Natural language semantic search system and method using weighted global semantic representations
First Claim
1. A method for performing a semantic matching process, the method, with at least one computing device, comprising:
- detecting one or more meanings of a query,comparing the one or more meanings with one or more detected meanings of one or more pieces of content, andoutputting at least one response of the comparing;
wherein detecting one or more meanings of the query further comprises;
detecting and formalizing all meanings of the query into a global semantic representation, wherein the global semantic representation gives a full meaning of the query by transforming individual or groups of words of the query into semantic representations comprising pairs of lemma and a semantic category retrieved from a lexicon and lexical functions assignments and rules database, andweighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the query;
wherein detecting one or more meanings of the one or more pieces of content further comprises;
detecting and formalizing one or more meanings of the one or more pieces of content into a global semantic representation, wherein the global semantic representation gives a full meaning of the one or more pieces of content by transforming individual or groups of words of the one or more pieces of content into semantic representations comprising of pairs of lemma and a semantic category retrieved from the lexicon and lexical functions assignments and rules database; and
weighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the one or more pieces of content;
wherein the comparing further comprises;
calculating a semantic matching degree and assigning a score between the global weighted semantic representation of the query and the global weighted semantic representation of the one or more pieces of content, andretrieving at least one piece of content of the one or more pieces of content based on the at least one piece of content having the best assigned score and output the retrieved at least one piece of content as the response;
wherein the one or more pieces of content are formed of phrases or expressions obtained from a contents database.
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Abstract
Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response (R) as the result of a semantic matching process consisting in comparing a natural language query (Q) with a plurality of contents (C), formed of phrases or expressions obtained from a contents'"'"' database (6), and selecting the response (R) as being the contents corresponding to the comparison having a best semantic matching degree. It involves the transformation of the contents (C) and the query in individual words or groups of tokenized words (W1, W2), which are transformed in its turn into semantic representations (LSC1, LSC2) thereof, by applying the rules of Meaning Text Theory and through Lexical Functions, the said semantic representations (LSC1, LSC2) consisting each of a couple formed of a lemma (L) plus a semantic category (SC).
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Citations
15 Claims
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1. A method for performing a semantic matching process, the method, with at least one computing device, comprising:
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detecting one or more meanings of a query, comparing the one or more meanings with one or more detected meanings of one or more pieces of content, and outputting at least one response of the comparing; wherein detecting one or more meanings of the query further comprises; detecting and formalizing all meanings of the query into a global semantic representation, wherein the global semantic representation gives a full meaning of the query by transforming individual or groups of words of the query into semantic representations comprising pairs of lemma and a semantic category retrieved from a lexicon and lexical functions assignments and rules database, and weighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the query; wherein detecting one or more meanings of the one or more pieces of content further comprises; detecting and formalizing one or more meanings of the one or more pieces of content into a global semantic representation, wherein the global semantic representation gives a full meaning of the one or more pieces of content by transforming individual or groups of words of the one or more pieces of content into semantic representations comprising of pairs of lemma and a semantic category retrieved from the lexicon and lexical functions assignments and rules database; and weighting the semantic representations in a basis of their category index and their frequency to generate a global weighted semantic representation of the one or more pieces of content; wherein the comparing further comprises; calculating a semantic matching degree and assigning a score between the global weighted semantic representation of the query and the global weighted semantic representation of the one or more pieces of content, and retrieving at least one piece of content of the one or more pieces of content based on the at least one piece of content having the best assigned score and output the retrieved at least one piece of content as the response; wherein the one or more pieces of content are formed of phrases or expressions obtained from a contents database. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification