SZTAKI HLT | Measuring semantic similarity of words using concept networks
Gábor Recski, Eszter Iklódi, Katalin Pajkossy, András Kornai
Measuring semantic similarity of words using concept networks
In Proceedings of the 1st Workshop on Representation Learning for NLP, 2016

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We present a state-of-the-art algorithm for measuring the semantic similarity of word pairs using novel combinations of word embeddings, WordNet, and the concept dictionary 4lang. We evaluate our system on the SimLex-999 benchmark data. Our top score of 0.755 is higher than any published system that we are aware of, well beyond the average inter-annotator agreement of 0.67, and close to the 0.78 average correlation between a human rater and the average of all other ratings, suggesting that our system has achieved near-human performance on this benchmark.

Citation
@inproceedings{Recski:2016c
    title = "Measuring Semantic Similarity of Words Using Concept Networks",
    author = "Recski, G{\'a}bor  and
      Ikl{\'o}di, Eszter  and
      Pajkossy, Katalin  and
      Kornai, Andr{\'a}s",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1622",
    doi = "10.18653/v1/W16-1622",
    pages = "193--200",
}