BME 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

Link to paper

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,
  author    = {Recski, G\'{a}bor  and  Ikl\'{o}di, Eszter  and  Pajkossy, Katalin  and  Kornai, Andras},
  title     = {Measuring Semantic Similarity of Words Using Concept Networks},
  booktitle = {Proceedings of the 1st Workshop on Representation Learning for NLP},
  year      = {2016},
  address   = {Berlin, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {193--200}
}