DOCTORAL ABSTRACT

Disambiguating Implicit Temporal Queries for
Temporal Information Retrieval Applications
Ricardo Campos
Polytechnic Institute of TomarPortugal
LIAAD–INESC TEC
rncampos@inescporto.pt
Time is an important dimension of the information retrieval area that can be very useful in
helping to meet the users information needs whenever they include temporal intents. However
retrieving the information that meets the query demands is not an easy process. The ambiguityof the query is traditionally one of the causes impeding the retrieval of relevant information.
This is particularly evident in the case of temporal queries where users tend to be subjective
when expressing their intents (e.g., avatar movie instead of avatar movie 2009). Determining thepossible times of the query is therefore of the utmost importance when attempting to achieve
better disambiguated results and in order to enable new forms of exploring them.
In this thesis, we present our contributions to disambiguate implicit temporal queries in realworld environment, i.e. the Web. To understand better this type of queries, three directions may
be followed: information extracted from (1) metadata, (2) query logs or (3) document contents.
Within the context of this thesis, we will focus on the latter. However, unlike existing approaches
we do not resort to a classification methodology. Instead, in our approach, we seek to detect relevant temporal expressions based on corpus statistics and a general similarity measure that makes
use of co-occurrences of words and years extracted from the contents of the documents. Moreover,
our methodology tends to be mostly language-independent as we do not use any linguistic-based
techniques. Instead, we use a rule-based model solution supported by regular expressions.
Based on this, we start by performing a comprehensive study of the temporal value of web
documents, particularly web snippets, showing that this type of collection is a valuable data
source in the process of dating implicit temporal queries. We then develop two methods. A
temporal similarity measure to evaluate the correlation between the query and the candidate dates
identified, called Generic Temporal Evaluation (GTE) and a threshold-based classifier that selects
the most relevant dates while filtering out the non-relevant or incorrect ones, known as GTEClass. Subsequently, we propose two different applications named GTE-Cluster and GTE-Rank.
The first one, uses the determined time of the queries to improve search results exploration. For
this purpose, we propose a flat temporal clustering model solution where documents are groupedat the year level. GTE-Rank, in turn, uses the same information to temporally re-rank the web
search results. We employ a combination approach that considers words and temporal scores,
where documents are ranked to reflect the relevance of the snippet for the query, both in theACM SIGIR Forum137

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conceptual and in the temporal dimension.
Through extensive experimental evaluation, we mean to demonstrate that our models offer
promising results in the field of Temporal Information Retrieval (T-IR), as demonstrated by theexperiments conducted over web corpora. As an additional contribution to the research community, we publicly provide a number of web services so that each of the different approaches canbe tested. Although the main motivation of our work is focused on queries with temporal nature,
the implemented prototypes allow the execution of any query including non-temporal ones. Finally, for future research direction, we study the behavior of web snippets in the context of Future
Information Retrieval (F-IR), a fairly recent topic which consists of extracting future temporal
information in order to answer user queries with a future temporal nature.
Available online at http://www.ccc.ipt.pt/~ricardo/PhDThesis_RCampos.pdf

ACM SIGIR Forum138

Vol. 47 No. 2 December 2013