., 2003), which may also be indexed. A taxonomy of question types (where, when, who, how many, etc.) is built by hand, and rules tuned to map questions to types. Several QA systems assume that an answer type catalog is available. And if it is not already available, they build such catalogs with great care (Harabagiu et aI., 2000b; Hovy et al., 2000) and classify each question into an answer type. Accordingly, the question is transformed into a keyword query to be submitted to the index. Responding passages are re-ranked using a variety of strategies.

Key Terms in this Chapter

Selector: A word in a question that is a ground constant and expected to appear as it is in an answer passage.

Focus Word: The word in ‘what’, ‘which’ and ‘name’ type of questions that specifies what the type of the answer should be.

NsySQLQA: A question answering technique that recovers from the question, fragments of what might have been posed as a structured query (SQL), and extracts answer passages based on these fragments (Section §1.3.1).

BayesWN: Construction of a Bayesian Network from WordNet lexical relations and training of the Bayesian Network in a semi-supervised manner from raw text.

BayesQA: Question Answering using Bayesian Inferencing on Lexical relations (Section §1.2) using the Bayesian Network Model BayesWN.

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Table of Contents
Foreword
Xiaohua Hu
Preface
Min Song, Yi-Fang Brook Wu
Acknowledgment
Min Song, Yi-Fang Brook Wu
Chapter 1
Ying Liu
In the automated text classification, a bag-of-words representation followed by the tfidf weighting is the most popular approach to convert the... Sample PDF
On Document Representation and Term Weights in Text Classification
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Chapter 2
Yi-fang Brook Wu, Quanzhi Li
Document keyphrases provide semantic metadata which can characterize documents and produce an overview of the content of a document. This chapter... Sample PDF
Deriving Document Keyphrases for Text Mining
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Chapter 3
John Atkinson
This chapter introduces a novel evolutionary model for intelligent text mining. The model deals with issues concerning shallow text representation... Sample PDF
Intelligent Text Mining: Putting Evolutionary Methods and Language Technologies Together
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Chapter 4
Xiaoyan Yu, Manas Tungare, Weigo Yuan, Yubo Yuan, Manuel Pérez-Quiñones, Edward A. Fox
Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection... Sample PDF
Automatic Syllabus Classification Using Support Vector Machines
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Chapter 5
Xiao-Li Li
In traditional text categorization, a classifier is built using labeled training documents from a set of predefined classes. This chapter studies a... Sample PDF
Partially Supervised Text Categorization
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Chapter 6
Yu-Jin Zhang
Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification... Sample PDF
Image Classification and Retrieval with Mining Technologies
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Chapter 7
Han-joon Kim
This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve... Sample PDF
Improving Techniques for Naïve Bayes Text Classifiers
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Chapter 8
Ricco Rakotomalala, Faouzi Mhamdi
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Using the Text Categorization Framework for Protein Classification
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Chapter 9
Wilson Wong
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Featureless Data Clustering
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Chapter 10
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Swarm Intelligence in Text Document Clustering
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Chapter 11
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Chapter 12
Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet, Ladjel Bellatreche, Mimoun Malki
The classification of textual documents has been the subject of many studies. Technologies like the Web and numerical libraries facilitated the... Sample PDF
SOM-Based Clustering of Textual Documents Using WordNet
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Chapter 13
Lean Yu, Shouyang Wang, Kin Keung Lai
With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some... Sample PDF
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Chapter 14
Sangeetha Kutty
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Frequent Mining on XML Documents
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Chapter 15
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The Process and Application of XML Data Mining
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Chapter 16
Francesco Buccafurri
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Approximate Range Querying over Sliding Windows
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Chapter 17
Jan H. Kroeze
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Slicing and Dicing a Linguistic Data Cube
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Chapter 18
Yi-fang Brook Wu, Xin Chen
This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules... Sample PDF
Discovering Personalized Novel Knowledge from Text
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Chapter 19
Catia Pesquita
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Untangling BioOntologies for Mining Biomedical Information
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Chapter 20
Luis M. de Campos
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Thesaurus-Based Automatic Indexing
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Chapter 21
Concept-Based Text Mining  (pages 346-358)
Stanley Loh, Leandro Krug Wives, Daniel Lichtnow, José Palazzo M. de Oliveira
The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’)... Sample PDF
Concept-Based Text Mining
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Chapter 22
Marcello Pecoraro
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Statistical Methods for User Profiling in Web Usage Mining
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Chapter 23
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Chapter 24
Pawan Lingras
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Chapter 25
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Chapter 26
Brigitte Trousse, Marie-Aude Aufaure, Bénédicte Le Grand, Yves Lechevallier, Florent Masseglia
This chapter proposes an original approach for ontology management in the context of Web-based information systems. Our approach relies on the usage... Sample PDF
Web Usage Mining for Ontology Management
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Chapter 27
Yue-Shi Lee
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Chapter 28
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Chapter 29
G.S. Mahalakshmi, S. Sendhilkumar
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Automatic Reference Tracking
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Chapter 30
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Chapter 31
Fotis Lazarinis
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Chapter 32
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Latent Semantic Analysis and Beyond
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Chapter 33
Ganesh Ramakrishnan, Pushpak Bhattacharyya
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Question Answering Using Word Associations
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Chapter 34
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The Scent of a Newsgroup: Providing Personalized Access to Usenet Sites through Web Mining
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Chapter 35
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Chapter 36
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Chapter 37
Pasquale De Meo
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Chapter 38
Diego Liberati
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Chapter 39
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Chapter 40
E. Thirumaran
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Chapter 41
Hanna Suominen
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Chapter 42
Yanliang Qi
The biology literatures have been increased in an exponential growth in recent year. The researchers need an effective tool to help them find out... Sample PDF
Text Mining in Bioinformatics: Research and Application
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Chapter 43
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Chapter 44
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This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software... Sample PDF
A Survey of Selected Software Technologies for Text Mining
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Chapter 45
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
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Application of Text Mining Methodologies to Health Insurance Schedules
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Chapter 46
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Chapter 47
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Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is... Sample PDF
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