Alignment-based Linguistic Sense Disambiguation Algorithm
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An Alignment-based Linguistic Sense Disambiguation Algorithm is a Linguistic Sense Detection Algorithm that is based on a Linguistic Sequence Alignment Algorithm.
- Context:
- It can range from being an Alignment-based Word Sense Disambiguation Algorithm to being an Alignment-based Sentence Sense Disambiguation Algorithm.
- Example(s):
- Alignment-based sense disambiguation algorithm implemented by the ADW System (Pilehvar et al., 2013),
- ...
- Counter-Example(s):
- See: Sequence Alignment Algorithm, Word Sense Disambiguation Algorithm, Word Sense, Semantic Similarity Measure, Semantic Word Similarity Measure, Semantic Word Similarity Benchmark Task.
References
2013
- (Pilehvar et al., 2013) ⇒ Mohammad Taher Pilehvar, David Jurgens, and Roberto Navigli. (2013). “Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity.” In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013) Volume 1: Long Papers.
- QUOTE: We view sense disambiguation as an alignment problem. Given two arbitrarily ordered texts, we seek the semantic alignment that maximizes the similarity of the senses of the context words in both texts. To find this maximum we use an alignment procedure which, for each word type wi in item $T_1$, assigns $w_i$ to the sense that has the maximal similarity to any sense of the word types in the compared text $T_2$. Algorithm 1 formalizes the alignment process, which produces a sense disambiguated representation as a result. Senses are compared in terms of their semantic signatures, denoted as function $\mathcal{R}$.
- QUOTE: We view sense disambiguation as an alignment problem. Given two arbitrarily ordered texts, we seek the semantic alignment that maximizes the similarity of the senses of the context words in both texts. To find this maximum we use an alignment procedure which, for each word type wi in item $T_1$, assigns $w_i$ to the sense that has the maximal similarity to any sense of the word types in the compared text $T_2$. Algorithm 1 formalizes the alignment process, which produces a sense disambiguated representation as a result. Senses are compared in terms of their semantic signatures, denoted as function $\mathcal{R}$.
