Using Coreference to Improve Passage Retrieval for Question Answering
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From: AAAI Technical Report FS-99-02. Compilation copyright © 1999, AAAI (www.aaai.org). All rights reserved. Using Coreference to Improve Passage Retrieval for Question Answering Thomas S. Morton Department of Computer and Information Science University of Pennsylvania t smorton©linc, cis, upenn, edu Abstract the user will likely not need to examine any of the re- trieved documents in their entire~: This also differs Wepresent a system which retrieves answers to from the document summarization, provided by many queries based on term weighting supplemented search engines today, in that the sentences selected are by coreference relationships betweenentities in a document. An evaluation of this system is given influenced by the query and are selected across multiple which demonstrates that the coreference relation- documents. ships allov~ significantly morequestions to be an- We view a system such as ours as providing a sec- swered than with a baseline system which doesn’t ondary level of processing after a small set of docu- modelthese relationships. ments, which the user believes contain the information desired, have been found. This first step would likely be 1 provided by a traditional search engine, thus this tech- Introduction nology serves as an enhancement to an existing docu- Search engines have become ubiquitous as a means for ment retrieval systems rather than a replacement. Ad- accessing information. When a ranking of documents vancements in document retrie~-al would only help the is returned by a search engine the information retrieval performance of a system such as ours as these improve- task is usually not complete. The document, as a unit ments would increase the likelihood that the answer to of information, is often too large for manyusers infor- the user’s query is in one of the top ranked documents mation needs and finding information within the set of returned. returned documents poses a burden of its own. Here we examine a technique for extracting sentences from Approach documents which attempts to satisfy the users infor- mation needs by providing an answer to the query pre- Oneof the difficulties in extracting text from the middle sented. The system does this by modeling coreference of a documentis that this text refers to its surround- relationships between entities in the document and uses Lug discourse context and often is incomplete without that context. While the relationship between a sen- this information to better select passages which contain answers. An evaluation of this system is given which tence and the discourse context in which it appears can demonstrates that it performs better than a standard be extremely complex, some of these relationships can weighting scheme without coreference relations. be modeled. In this paper we attempt to model iden- tity coreference relationships between noun phrases in a document and then use these relationships to better Problem Statement rank segments of text for question answering. A query indicates an informational need by the user to The system currently models coreference relation- the search engine. The information required may take ships between entities occuring as proper noun phrases, the form of a sentence or even a noun phrase. Here such as names and locations, definite noun phrases, the task is to retrieve the passage of text which con- typically beginning with "the" in English, and non- tains the answer to the query from a small collection possessive, third person pronouns such as "he", "she", of documents. Passages are then ranked and presented "it", and "they". to the user. We only examine queries to which answers are likely to be stated in a sentence or noun phrase Implementation since answers which are typically longer can be difficult to annotate reliably. This technology differs from the The system processes a query and a set of documents standard document ranking task in that, if successful by first preprocessing the documents to prepare them for the coreference component of the system. Once this 1Copyright (~)1999, AmericanAssociation for Artificial is done the system identifies coreference relationships Intelligence (w~v.aaai.org). All rights reserved. between noun phrases in the document. Finally each 72 sentence is ranked based on its terms and the terms of 5. In which noun phrase in a sentence did the pronoun noun phrases coreferent with it. occur (first, second,...). Preprocessing 6. Which pronoun is being considered. Determining coreference between noun phrases requires 7. Are the pronoun and the noun phrase compatible in that the noun phrases in the text have been identified. number. This processing begins by preprocessing the HTML 8. If the candidate noun phrase is another pronoun is it to determine likely boundaries between segments of compatible with the referring pronoun. text, sentence detecting these segments using a sentence detector described in (Reynar & Ratnaparkhi 1997), 9. If the candidate noun phrase is another pronoun is it and tokenizing those sentences using a tokenizer de- the same as the referring pronoun. scribed in (Reynar 1998). The text can then be part The model is trained on nearly 1200 annotated exam- of speech tagged using the tagger described in (Ratna- ples of pronounswhich refer or fail to refer to previously parkhi 1996) and finally noun phrases are determined occurring noun phrases. using a maximumentropy model trained on the Penn Treebank(Marcus, Santorini, & Marcinkiewicz 1994). Segment Ranking The output of Nymble(Bikel et al. 1997), a named en- Segments for both systems are scored and ranked based tity recognizer which determines which words are peo- on the sum of the idf weights(Salton 1989) for each ple’s names, organizations, locations, etc., is also used unique term which occurs in the segment and also for determining coreference relationships. occurs in the query. The idf weights are computed Coreference based on the documents found on TRECdiscs 4 and 5 (Voorhees & Harman 1997). No additional score Once preprocessing is completed the system iterates given for tokens occuring more than once in a segment. through each of the noun phrases to determine if they Segments for the baseline system are 250 byte se- refer to a noun phrase which has occured previously. quences of text. The coreference enhanced system ranks Only proper noun phrases, definite noun phrases, and each sentences detected during the preprocessing phase. non-possessive third person pronouns are considered. In the coreference enhanced system, terms which occur Proper nouns phrases are determined by the part of in a coreferential noun phrase are also considered to speech assigned to the last word in the noun phrase. A have occured in that s%~ment. proper noun phrase is considered coreferent with a pre- viously occuring noun phrase if it is a substring of that Evaluation noun phrase excluding abbreviations and words which are not proper nouns. A noun phrase is considered def- For the evaluation of the system fifty queries were se- inite if it begins with a the determiner "the" or begins lected from a collection of actual queries presented to with a possessive pronoun or a past participle verb. A an online search en~o-ine2. Queries were selected based definite noun phrase is considered coreferent with an- on their expressing the users information need clearly, other noun phrase if the last word in the noun phrase their being likely answered in a single sentence, and matches the last word in a previously occuring noun non-dubious intent. phrase. The mechanism for resolving pronouns consists The queries were then presented to Altavista 3 and of a maximumentropy model which examines two noun the top 20 ranked documents were retrieved. The doc- phrases and produces a probability that they co-refer. uments were then processed by the baseline system as The twenty previously occuring noun phrases are con- well as the coreference system and the top ten ranked sidered as well as the possibility that the pronounrefers text segments were presented to the user. Since some to none of these noun phrases. The pair with the high- of the documents retrieved were very long the baseline est probability are considered coreferent or the pronoun system was used to select the best segment and then is left unresolved whenthe model predicts that the most the coreference system was applied to a 10K window likely outcome is that the pronoun doesn’t refer to any surrounding that segment. of the proceeding noun phrases. The model considers Each system presented the user with the top 10 the following features: ranked segments. Segments for both systems were lim- The category of the noun phrase being considered as ited to 250 bytes. In the coreference s~tem equal 1. amounts of text from each side of the sentence were determined by the named entity recognizer. added if the sentence was less than 250 bytes and the 2. Howmany noun phrases occur between the candidate sentence was truncated if it was more than 250 bytes. noun phrase and the pronoun. The output of the systems was evaluated by a novice 3. How many sentences occur between the candidate user and the systems were scored using the following noun phrase and the pronoun. metric: 4. Whichnoun phrase in a sentence is being referred to 2Electric Monk, ~.electricmonk.com (first, second, ...). 3Altavista, ~-~rw.altavista, corn of the coreference relations which are recovered from ,~ lo-(r~.k(q)-l) the text. Specifically we would like to model quoted ~q:l I0 speech contexts which would allow us to consider first n and second person pronouns. Here n is the number of questionsprocessed and rank(q)is the rankingof the firstsegment to answer References questionq. Usingthis metric the systemsperformed as Baldwin, B., and Morton, T. 1998. Dynamic follows: coreference-based summarization. In Proceedings of the Third Conference on Empirical Methods in Natu- Baseline I ICoreference 30.66% 44.89% ral Language Processing.