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Edgar Meij

semantic search research ッ

  • Publications
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    • Workshop Papers
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    • Theses
    • Submitted
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RepLab 2014

23/02/2014 Blog No Comments

RepLab is a competitive evaluation exercise for Online Reputation Management systems. In 2012 and 2013, RepLab focused on the problem of monitoring the reputation of (company) entities on Twitter, and dealt with the tasks of entity linking (“Is the tweet about the entity?”), reputation polarity (“Does the tweet have positive or negative implications for the entity’s reputation?”), topic detection (“What is the issue relative to the entity that is discussed in the tweet?”), and topic ranking (“Is the topic an alert that deserves immediate attention?”).

RepLab 2014 will again focus on Reputation Management on Twitter and will be addressing two new tasks, see below. We will use tweets in two languages: English and Spanish.

  1. The classification of tweets with respect to standard reputation dimensions such as Performance, Leadership, Innovation, etc.
  2. The classification of Twitter profiles (authors) with respect to a certain domain, classifying them as journalists, professionals, etc. Second, this task focuses on finding the opinion makers.

The second task is a part of the shared PAN-RepLab author profiling task. Besides the characterization of profiles from a reputation analysis perspective, participants can also attempt to classify authors by gender and age, which is the focus of PAN 2014.

Important dates:

  • March 1 – Training data released
  • March 17 – Test data released
  • May 5 – System results due

See http://nlp.uned.es/replab2014/ for more info and how to participate.

CLEFedgar-meijedgar-rameijEntity linkingmeijmeij-inPANRepLabreplab-2013-proceedingsreplab-2014replab-2015semalt-expertTwitter

Using Temporal Bursts for Query Modeling

Entity Linking and Retrieval for Semantic Search (WSDM 2014)

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This is the website of Edgar Meij. I lead several groups of researchers and engineers at Bloomberg working on knowledge graphs, question answering, information retrieval, machine learning, and more…

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