JoBimText System
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A JoBimText System is a Semantic Similarity System that implements a JoBim Algorithm.
- Context:
- It supports the construction of a JoBim model.
- It calculates semantic relatedness of pairs of terms, finds nearest neighbours and offers a native web server.
- Example(s):
- …
- Counter-Example(s):
- See: Distributional Similarity System, WordNet, Word Embedding System, Character Embedding System, Out-Of-Vocabulary (OOV) Embedding System, Subword Embedding System, Natural Language Processing System, Machine Translation System, Translation-based Word Embedding System, Semantic Relatedness, Word Similarity Task, Word Analogy Task.
References
2018
- (Sales et al., 2018) ⇒ Juliano Efson Sales, Leonardo Souza, Siamak Barzegar, Brian Davis, Andre Freitas, and Siegfried Handschuh. (2018). “Indra: A Word Embedding and Semantic Relatedness Server.” In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
- QUOTE: JoBimText is a semantic similarity tool that implements its own algorithm named JoBim (Biemann et al., 2013). The tool supports the construction of the JoBim model and also calculates semantic relatedness of pairs of terms, finds nearest neighbours and offers a native web server.
2013
- (Biemann & Riedl, 2013) ⇒ Chris Biemann, and Martin Riedl. (2013). “Text: Now in 2D! A Framework for Lexical Expansion with Contextual Similarity.” In: J. Lang. Model., 1(1).
- QUOTE: We operationalize distributional similarity in a general framework for large corpora, and describe a new method to generate similar terms in context. Our evaluation shows that distributional similarity is able to produce high-quality lexical resources in an unsupervised and knowledge-free way, and that our highly scalable similarity measure yields better scores in a WordNet-based evaluation than previous measures for very large corpora.