Query Expansion with Temporal Segmented Texts

Craveiro, Olga; Macedo, Joaquim; Madeira, Henrique

doi:10.1007/978-3-319-06028-6_65

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Recently, modeling temporal relevance was shown to be effective for searching time-sensitive collections. Craveiro et al [7] explored the segmentation of textual news articles, so that it can be leveraged in the query expansion process to focus the expansion terms temporally. Efron et al [11] proposed a general and principled retrieval model for microblog search with temporal feedback.…”

Section: Related Workmentioning

confidence: 99%

Modeling Temporal Evidence from External Collections

Martins

Magalhães

Callan

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.

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Section: Related Workmentioning

confidence: 99%

Modeling Temporal Evidence from External Collections

Martins

Magalhães

Callan

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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“…Keikha et al [17] represented queries and documents with their normalized term frequencies in the time dimension and used a time-based similarity metric to measure relevance. Craveiro et al [3] exploited the temporal relationship between words for query expansion. Choi and Cro [2] presented a method to select time periods for expansion based on users' behaviors (i.e., retweets).…”

Section: Related Work 21 Temporal Information Retrievalmentioning

confidence: 99%

Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams

Rao,

He,

Zhang

et al. 2017

Preprint

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Time is an important relevance signal when searching streams of social media posts. e distribution of document timestamps from the results of an initial query can be leveraged to infer the distribution of relevant documents, which can then be used to rerank the initial results. Previous experiments have shown that kernel density estimation is a simple yet e ective implementation of this idea. is paper explores an alternative approach to mining temporal signals with recurrent neural networks. Our intuition is that neural networks provide a more expressive framework to capture the temporal coherence of neighboring documents in time. To our knowledge, we are the rst to integrate lexical and temporal signals in an end-to-end neural network architecture, in which existing neural ranking models are used to generate query-document similarity vectors that feed into a bidirectional LSTM layer for temporal modeling. Our results are mixed: existing neural models for document ranking alone yield limited improvements over simple baselines, but the integration of lexical and temporal signals yield signi cant improvements over competitive temporal baselines.

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“…There have been several other works that studied temporal query expansion [8,4,3,12]. Keikha et al [8] represented queries and documents with their normalized term frequencies in the time dimension and used a time-based similarity metric to measure relevance.…”

Section: Related Workmentioning

confidence: 99%

“…Keikha et al [8] represented queries and documents with their normalized term frequencies in the time dimension and used a time-based similarity metric to measure relevance. Craveiro et al [4] exploited the temporal relationship between words for query expansion. Choi et al [3] presented a method to select time periods for expansion based on users' behaviors (i.e., retweets).…”

Section: Related Workmentioning

confidence: 99%

Temporal Query Expansion Using a Continuous Hidden Markov Model

Rao

Lin

2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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In standard formulations of pseudo-relevance feedback, document timestamps do not play a role in identifying expansion terms. Yet we know that when searching social media posts such as tweets, relevant documents are bursty and usually occur in temporal clusters. The main insight of our work is that term expansions should be biased to draw from documents that occur in bursty temporal clusters. This is formally captured by a continuous hidden Markov model (cHMM), for which we derive an EM algorithm for parameter estimation. Given a query, we estimate the parameters for a cHMM that best explains the observed distribution of an initial set of retrieved documents, and then use Viterbi decoding to compute the most likely state sequence. In identifying expansion terms, we only select documents from bursty states. Experiments on test collections from the TREC 2011 and 2012 Microblog tracks show that our approach is significantly more effective than the popular RM3 pseudo-relevance feedback model.

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Query Expansion with Temporal Segmented Texts

Cited by 5 publications

References 5 publications

Modeling Temporal Evidence from External Collections

Modeling Temporal Evidence from External Collections

Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams

Temporal Query Expansion Using a Continuous Hidden Markov Model

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