Yuval Pinter

Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis

Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on community question answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.

Identifying Web Queries with Question Intent

Vertical selection is the task of predicting relevant verticals for a Web query so as to enrich the Web search results with complementary vertical results. We investigate a novel variant of this task, where the goal is to detect queries with a question intent. Specifically, we address queries for which the user would like an answer with a human touch. We call these CQA-intent queries, since answers to them are typically found in community question answering (CQA) sites. A typical approach in vertical selection is using a verticals specific language model of relevant queries and computing the query-likelihood for each vertical as a selective criterion. This works quite well for many domains like Shopping, Local and Travel. Yet, we claim that queries with CQA intent are harder to distinguish by modeling content alone, since they cover many different topics. We propose to also take the structure of queries into consideration, reasoning that queries with question intent have quite a different structure than other queries. We present a supervised classification scheme, random forest over word-clusters for variable length texts, which can model the query structure. Our experiments show that it substantially improves classification performance in the CQA-intent selection task compared to content-oriented based classification, especially as query length grows.

Syntactic Parsing of Web Queries with Question Intent

Accurate automatic processing of Web queries is important for high-quality information retrieval from the Web. While the syntactic structure of a large portion of these queries is trivial, the structure of queries with question intent is much richer. In this paper we therefore address the task of statistical syntactic parsing of such queries. We first show that the standard dependency grammar does not account for the full range of syntactic structures manifested by queries with question intent. To alleviate this issue we extend the dependency grammar to account for segments – independent syntactic units within a potentially larger syntactic structure. We then propose two distant supervision approaches for the task. Both algorithms do not require manually parsed queries for training. Instead, they are trained on millions of (query, page title) pairs from the Community Question Answering (CQA) domain, where the CQA page was clicked by the user who initiated the query in a search engine. Experiments on a new treebank consisting of 5,000 Web queries from the CQA domain, manually parsed using the proposed grammar, show that our algorithms outperform alternative approaches trained on various sources: tens of thousands of manually parsed OntoNotes sentences, millions of unlabeled CQA queries and thousands of manually segmented CQA queries.