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Towards Content Expiry Date Determination: Predicting Validity Periods of Sentences

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

Knowing how long text content will remain valid can be useful in many cases such as supporting the creation of documents to prolong their usefulness, improving document retrieval or enhancing credibility estimation. In this paper we introduce a novel research task of forecasting content’s validity period. Given an input sentence the task is to approximately determine until when the information stated in the content will remain valid. We propose machine learning approaches equipped with NLP and statistical features that can successfully work on a relatively small number of annotated data.

A. Almquist—This work was mainly done at the University of Gothenburg.

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Notes

  1. 1.

    http://www.timeml.org/tarsqi/modules/gutime/index.html.

  2. 2.

    https://nlp.stanford.edu/software/sutime.html.

  3. 3.

    https://github.com/HeidelTime/heideltime.

  4. 4.

    We assume that content is valid at its creation time.

  5. 5.

    https://en.wikipedia.org/wiki/Logarithmic_timeline.

  6. 6.

    In Experiments in Sec 5, we also test the case with the reduced set of three classes.

  7. 7.

    Determining the approximate expiry date requires then extending the actual creation time of a sentence with its predicted validity period.

  8. 8.

    https://radimrehurek.com/gensim/.

  9. 9.

    Text dump from 2018-05-01.

  10. 10.

    TIME, DATE and DURATION expressions often point to a specific point (or duration) in time which means that they can be used as explicit markers for when information ceases to be valid. However, SET expressions, such as “every day”, does not.

  11. 11.

    We used pre-trained word embeddings by Google created based on news: https://code.google.com/archive/p/word2vec/.

  12. 12.

    https://wordnet.princeton.edu/.

  13. 13.

    https://github.com/Ejhfast/empath-client.

  14. 14.

    http://commoncrawl.org/.

  15. 15.

    https://github.com/AxlAlm/ValidityPeriods-dataset.

  16. 16.

    http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm.

  17. 17.

    https://www.kaggle.com/snapcrack/all-the-news/data.

  18. 18.

    http://kopiwiki.dsd.sztaki.hu/.

  19. 19.

    https://www.nltk.org/.

  20. 20.

    https://stanfordnlp.github.io/CoreNLP/.

  21. 21.

    e.g. “i’m to “I” and “am”.

  22. 22.

    LSA, average word length, sentence length, POS-tags, Sentence embeddings, TempoWordNet.

  23. 23.

    For LinearSVC we use \(C=0.7\).

  24. 24.

    For RBF we use \(C=60\).

  25. 25.

    We use 150 trees.

  26. 26.

    Five neighbors are used together with distance weighting.

  27. 27.

    https://keras.io/.

  28. 28.

    http://scikit-learn.org/stable/.

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Acknowledgements

We thank Nina Tahmasebi for valuable comments and encouragement. This research has been supported by JSPS KAKENHI Grants (#17H01828, #18K19841) and by Microsoft Research Asia 2018 Collaborative Research Grant.

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Authors and Affiliations

  1. SentiSum, London, UK

    Axel Almquist

  2. Kyoto University, Kyoto, 606-8501, Japan

    Adam Jatowt

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  1. Axel Almquist
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Correspondence to Adam Jatowt .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Almquist, A., Jatowt, A. (2019). Towards Content Expiry Date Determination: Predicting Validity Periods of Sentences. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_6

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