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Similarity Measure for Polish Short Texts Based on Wordnet-Enhanced Bag-of-words Representation

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Human Language Technology. Challenges for Computer Science and Linguistics (LTC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10930))

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Abstract

We present a method for computing semantic similarity of Polish texts with main focus given to short texts. We have taken into account the limited set of language tools for Polish, and especially that syntactic and semantic parsers do not express accuracy and robustness high enough and to become a stable basis for similarity computation. A very large wordnet of Polish, namely plWordNet is used to construct meaning representations for words in such a way that different words of the similar meaning receive similar representations. The use of a Word Sense Disambiguation (WSD) tool for Polish brought positive results in one of the method variants, regardless of the limited accuracy of the WSD tool. The proposed measures have been compared with the manual evaluation of sentence pairs. The measures were also applied as a part of the Question Answering system. Improved performance of answer finding was achieved in several types of tests.

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Notes

  1. 1.

    A lemma is a basic morphological form (or entry form) which represents a set of word forms that differ only in the values of their grammatical categories, like case, gender, person.

  2. 2.

    plWordNet includes also relations linking lexical units (i.e. triples: lemma, Part of Speech and sense identifier), and selected of them were used also in the proposed methods. plWordNet 3.0 emo is the largest wordnet in the world including 197,721 synsets, 179,125 lemmas and 260,214 lexical units described by more 40 types of lexico-semantic relations (more than 90 together with subtypes) and more than 600,000 relation links, cf. [13]. The vast majority of plWordNet has been manually mapped onto Princeton WordNet.

  3. 3.

    Web application and Web Service: http://ws.clarin-pl.eu/wsd.shtml.

  4. 4.

    The balanced test set is a more difficult test case as even very unfrequent senses are represented in it, while the average text sample has a character of running text and do not include many rare senses.

  5. 5.

    The total score in the whole graph is equal to 1.

  6. 6.

    For Proper Names, the morpho-syntactic tagger used returns often the word form as a lemma.

  7. 7.

    Synonymy is expressed by synsets. Hypernymy links more general synsets with more specific ones. However, in fact, in plWordNet all relations are defined for lexical units, cf. [14]. Synset relations are notational abbreviations and a link between two synsets means that all pairs of their members are linked by the given relation. Hyponymy (a reverse relation to the hypernymy). Meronymy links a part/element/portion etc. to the whole, e.g. [18], and holonymy links a whole to its part/element/portion, but it is not necessarily reverse to meronymy.

  8. 8.

    Instance links Proper Names with common nouns that represent their most specific characterisation and type is the reverse relation. Inter-register synonymy links synsets whose members are very close in meaning but differ in use in the language practice, i.e. they differ by their stylistic register, e.g. one synset belongs to the general language and the other one to vulgar.

  9. 9.

    These relations are not shared among lexical units, and that is why they link only selected lexical units, not synsets. Femininity links lexical units of feminine forms with their masculine counterparts. Markedness is a general relation for several semantic associations signalled by the derivational relations, i.e. diminutive, augmentative, young being. Antonymy expresses binary opposition in meaning, and converse is also binary semantic opposition, but such that the two lexical units have (verbs) argument structures with arguments of opposite roles or filling the opposite roles in some predicates (nouns).

  10. 10.

    https://www.wikinews.org .

  11. 11.

    The opposition relations resulted in improvement in the application of the word expansion algorithm, as lemmas linked by the opposition relations are often very similar according to the Distributional Semantic methods, e.g. word embeddings.

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Acknowledgments

This work was co-financed as a part of the investment in the CLARIN-PL research infrastructure (www.clarin-pl.eu) funded by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland

    Maciej Piasecki & Anna Gut

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  1. Maciej Piasecki
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  2. Anna Gut
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Correspondence to Maciej Piasecki .

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Editors and Affiliations

  1. Adam Mickiewicz University, Poznań, Poland

    Zygmunt Vetulani

  2. LIMSI-CNRS, Orsay Cedex, France

    Joseph Mariani

  3. Adam Mickiewicz University, Poznań, Poland

    Marek Kubis

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Piasecki, M., Gut, A. (2018). Similarity Measure for Polish Short Texts Based on Wordnet-Enhanced Bag-of-words Representation. In: Vetulani, Z., Mariani, J., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2015. Lecture Notes in Computer Science(), vol 10930. Springer, Cham. https://doi.org/10.1007/978-3-319-93782-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-93782-3_13

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  • Online ISBN: 978-3-319-93782-3

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