We present an open-domain Question-Answering system that learns to answer questions based on successful past interactions. We follow a pattern-based approach to Answer-Extraction, where (lexico-syntactic) patterns that relate a question to its answer are automatically learned and used to answer future questions. Results show that our approach contributes to the system's best performance when it is conjugated with typical Answer-Extraction strategies. Moreover, it allows the system to learn with the answered questions and to rectify wrong or unsolved past questions.
The focus has turned to automatic pattern learning, with many approaches taking advantage of the large amount of information on the Web. For example, @cite_0 and @cite_1 learn surface patterns for a particular relation. Javelin @cite_2 adds flexibility to the learned patterns by allowing, for instance, certain terms to be generalized into named entities. In all the previous cases, seeds are sets of two (manually chosen) entities. On the contrary, Ephyra system @cite_6 takes as input questions and answers, and learns patterns between the answer and two or more key-phrases. However, these are extracted from the question by hand-made patterns. Previous approaches learn lexical patterns; others learn patterns also conveying syntactic information in the form of dependency relations @cite_9 @cite_7 . Particularly, Shen al define a pattern as the smallest dependency tree that conveys the answer and one question term.
@cite_7 @cite_9 @cite_1 @cite_6 @cite_0 @cite_2
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One of the most accurate methods in Question Answering (QA) uses off-line information extraction to find answers for frequently asked questions. It requires automatic extraction from text of all relation instances for relations that users frequently ask for. In this chapter, two methods are presented for learning relation instances for relations relevant in a closed and open domain (medical) QA system. Both methods try to learn automatic dependency paths that typically connect two arguments of a given relation. The first (lightly supervised) method starts from a seed list of argument instances, and extracts dependency paths from all sentences in which a seed pair occurs. This method works well for large text collections and for seeds which are easily identified, such as named entities, and is well-suited for open domain QA. A second experiment concentrates on medical relation extraction for the question answering module of the IMIX system. The IMIX corpus is relatively small and relation instances may contain complex noun phrases that do not occur frequently in the exact same form in the corpus. In this case, learning from annotated data is necessary. Dependency patterns enriched with semantic concept labels are shown to give accurate results for relations that are relevant for a medical QA system. Both methods improve the performance of the Dutch QA system Joost. In this paper, we explore the syntactic relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from syntactic trees. We further propose a QA-specific tree kernel to partially match the syntactic relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the syntactic relation patterns help to improve the performance by 6.91 MRR based on the common features. We describe herein a Web based pattern mining and matching approach to question answering. For each type of questions, a lot of textual patterns can be learned from the Web automatically, using the TREC QA track data as training examples. These textual patterns are assessed by the concepts of support and confidence, which are borrowed from the data mining community. Given a new unseen question, these textual patterns can be utilized to extract and rank the plausible answers on the Web. The performance of this approach has been evaluated also by the TREC QA track data. This paper describes the Ephyra question answering engine, a modular and extensible framework that allows to integrate multiple approaches to question answering in one system Our framework can be adapted to languages other than English by replacing language-specific components It supports the two major approaches to question answering, knowledge annotation and knowledge mining Ephyra uses the web as a data resource, but could also work with smaller corpora In addition, we propose a novel approach to question interpretation which abstracts from the original formulation of the question Text patterns are used to interpret a question and to extract answers from text snippets Our system automatically learns the patterns for answer extraction, using question-answer pairs as training data Experimental results revealed the potential of this approach. In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions. Using the TREC-10 question set, we report results for two cases: answers determined from the TREC-10 corpus and from the web. We describe Javelin, a Cross-lingual Question Answering system which participated in the NTCIR-8 ACLIA evaluation and which is designed to work on any type of question, including factoid and complex questions. The key technical contribution of this paper is a minimally supervised bootstrapping approach to generating lexicosyntactic patterns used for answer extraction. The preliminary evaluation result (measured by nugget F3 score) shows that the proposed pattern learning approach outperformed two baselines, a supervised learning approach used in NTCIR-7 ACLIA and a simple key-term based approach, for both monolingual and crosslingual tracks. The proposed approach is general and thus it has potential applicability to a wide variety of information access applications which require deeper semantic processing.