Gaël Dias

Survey of Temporal Information Retrieval and Related Applications

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.

Query log driven web search results clustering

Different important studies in Web search results clustering have recently shown increasing performances motivated by the use of external resources. Following this trend, we present a new algorithm called Dual C-Means, which provides a theoretical background for clustering in different representation spaces. Its originality relies on the fact that external resources can drive the clustering process as well as the labeling task in a single step. To validate our hypotheses, a series of experiments are conducted over different standard datasets and in particular over a new dataset built from the TREC Web Track 2012 to take into account query logs information. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantages over traditional clustering and labeling techniques.

Enriching temporal query understanding through date identification: how to tag implicit temporal queries?

Generically, search engines fail to understand the users temporal intents when expressed as implicit temporal queries. This causes the retrieval of less relevant information and prevents users from being aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating a relevant date(s) to a given query and filtering out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We particularly focus on extracting years, which are a kind of temporal information that often appears in this type of collection. We evaluate our methodology using a set of real-world text temporal queries, which are clear concepts (i.e. queries which are non-ambiguous in concept and temporal in their purpose). Experiments show that when compared to baseline methods, determining the most relevant dates relating to any given implicit temporal query can be improved with a new temporal similarity measure.

Haptic Perception of Document Structure for Visually Impaired People on Handled Devices

The dissemination of information available through the World Wide Web makes universal access more and more important and supports visually impaired people (VIP) in their everyday life. To access this information, VIP use screen readers to extract the textual information, which is displayed on the screen. The extracted two-dimensional information is linearized and is either written in Braille on a special output device or presented by voice output. However, on the one hand, some studies showed that impaired populations prefer to use commonly available software and hardware solutions rather than dedicated specialized solutions. On the other hand, we demonstrated in previous research that the perception of the document structure plays an important role in its memorization. In this paper, we propose to automatically generate vibrating pages from document layout skeletons based on the transformation of light contrasts into low-frequency tactile vibrations. First experiments on digital tablets show promising results in a “Design-for-All” paradigm.

GTE-Cluster: A Temporal Search Interface for Implicit Temporal Queries

In this paper, we present GTE-Cluster an online temporal search interface which consistently allows searching for topics in a temporal perspective by clustering relevant temporal Web search results. GTE-Cluster is designed to improve user experience by augmenting document relevance with temporal relevance. The rationale is that offering the user a comprehensive temporal perspective of a topic is intuitively more informative than retrieving a result that only contains topical information. Our system does not pose any constraint in terms of language or domain, thus users can issue queries in any language ranging from business, cultural, political to musical perspective, to cite just a few. The ability to exploit this information in a temporal manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or temporal clustering.

GTE-Rank

We propose a novel temporal re-ranking algorithm.We devise and provide new datasets for time-sensitive evaluation purposes.We conduct comparative experiments (including algorithms with a temporal focus).We investigate the effectiveness of GRank by running a crowdsourcing experiment.We build a prototype system that can be tested by the research community. 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 users 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.

GTE-Rank: Searching for Implicit Temporal Query Results

Temporal information retrieval has been a topic of great interest in recent years. Despite the efforts that have been conducted so far, most popular search engines remain underdeveloped when it comes to explicitly considering the use of temporal information in their search process. In this paper we present GTE-Rank, an online searching tool that takes time into account when ranking time-sensitive query web search results. GTE-Rank is defined as a linear combination of topical and temporal scores to reflect the relevance of any web page both in topical and temporal dimensions. The resulting system can be explored graphically through a search interface made available for research purposes.

Easy Web Search Results Clustering: When Baselines Can Reach State-of-the-Art Algorithms

This work discusses the evaluation of baseline algorithms for Web search results clustering. An analysis is performed over frequently used baseline algorithms and standard datasets. Our work shows that competitive results can be obtained by either fine tuning or performing cascade clustering over well-known algorithms. In particular, the latter strategy can lead to a scalable and real-world solution, which evidences comparative results to recent text-based state-of-the-art algorithms.

QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts

This paper presents our participation to the SemEval Task-17, related to “Taxonomy Extraction Evaluation” (Bordea et al., 2015). We propose a new methodology for semi-supervised and auto-supervised acquisition of lexical taxonomies from raw texts. Our approach is based on the theory of pretopology that offers a powerful formalism to model subsumption relations and transforms a list of terms into a structured term space by combining different discriminant criteria. In order to reach a good pretopological space, we define the Learning Pretopological Spaces method that learns a parameterized space by using an evolutionary strategy.

An Empirical Study for Examining the Performance of Visually Impaired People in Recognizing Shapes through a Vibro-tactile Feedback

In this paper, we present results of an empirical study for examining the performance of blind individuals in recognizing shapes through a vibro-tactile feedback. The suggested vibro-tactile system maps different shades of grey to one pattern low-frequencies tactical vibrations. Performance data is reported, including number of errors, and qualitative understanding of the displayed shapes.