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

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Campos, R (Author) Other; Dias, G (Author) Other; Jorge, AM (Author) FCUP; Nunes, C (Author) Other

Conference proceedings International

Title: ACM International Conference Proceeding Series

Pages: 41-48

2nd Temporal Web Analytics Workshop, TempWeb 2012

Lyon, 17 April 2012 through 17 April 2012

Indexing

Scopus - 10 Citations

Other information

Authenticus ID: P-008-4FS

DOI: 10.1145/2169095.2169103

Resumo (PT): Generically, search engines fail to understand the user's 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.

Abstract (EN): Generically, search engines fail to understand the user's 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. © 2012 ACM.

Language: English

Type (Professor's evaluation): Scientific