Dynamic Collective Entity Representations for Entity Ranking

Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity’s description in a knowledge base, and the way people refer to the entity when searching for it. To counter this issue we propose a method for constructing dynamic collective entity representations. We collect entity descriptions from a variety of sources and combine them into a single entity representation by learning to weight the content from different sources that are associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time and learn the best representation as time evolves so as to capture the dynamics of how people search entities. Incorporating dynamic description sources into dynamic collective entity representations improves retrieval effectiveness by 7% over a state-of-the-art learning to rank baseline. Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations.

  • [PDF] D. Graus, M. Tsagkias, W. Weerkamp, E. Meij, and M. de Rijke, “Dynamic collective entity representations for entity ranking,” in Proceedings of the ninth acm international conference on web search and data mining, 2016.
    Author = {Graus, David and Tsagkias, Manos and Weerkamp, Wouter and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {Proceedings of the ninth ACM international conference on Web search and data mining},
    Date-Added = {2016-01-07 17:24:16 +0000},
    Date-Modified = {2016-01-07 17:25:55 +0000},
    Series = {WSDM 2016},
    Title = {Dynamic Collective Entity Representations for Entity Ranking},
    Year = {2016},
    Bdsk-Url-1 = {}}
CIKM 2014

Time-Aware Rank Aggregation for Microblog Search

We tackle the problem of searching microblog posts and frame it as a rank aggregation problem where we merge result lists generated by separate rankers so as to produce a final ranking to be returned to the user. We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor modeling. It is time-aware and rewards posts that are published in or near a burst of posts that are ranked highly in many of the lists being aggregated. Our experimental results show that it significantly outperforms state-of-the-art rank aggregation and time-sensitive microblog search algorithms.

RepLab 2014

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 for more info and how to participate.

Example entity linking for tweets, to support tweets summarization

Personalized Time-Aware Tweets Summarization

To appear as full paper at SIGIR 2013.

In this paper we focus on selecting meaningful tweets given a user’s interests. Specifically, we consider the task of time-aware tweets summarization, based on a user’s history and collaborative social influences from “social circles.” Continue reading “Personalized Time-Aware Tweets Summarization” »

Overview of RepLab 2012: Evaluating Online Reputation Management Systems

This paper summarizes the goals, organization and results of the first RepLab competitive evaluation campaign for Online Reputation Management Systems (RepLab 2012). RepLab focused on the reputation of companies, and asked participant systems to annotate different types of information on tweets containing the names of several companies. Two tasks were proposed: a pro ling task, where tweets had to be annotated for relevance and polarity for reputation, and a monitoring task, where tweets had to be clustered thematically and clusters had to be ordered by priority (for reputation management purposes). The gold standard consisted of annotations made by reputation management experts, a feature which turns the RepLab 2012 test collection in a useful source not only to evaluate systems, but also to reach a better understanding of the notions of polarity and priority in the context of reputation management.

  • [PDF] E. Amigó, A. Corujo, J. Gonzalo, E. Meij, and M. de Rijke, “Overview of RepLab 2012: evaluating online reputation management systems,” in Clef (online working notes/labs/workshop), 2012.
    Author = {Enrique Amig{\'o} and Adolfo Corujo and Julio Gonzalo and Edgar Meij and Maarten de Rijke},
    Booktitle = {CLEF (Online Working Notes/Labs/Workshop)},
    Date-Added = {2012-09-20 12:48:33 +0000},
    Date-Modified = {2012-10-30 09:30:49 +0000},
    Title = {Overview of {RepLab} 2012: Evaluating Online Reputation Management Systems},
    Year = {2012}}
Twitter aspects

Identifying Entity Aspects in Microblog Posts

Online reputation management is about monitoring and handling the public image of entities (such as companies) on the Web. An important task in this area is identifying aspects of the entity of interest (such as products, services, competitors, key people, etc.) given a stream of microblog posts referring to the entity. In this paper we compare different IR techniques and opinion target identification methods for automatically identifying aspects and find that (i) simple statistical method such as TF.IDF are a strong baseline for the task, being significantly better than applying opinion-oriented methods and (ii) only considering terms tagged as nouns improves the results for all the methods analyzed.

More information on the dataset that we created (and used in this paper) can be found here.

  • [PDF] D. Spina, E. Meij, M. de Rijke, A. Oghina, B. M. Thuong, and M. Breuss, “Identifying entity aspects in microblog posts,” in The 35th international acm sigir conference on research and development in information retrieval, 2012.
    Author = {Damiano Spina and Meij, Edgar and de Rijke, Maarten and Andrei Oghina and Bui Minh Thuong and Mathias Breuss},
    Booktitle = {The 35th International ACM SIGIR conference on research and development in Information Retrieval},
    Date-Added = {2012-05-03 22:17:17 +0200},
    Date-Modified = {2012-10-30 08:40:47 +0000},
    Series = {SIGIR 2012},
    Title = {Identifying Entity Aspects in Microblog Posts},
    Year = {2012}}
Twitter aspects

A Corpus for Entity Profiling in Microblog Posts

Microblogs have become an invaluable source of information for the purpose of online reputation management. An emerging problem in the field of online reputation management consists of identifying the key aspects of an entity commented in microblog posts. Streams of microblogs are of great value because of their direct and real-time nature and synthesizing them in form of entity profiles facilitates reputation managers to keep a track of the public image of the entity. Determining such aspects can be non-trivial because of creative language usage, the highly contextualized and informal nature of microblog posts, and the limited length of this form of communication.

In this paper we present two manually annotated corpora to evaluate the task of identifying aspects on Twitter, both of them based upon the WePS-3 ORM task dataset and made available online. The first is created using a pooling methodology, for which we have implemented various methods for automatically extracting aspects from tweets that are relevant for an entity. Human assessors have labeled each of the candidates as being relevant. The second corpus is more fine-grained and contains opinion targets. Here, annotators consider individual tweets related to an entity and manually identify whether the tweet is opinionated and, if so, which part of the tweet is subjective and what the target of the sentiment is, if any.

You can find more information on this test collection at

  • [PDF] D. Spina, E. Meij, A. Oghina, B. M. Thuong, M. Breuss, and M. de Rijke, “A corpus for entity profiling in microblog posts,” in Lrec 2012 workshop on language engineering for online reputation management, 2012.
    Author = {Damiano Spina and Edgar Meij and Andrei Oghina and Bui Minh Thuong and Mathias Breuss and Maarten de Rijke},
    Booktitle = {LREC 2012 Workshop on Language Engineering for Online Reputation Management},
    Date-Added = {2012-03-29 12:18:51 +0200},
    Date-Modified = {2012-03-29 12:20:09 +0200},
    Title = {A Corpus for Entity Profiling in Microblog Posts},
    Year = {2012}}
Research on Twitter

Adding Semantics to Microblog Posts

Microblogs have become an important source of information for marketing, intelligence, and reputation management purposes. Streams of microblogs are of great value because of their direct and real-time nature. Determining what an individual microblog post is about, however, can be non-trivial because of creative language usage, the highly contextualized and informal nature of microblog posts, and the limited length of this form of communication.

We propose a solution to the problem of determining what a microblog post is about through semantic linking: we add semantics to posts by automatically identifying concepts that are semantically related to it and generating links to the corresponding Wikipedia articles. The identified concepts can subsequently be used for, e.g., social media mining, thereby reducing the need for manual inspection and selection. Using a purpose-built test collection of tweets, we show that recently proposed approaches for semantically linking do not perform well, mainly due to the idiosyncratic nature of microblog posts. We propose a novel method based on machine learning with a set of innovative features and show that is able to achieve significant improvements over all other methods, especially in terms of precision.

  • [PDF] E. Meij, W. Weerkamp, and M. de Rijke, “Adding semantics to microblog posts,” in Proceedings of the fifth acm international conference on web search and data mining, 2012.
    Author = {Meij, Edgar and Weerkamp, Wouter and de Rijke, Maarten},
    Booktitle = {Proceedings of the fifth ACM international conference on Web search and data mining},
    Date-Added = {2015-01-20 20:28:31 +0000},
    Date-Modified = {2015-01-20 20:28:31 +0000},
    Series = {WSDM 2012},
    Title = {Adding Semantics to Microblog Posts},
    Year = {2012},
    Bdsk-Url-1 = {}}