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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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 (

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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