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Time segment language model for microblog retrieval

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

Related studies have shown that the time characteristics of microblog can improve retrieval performance. However, these researches mainly focus on the time distribution of tweets related to a given query. And this single time characteristics might not be sufficient to reflect time characteristics of microblog. Inspired by the recent success of time-based language models for microblog retrieval, this paper proposes a time segment language model (TSLM) to model the time characteristics of microblog. Briefly, TSLM constructs the language model of each time segment to model the probability distribution over sequences of words for each different time segment. Based on TSLM, the time distribution of terms (tDT), the time distribution of queries (tDQ) and the time distribution of documents (tDD) are proposed. Furthermore, TSLM is exploited to estimate the query model, the document model and compute the similarity between query and document. The experimental results on the Tweets2011 corpus show that the proposed approaches outperform several state-of-the-art baselines.

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Notes

  1. http://twittertools.cc/.

  2. The corpus contains about 16 million tweets, but some microblogs were not downloaded for the reasons such as having be deleted, hidden, and so on.

  3. http://nutch.apache.org/.

  4. http://www.lemurproject.org/indri/.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Foshan University, Foshan, 528231, China

    Zhong-yuan Han, Lei-lei Kong & Hao-liang Qi

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  1. Zhong-yuan Han
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  2. Lei-lei Kong
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Correspondence to Lei-lei Kong.

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This work is supported by National Social Science Fund of China (No. 18BYY125).

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Han, Zy., Kong, Ll. & Qi, Hl. Time segment language model for microblog retrieval. Neural Comput & Applic 33, 4763–4777 (2021). https://doi.org/10.1007/s00521-020-05534-x

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