Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most s...

AnswerBus1 is an open-domain question answering system based on sentence level Web information retrieval. It accepts users’ natural-language questions in English, German, French, Spanish, Italian and Portuguese and provides answers in English. Five search engines and directories are used to retrieve Web pages that are relevant to user questions. From the Web pages, AnswerBus extracts sentences ...

In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval. It employs the word2vec model to...

The research presented in this paper focuses on the adoption of semantic models for Question Answering (QA) systems. We propose a framework which exploits semantic technologies to analyze the question, retrieve and rank relevant passages. It exploits: (a) Natural Language Processing algorithms for the analysis of questions and candidate answers both in English and Italian; (b) Information Retri...

The growing diagnosis and public awareness of Autism Spectrum Disorders (ASD) leads more parents to seek answers to their suspicions for ASD in their child on Internet forums. This study describes an analysis of the quality of content of 371 answers on Yahoo Answers (YA), a social questionand-answer forum, to parents querying whether their child has ASD. We contrasted the perceived quality of a...

Recommendation is the task of providing content that is likely to interest users and enrich their online experience. At Yahoo!, the variety of user-facing applications and the nature and volume of data raise multiple recommendation opportunities and challenges. In this paper, we provide a brief description of several projects within Yahoo! Labs, in the context of Web Search (Section 2), interpr...