Variations on language modeling for information retrieval

Kraaij, Wessel

doi:10.1145/1067268.1067291

Cited by 40 publications

(37 citation statements)

References 175 publications

(116 reference statements)

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“…The temporal language models assign a probability to a document according to word usage statistics over time. The JRH approach employs a normalized log-likelihood ratio (NLLR) [70] for computing the similarity between two language models. Given a partitioned corpus, it is possible to determine the time of a non-timestamped document d i by comparing the language model of d i with each corpus partition p j using the following equation:…”

Section: Temporal Language Modelsmentioning

confidence: 99%

Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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Section: Temporal Language Modelsmentioning

confidence: 99%

Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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“…The entropy of the query, P t2V Pðtjh Q Þ log Pðtjh Q Þ, is a query specific constant and can thus be ignored for ranking purposes. In fact, one could argue that ranking on just the cross-entropy term provides a more concise ranking formula and is a suitable distance measure for comparing probability distributions in its own right (Kraaij, 2004). When the query model is generated using the empirical, maximum-likelihood estimate (MLE) on the original query, i.e.,…”

Section: The Kl-divergence Retrieval Frameworkmentioning

confidence: 99%

Conceptual language models for domain-specific retrieval

Meij¹,

Trieschnigg

Rijke³

et al. 2010

Information Processing & Management

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a b s t r a c tOver the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model.Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.

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“…A normalized log-likelihood ratio [3] is used to compute the similarity between two language models. Given a partitioned corpus, it is possible to determine the timestamp of a non-timestamped document d i by comparing the language model of d i with each corpus partition p j using the following equation:…”

Section: Temporal Language Modelsmentioning

confidence: 99%

Using Temporal Language Models for Document Dating

Kanhabua

Nørvåg

2009

Machine Learning and Knowledge Discovery in Databases

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Abstract. In order to increase precision in searching for web pages or web documents, taking the temporal dimension into account is gaining increased interest. A particular problem for web documents found on the Internet is that in general, no trustworthy timestamp is available. This is due to its decentralized nature and the lack of standards for time and date. In previous work we have presented techniques for solving this problem. In this paper, we present a tool for determining the timestamp of a non-timestamped document (using file, URL or text as input) using temporal language models. We also outline how this tool will be demonstrated.

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Variations on language modeling for information retrieval

Cited by 40 publications

References 175 publications

Time-aware approaches to information retrieval

Time-aware approaches to information retrieval

Conceptual language models for domain-specific retrieval

Using Temporal Language Models for Document Dating

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