Abstract
We present an unsupervised phrase relatedness function (f) that has been applied in a Semantic Textual Similarity system (TrWP) of SemEval-2015. The best run of TrWP was ranked 33 among 73 runs. f finds the relatedness strength between two phrases using overlapping bi-gram context extracted from the Google-n-gram corpus. The relatedness strength is the strength of association capturing how similar or dissimilar two phrases are. In order to find the relatedness strength, f applies a sum-ratio (SR) technique based on the statistics of the overlapping n-grams associated with two input phrases. The experimental result from f demonstrates improvement over existing phrase relatedness methods on two standard datasets of 216 phrase-pairs. f does not require any human annotated resource and is independent of the syntactic structure of phrases.
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Notes
- 1.
We use ‘relatedness’ and ‘similarity’ interchangeably in our paper, albeit ‘similarity’ is a special case or a subset of ‘relatedness’.
- 2.
We use the term Sum-Ratio as the weighted mean of two numbers.
- 3.
Perform pruning on the bi-gram contexts implies to the pruning of the Google-n-grams from which those contexts are extracted.
- 4.
We prefer Pearson’s r to Spearman’s \(\rho \) because Agirre et al. [28] stated that Pearson’s r is more informative than Spearman’s \(\rho \). Spearman’s \(\rho \) considers the rank differences while Pearson’s r takes into account the value differences. Moreover, SemEval-2013 [28] used Pearson’s r for evaluation task.
- 5.
Pearson’s r is not computed using Mitchell and Lapata’s [7] system due to the unavailability of their individual phrase-pair score. Moreover, in an attempt to reproduce Mitchell and Lapata’s [7] method, Hartung and Frank [6] get Spearman’s \(\rho = 0.34\) instead of \(\rho =0.46\) on 108 adjective-noun pairs.
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Rakib, M.R.H., Islam, A., Milios, E. (2016). f: Phrase Relatedness Function Using Overlapping Bi-gram Context. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_19
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