Exploring Correlation of Dependency Relation Paths for Answer Extraction

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 889–896, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Exploring Correlation of Dependency Relation Paths for Answer Extraction Dan Shen Department of Computational Linguistics Saarland University Saarbruecken, Germany dshen@coli.uni-sb.de Dietrich Klakow Spoken Language Systems Saarland University Saarbruecken, Germany klakow@lsv.uni-saarland.de Abstract In this paper, we explore correlation of dependency relation paths to rank candi- date answers in answer extraction. Using the correlation measure, we compare de- pendency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in ques- tion. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from train- ing. Experimental results show that our method significantly outperforms state-of- the-art syntactic relation-based methods by up to 20% in MRR. 1 Introduction Answer Extraction is one of basic modules in open domain Question Answering (QA). It is to further process relevant sentences extracted with Passage / Sentence Retrieval and pinpoint exact answers us- ing more linguistic-motivated analysis. Since QA turns to find exact answers rather than text snippets in recent years, answer extraction becomes more and more crucial. Typically, answer extraction works in the fol- lowing steps: • Recognize expected answer type of a ques- tion. • Annotate relevant sentences with various types of named entities. • Regard the phrases annotated with the ex- pected answer type as candidate answers. • Rank candidate answers. In the above work flow, answer extraction heav- ily relies on named entity recognition (NER). On one hand, NER reduces the number of candidate answers and eases answer ranking. On the other hand, the errors from NER directly degrade an- swer extraction performance. To our knowledge, most top ranked QA systems in TREC are sup- ported by effective NER modules which may iden- tify and classify more than 20 types of named en- tities (NE), such as abbreviation, music, movie, etc. However, developing such named entity rec- ognizer is not trivial. Up to now, we haven’t found any paper relevant to QA-specific NER develop- ment. So, it is hard to follow their work. In this pa- per, we just use a general MUC-based NER, which makes our results reproducible. A general MUC-based NER can’t annotate a large number of NE classes. In this case, all noun phrases in sentences are regarded as candi- date answers, which makes candidate answer sets much larger than those filtered by a well devel- oped NER. The larger candidate answer sets result in the more difficult answer extraction. Previous methods working on surface word level, such as density-based ranking and pattern matching, may not perform well. Deeper linguistic analysis has to be conducted. This paper proposes a statisti- cal method which exploring correlation of depen- dency relation paths to rank candidate answers. It is motivated by the observation that relations be- tween proper …

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