Lin Similarity Measure

A Lin Similarity Measure is a Node-based Semantic Similarity Measure that is based on information content of the least common subsumer.

• AKA: Lin Similarity, Lin Lexical Semantic Similarity Measure.
• Context:
  • It is defined as: [math]\displaystyle{ \frac{2 \times ResnikSimilarity(c_1,c_2)}{IC(c_1) + IC(c_2)} }[/math]
• Counter-Example(s):
  • Resnik Similarity.
  • Jiang-Conrath Similarity Measure.
  • Path Distance Similarity.
  • Leacock Chodorow Similarity.
  • Wu-Palmer Similarity.

See: Semantic Similarity Measure, Semantic Similarity Score, Topological Semantic Similarity Measure, Edge-based Semantic Similarity Measure.

---

---

References

2011

• (NLTK - WordNetCorpusReader Module, 2011-Jun-19) ⇒ http://nltk.googlecode.com/svn/trunk/doc/api/nltk.corpus.reader.wordnet.WordNetCorpusReader-class.html
  • QUOTE: Lin Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input synsets.

1998

• (Lin, 1998) ⇒ Dekang Lin. (1998). “An Information-Theoretic Definition of Similarity.” In: Proceedings of the 15th International Conference on Machine Learning (ICML 1998).
  • QUOTE: This paper presents a definition of similarity that achieves two goals: Universality: We define similarity in information-theoretic terms. It is applicable as long as the domain has a probabilistic model. Since probability theory can be integrated with many kinds of knowledge representations, such as first order logic [Bacchus, 1988] and semantic networks [Pearl, 1988], our definition of similarity can be applied to many different domains where very different similarity measures had previously been proposed. Moreover, the universality of the definition also allows the measure to be used in domains where no similarity measure has previously been proposed, such as the similarity between ordinal values. Theoretical Justification: The similarity measure is not defined directly by a formula. Rather, it is derived from a set of assumptions about similarity. In other words, if the assumptions are deemed reasonable, the similarity measure necessarily follows.

---