Search results
Results from the WOW.Com Content Network
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
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 [citation needed] as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of ...
Distributional semantic models have been applied successfully to the following tasks: finding semantic similarity between words and multi-word expressions; word clustering based on semantic similarity; automatic creation of thesauri and bilingual dictionaries; word sense disambiguation; expanding search requests using synonyms and associations;
ESA is considered by its authors a measure of semantic relatedness (as opposed to semantic similarity). On datasets used to benchmark relatedness of words, ESA outperforms other algorithms, including WordNet semantic similarity measures and skip-gram Neural Network Language Model ( Word2vec ).
OpenSearch (software) and Solr: the two most well-known search engine programs (many smaller exist) based on Lucene. Gensim is a Python+ NumPy framework for Vector Space modelling. It contains incremental (memory-efficient) algorithms for term frequency-inverse document frequency , latent semantic indexing , random projections and latent ...
BabelNet has been shown to enable multilingual natural language processing applications. The lexicalized knowledge available in BabelNet has been shown to obtain state-of-the-art results in: Semantic relatedness, [4] [5] Multilingual word-sense disambiguation [6] and entity linking, with the Babelfy system, [7] Video games with a purpose. [8]
Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word. An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as ...
In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.