Semantic Similarity System

A Semantic Similarity System is a semantic analysis system that can solve a semantic similarity task (which calculates semantic distance between linguistic terms in a textual data).

• AKA: Semantic Similarity Machine Learning System, Semantic Similarity Measuring System, Semantic Similarity Modelling System.
• Context:
  • It can solve a Semantic Similarity Task by implementing a Semantic Similarity Algorithm.
  • It can range from being a Topological Semantic Similarity Analysis System to being a Statistical Semantic Similarity Analysis System.
  • It can range between being a Semantic Word Similarity System to being a Semantic Textual Similarity System.
  • …
• Example(s):
  • Edge-based Similarity Learning System,
  • Node-based Similarity Learning System,
  • Normalized Compression Distance Measuring System.
  • …
• Counter-Example(s):
  • Lexicographical Similarity Learning System,
  • Semantic Relatedness Learning System.
• See: Text Corpus, Metric, Semantics, Lexicographical, Topological, Ontology, Partially Ordered Set, Directed Acyclic Graph, Taxonomy (General), Vector Space Model, Correlation.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_similarity Retrieved:2021-5-29.
  • Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations.

    For example, "car" is similar to "bus", but is also related to "road" and "driving".

    Computationally, semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts ordered in a partially ordered set and represented as nodes of a directed acyclic graph (e.g., a taxonomy), would be the shortest-path linking the two concept nodes. Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus. The evaluation of the proposed semantic similarity / relatedness measures are evaluated through two main ways. The former is based on the use of datasets designed by experts and composed of word pairs with semantic similarity / relatedness degree estimation. The second way is based on the integration of the measures inside specific applications such the information retrieval, recommender systems, natural language processing, etc.

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