Methods and systems of supervised learning of semantic relatedness
AssigneeTechnion Research & Development Foundation Limited
InventorsRan EL-YANIV, David Yanay
Life of this patent
Estimating the term…
Abstract
A method of evaluating a semantic relatedness of terms. The method comprises providing a plurality of text segments, calculating, using a processor, a plurality of weights each for another of the plurality of text segments, calculating a prevalence of a co-appearance of each of a plurality of pairs of terms in the plurality of text segments, and evaluating a semantic relatedness between members of each the pair according to a combination of a respective the prevalence and a weight of each of the plurality of text segments wherein a co-appearance of the pair occurs.
Claims20
A computerized method of evaluating semantic relatedness of terms, comprising:
- obtaining a plurality of text segments extracted from a plurality of documents associated with at least one user;
- calculating, using a processor, a plurality of weights, each one of said plurality of weights is calculated for a text segment of said plurality of text segments based on an analysis of the behavior of said at least one user with reference to said each text segment;
- calculating a prevalence of a co-appearance of each of a plurality of pairs of terms in said plurality of text segments;
- evaluating a semantic relatedness for determining the strength of the semantic relatedness between the terms of each said pair according to a combination of:
- 1) a prevalence of the said pair in said plurality of text segments and 2) the weight of each text segment of said plurality of text segments in which a co-appearance of said pair occurs;
- and generating a semantic relatedness dataset mapping said semantic relatedness between at least some terms of said plurality of pairs of terms, said dataset is subject to said at least one user.
A computerized method of evaluating a semantic relatedness of terms, comprising:
- identifying a plurality of text segments extracted from a plurality of documents associated with at least one targeted user;
- calculating, using a processor, a plurality of weights, each one of said plurality of weights is calculated for a text segments of said plurality of text segments based on an analysis of the behavior of said plurality of text segments;
- calculating a prevalence of a co-appearance of each of a plurality of pairs of a plurality of terms in said plurality of text segments;
- evaluating a semantic relatedness between said terms of each said pair according to said prevalence, and the weights of said text segments in which a co-appearance of each of said pairs occurs,
- generating a semantic relatedness between said terms of each said pair according to said prevalence, and the weights of said text segments in which a co-appearance of each of said pair occurs,
- generating a semantic relatedness dataset mapping said semantic relatedness between at least some of said plurality of terms, wherein said semantic relatedness dataset is subjective to said at least one target user;
- and
- using said semantic relatedness dataset in conjunction with inputs of said at least one user for at least one of aggregating personalized content, searching for content, and providing services to said at least one targeted user.
A system of evaluating a semantic relatedness of terms, comprising:
- a processor;
- an input interface which receives a plurality of text segments extracted from a plurality of documents associated with at least one user;
- a weighting module calculating a plurality of weights, each one of said plurality of weights is calculated for a text segment of said plurality of text segments based on an analysis of the behavior of said at least one user with reference to said each text segment;
- and
- a dataset generation module which, using said processor, A) calculates a prevalence of a co-appearance of each of a plurality of pairs of terms in said plurality of text segments, B) evaluates a semantic relatedness between of each said pair according to a combination of:
- 1) a prevalence of the said plurality of text segments, and 2) a weight of each text segment of said plurality of text segments in which a co-appearance of said pair occurs, said semantic relatedness subjective to said at least one user and, C) generates a semantic relatedness dataset mapping said semantic relatedness between said terms of each said pair.
Dependencies are inferred from the claim text.