A New Approach to Query Segmentation for Relevance Ranking in Web Search

Abstract In this paper, we try to determine how best to improve state-ofthe-art methods for relevance ranking in web searching by query segmentation. Query segmentation is meant to separate the input query into segments, typically natural language phrases. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create the top k candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the results of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely n-gram BM25, key n-gram model and term dependency model, within the framework of learning to rank. Our experimental results on large scale web search datasets show that our method can indeed significantly improve relevance ranking in all three cases

Dual frames: a new tool for semantic parsing

peer reviewedThis publication was presenting another key aspect of the automatic analyser of French SABA: the introduction of “dual frames” as a new knowledge representation tool for expressing semantic dependencies between concepts. Understanding natural language sentences requires to relate the concepts and relations expressed by them to the knowledge representation of a world model (or domain model), which is typically a kind of ontology, organizing concepts in hierarchical structures enriched with additional semantic relations. Dual frames were descriptions of the different types of semantic relationships that could be established between concepts occurring in a sentence. They could be assigned to all meaningful elements of a sentence and could be derived from more generic concepts by using ontology-based inheritance, and could be combined using intersection and union operations, to infer the semantic dependencies of complex sentence structures such as noun phrases and subordinate clauses. By matching the frames of a main term and its complements, semantic dependencies could be identified without syntactic analysis of the sentence, even in the presence of syntactic mistakes, thus making possible a robust natural language analysis