Abstract. This paper describes the IRIT lab participation to the Vital Filtering task (also known as Cumulative Citation Recommendation) of the TREC 2014 Knowledge Base Acceleration Track. This task aims at identifying vital documents containing timely new information that should help a human

Abstract. This report summarizes our participation at KBA-CCR track in TREC 2014. Our submissions are generated in two steps: (1) Filtering a candidate documents collection from the stream corpus for a set of tar-get entities; and (2) Estimating the relevance levels between candidate documents

This paper describes the WHU IRLAB participation to the Vital Filtering task of the TREC 2014 Knowledge Base Acceleration Track. In this task, we implemented a system to detect vital documents that could be used for a human editor to update or create the profile of an entity. Our approach

Identifying documents that contain timely and vi-tal information for an entity of interest, a task known as vital filtering, has become increasingly important with the availability of large document collections. To efficiently filter such large text corpora in a streaming manner, we need to com-pactly represent previously observed entity con-texts, and quickly estimate whether a new doc-ument contains novel information. Existing ap-proaches to modeling contexts, such as bag of words, latent semantic indexing, and topic mod-els, are limited in several respects: they are un-able to handle streaming data, do not model the underlying topic of each document, suffer from lexical sparsity, and/or do not accurately estimate temporal vitalness. In this paper, we introduce a word embedding-based non-parametric repre-sentation of entities that addresses the above limi-tations. The word embeddings provide accurate and compact summaries of observed entity con-texts, further described by topic clusters that are estimated in a non-parametric manner. Addition-ally, we associate a staleness measure with each entity and topic cluster, dynamically estimating their temporal relevance. This approach of using word embeddings, non-parametric clustering, and staleness provides an efficient yet appropriate rep-resentation of entity contexts for the streaming setting, enabling accurate vital filtering. 1.

This paper serves as an introduction to the research described in detail in the remainder of the volume. The next section defines the common retrieval tasks performed in TREC-6. Sections 3 and 4 provide details regarding the test collections and the evaluation methodology used in TREC. Section 5

Abstract not found

This paper presents the University of Amsterdam’s partici-pation in TREC 2014. For the Contextual Suggestion Track, we experimented with the use of anchor text representations in the language modeling framework, and base our runs ei-ther on full ClueWeb12 or the subset of touristic aggregators (e

1.1 Summary for Previous Participants For participants familiar with the 2012 Contextual Suggestion Track we have provided a list of the main changes to this year’s track: • Contexts no longer include a temporal component (day of week, time of day, and season), contexts

participated in TREC’s Federated Web (FedWeb) and Knowledge Base Acceleration (KBA) tracks in 2014. Specifically, we submitted runs for the FedWeb resource selection and KBA

Unité de recherche: Institut de recherche en Informatique de Toulouse (IRIT) tel-00609903, version 1- 20 Jul 2011 Seeing, knowing, doing Case studies in modal logic