Our goal in participating in the TREC 2009 Entity Track was to study whether relation extraction techniques can help in improving accuracy of the entity finding task. Finding related entities is informational in nature and we wanted to explore if inducing structure on the queries helps satisfy this information need. The research outlook we took was to study techniques that retrieve relations be...

Modern search engines are increasingly aiming to understand users’ intent in order to answer information needs more effectively by providing richer information than the traditional “ten blue links”. This information might include context about the entities present in the query, direct answers to questions that concern entities and more. A recent trend when answering queries that refer to a sing...

With the increasing use of entities in serving people’s daily information needs, recognizing synonyms—different ways people refer to the same entity—has become a crucial task for many entity–leveraging applications. Previous works often take a “literal” view of the entity, i.e., its string name. In this work, we propose adopting a “structured” view of each entity by considering not only its str...

Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salienc...

Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases ...

Traditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the pre-statistical NLP era in terms of sophistication. Two ind...

Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases ...

Retrieving Proper Names (PNs) relevant to an audio document can improve speech recognition and content based audio-video indexing. Latent Dirichlet Allocation (LDA) topic model has been used to retrieve Out-Of-Vocabulary (OOV) PNs relevant to an audio document with good recall rates. However, retrieval of OOV PNs using LDA is affected by two issues, which we study in this paper: (1) Word Freque...

The World Wide Web has transcended from a read-only to a read-write web. The problem of identifying important online or real life events from large textual document streams that are freely available on the World Wide Web is increasingly gaining popularity, given the flourishing of the social web. Earlier work used efficient algorithm for detecting all important events from a document stream thr...

Our strategy for TREC KBA CCR track is to first retrieve as many vital or documents as possible and then apply more sophisticated classification and ranking methods to differentiate vital from useful documents. We submitted 10 runs generated by 3 approaches: question expansion, classification and learning to rank. Query expansion is an unsupervised baseline, in which we combine entities’ names ...