TAILIEUCHUNG - Báo cáo khoa học: "Exploring Correlation of Dependency Relation Paths for Answer Extraction"

In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. 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 training. Experimental results show that our method significantly outperforms state-ofthe-art syntactic relation-based methods by up to 20% in MRR. . | Exploring Correlation of Dependency Relation Paths for Answer Extraction Dan Shen Department of Computational Linguistics Saarland University Saarbruecken Germany dshen@ Dietrich Klakow Spoken Language Systems Saarland University Saarbruecken Germany klakow@ Abstract In this paper we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. 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 training. Experimental results show that our method significantly outperforms state-of-the-art syntactic relation-based methods by up to 2o 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 using 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 following steps Recognize expected answer type of a question. Annotate relevant sentences with various types of named entities. Regard the phrases annotated with the expected answer type as candidate answers. Rank candidate answers. In the above work flow answer extraction heavily 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 answer extraction performance. To our knowledge most top ranked QA systems in TREC are supported by effective NER .

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