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About

Ricardo Campos is an assistant professor at the ICT Departmental Unit of the Polytechnic Institute of Tomar and member of LIAAD-INESC TEC, the Artificial Intelligence and Decision Support Lab of U. Porto

He is PhD in Computer Science by the University of Porto (U. Porto), MSc. on Computer Science Engineering by the University of Beira Interior (UBI) and BSc. in Maths and Computer Science, currently Computer Science (UBI).

His work on temporal information retrieval led him to won the Fraunhofer Portugal Challenge 2013 and to be distinguished as an “outstanding” researcher by the INESC TEC research lab.

His research interests are Information Retrieval, Web Mining and Natural Language Processing. In particular, he focus on how temporal features impact the improvement of web search systems.

He has published research in the fields of Information Retrieval and has been guest to participate in some research projects having being awarded the best short paper award at ECIR'18 (40th International Conference on Information Retrieval) for the paper entitled "A Text Feature Based Automatic Keyword Extraction Method for Single Documents".

In 2018 he has also been awarded the 1st prize of the Arquivo.pt Award for his project Conta-me Histórias (http://contamehistorias.pt)

He is an editorial board member of the Information Processing & Management Journal (Elsevier), co-chaired international conferences and workshops in Information Retrieval, being also a program committee member of several international conferences.

For comprehensive access to his works, please refer to his website at http://www.ccc.ipt.pt/~ricardo

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Details

Details

Publications

2018

A Text Feature Based Automatic Keyword Extraction Method for Single Documents

Authors
Campos, R; Mangaravite, V; Pasquali, A; Jorge, AM; Nunes, C; Jatowt, A;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2018

YAKE! Collection-Independent Automatic Keyword Extractor

Authors
Campos, R; Mangaravite, V; Pasquali, A; Jorge, AM; Nunes, C; Jatowt, A;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2017

Identifying top relevant dates for implicit time sensitive queries

Authors
Campos, R; Dias, G; Jorge, AM; Nunes, C;

Publication
Inf. Retr. Journal

Abstract
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections. © 2017 Springer Science+Business Media New York

2017

Detecting Seasonal Queries Using Time Series and Content Features

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

Publication
Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2017, Amsterdam, The Netherlands, October 1-4, 2017

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).

2016

GTE-Rank: A time-aware search engine to answer time-sensitive queries

Authors
Campos, R; Dias, G; Jorge, A; Nunes, C;

Publication
INFORMATION PROCESSING & MANAGEMENT

Abstract
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as "Philip Seymour Hoffman" where the results may require no recency at all. In this work, we focus on this type of queries named "time-sensitive queries" where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.

2015

Survey of Temporal Information Retrieval and Related Applications

Authors
Campos, R; Dias, G; Jorge, AM; Jatowt, A;

Publication
ACM COMPUTING SURVEYS

Abstract
Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped.

2014

GTE-Rank: Searching for Implicit Temporal Query Results

Authors
Campos, R; Dias, G; Jorge, AM; Nunes, C;

Publication
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014

Abstract

2014

GTE-Rank

Authors
Campos, R; Dias, G; Jorge, AM; Nunes, C;

Publication
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14

Abstract