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Publicações

Publicações por Ricardo Campos

2016

Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016), Padua, Italy, March 20, 2016

Autores
Alvarez, MM; Kruschwitz, U; Kazai, G; Hopfgartner, F; Corney, D; Campos, R; Albakour, D;

Publicação
NewsIR@ECIR

Abstract

2018

Proceedings of the First Workshop on Narrative Extraction From Text (Text2Story 2018) co-located with 40th European Conference on Information Retrieval (ECIR 2018), Grenoble, France, March 26, 2018

Autores
Jorge, AM; Campos, R; Jatowt, A; Nunes, S;

Publicação
Text2Story@ECIR

Abstract

2017

Detecting Seasonal Queries Using Time Series and Content Features

Autores
Mansouri, B; Zahedi, MS; Rahgozar, M; Campos, R;

Publicação
ICTIR'17: PROCEEDINGS OF THE 2017 ACM SIGIR INTERNATIONAL CONFERENCE THEORY OF INFORMATION RETRIEVAL

Abstract
Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries "Golden Globe Award", "September 11th" or "Halloween", which refer to seasonal events that occur or have occurred at a specific occasion and for which, people often search in a planned and cyclic manner. Understanding this seasonal behavior, may help search engines to provide better ranking approaches and to respond with temporally relevant results leading into user's satisfaction. Detecting the diverse types of seasonal queries is therefore a key step for any search engine looking to present accurate results. In this paper, we categorize web search queries by their seasonality into 4 different categories: Non-Seasonal (NS, e.g., "Secure passwords"), Seasonal-related to ongoing events (SOE, "Golden Globe Award"), Seasonal-related to historical events (SHE, e.g., "September 11th") and Seasonal-related to special days and traditions (SSD, e.g., "Halloween"). To classify a given query we extract both time series (using the document publish date) and content features from its relevant documents. A Random Forest classifier is then used to classify web queries by their seasonality. Our experimental results show that they can be categorized with high accuracy. © 2017 Copyright held by the owner/author(s).

2013

Disambiguating implicit temporal queries for temporal information retrieval applications

Autores
Campos, R;

Publicação
SIGIR Forum

Abstract

2018

ParsTime: Rule-Based Extraction and Normalization of Persian Temporal Expressions

Autores
Mansouri, B; Zahedi, MS; Campos, R; Farhoodi, M; Rahgozar, M;

Publicação
ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018)

Abstract
Extraction and normalization of temporal expressions are essential for many NLP tasks. While a considerable effort has been put on this task over the last few years, most of the research has been conducted on the English domain, and only a few works have been developed on other languages. In this paper, we present ParsTime, a tagger for temporal expressions in Persian (Farsi) documents. ParsTime is a rule-based system that extracts and normalizes Persian temporal expressions according to the TIMEX3 annotation standard. Our experimental results show that ParsTime can identify temporal expressions in Persian texts with an F1-score 0.89. As an additional contribution we make available our code to the research community.

2018

First International Workshop on Narrative Extraction from Texts: Text2Story 2018

Autores
Jorge, AM; Campos, R; Jatowt, A; Nunes, S;

Publicação
ADVANCES IN INFORMATION RETRIEVAL (ECIR 2018)

Abstract

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