Thc p」mary obiectiVe Of the track is to accomplish a mechanism to answcr entity related scarches over、veb data The TREC orgttzers prov■ ded a segmcnt of web data le ClucWeb09 data collection which is uscd for this work An example ofthe cntity rclatcd query is``ur7friS οrtt fhan′αメand ra И′′ブc々 ′odandair′′′es2' To answer such qucries,it needs twcl phases of processing, onc is omine processing an...

Entity Linking is the task of mapping mentions in documents to entities in a knowledge base. One of the crucial tasks is to identify the disambiguating context of the mention, and joint assignment models leverage the relationships within the knowledge base. We demonstrate how joint assignment models can be approximated with information retrieval. We build on pseudo-relevance feedback and use th...

We present an experimental study of the performance of a hybrid semantic and linguistic system for recognizing and linking entities from formal and informal texts. In the current literature, systems are generally tailored to one or a few types of textual documents (e.g. narrative texts, newswire articles, informal text such as microposts). In contrast, we assess the performance of a hybrid appr...

Linked Data is developing towards a large, global repository for structured, interlinked descriptions of real-world entities. An emerging problem in many Web applications making use of data like Linked Data is how a lengthy description can be tailored to the task of quickly identifying the underlying entity. As a solution to this novel problem of entity summarization, we propose RELIN, a varian...

We propose to base the development of vector-space models of semantics on concept extensions, which defines concepts to be sets of entities. We investigate two sources of knowledge about entities that could be relevant: distributional information provided by word or phrase embeddings, and ontological information derived from a knowledge base. We develop a feedforward neural network architecture...

The path ranking algorithm (PRA) has been recently proposed to address relational classification and retrieval tasks at large scale. We describe Cor-PRA, an enhanced system that can model a larger space of relational rules, including longer relational rules and a class of first order rules with constants, while maintaining scalability. We describe and test faster algorithms for searching for th...

In this paper we present a system which uses ontological resources and a gene name variation generation tool to expand concepts in the original query. The novelty of our approach lies in our concept-based normalization ranking model. For the 2007 Genomic task, we also modified this system architecture with an additional dynamic form of query expansion called entity-based relevance feedback. Thi...

Chemical patents contain detailed information on novel chemical compounds that is valuable to the chemical and pharmaceutical industries. In this paper, we introduce a system, NERChem that can recognize chemical named entity mentions in chemical patents. NERChem is based on the conditional random fields model (CRF). Our approach incorporates (1) class composition, which is used for combining ch...

Open-domain Question Answering (QA) systems typically leverage an answer selection component to rank candidate answer sentences based on how likely they will contain the answer to a given question. This component plays a crucial rule in the QA system as it usually dictates how downstream processing modules (e.g., answer extraction) retain and present answers to users. Most existing works in thi...

We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities. Our system design abstracts core tasks of most neural entity linking systems for MonetDB. To the best of our knowledge, this is the first defacto implemented system integrating entity-linking in a database. We leverage the ability of ...