A method for computing lexical semantic distance using linear functionals

A topological embedding of the lexicon for semantic distance computation

“…The Gram-Schmidt procedure can be summarized in matrix form as follows (Jensen et al 2008). The Gram-Schmidt procedure can be summarized in matrix form as follows (Jensen et al 2008).…”

“…We compare our distance metric (referred to as Jensen et al 2008) with the two widely documented metrics that outperformed it in the word similarity task (see Section 4.1), namely Leacock and Chodorow's, and Lin's, as well as with one of the metrics that performed worse than ours did, namely Resnik's. Our document collection is a reduced version of the Reuters-21578 dataset, which contains 200 documents, from 5 classes.…”

“…In Jensen, Giraud-Carrier and Davis (2008), we tested our lexical semantic distance measure on a well-known word similarity and relatedness dataset, and showed that it compared favorably with several other knowledge-based semantic distances. Here, we first extend the word-level distance to its natural document-level distance, and describe a space and time efficient implementation.…”

“…In Jensen et al . (2008), we applied our word-level semantic measure to the well-known benchmark dataset designed by Rubenstein and Goodenough (1965). This dataset consists of 65 pairs of words that 51 human subjects were asked to rate for similarity of meaning on a scale of 0–4.…”

Intelligent mobile messaging

“…Many sentences and concepts are conceptually very similar nevertheless they share no keywords and vice versa. In such this state, searching based on semantic approaches to reach the correct answer efficiently is much requisite [4,5]. Context-aware system (CA), has the ability to use contextual information for acquiring, discovering, interpreting, and processing diverse context, which forms the key to distribute and weave computer technology into the real life [1,3].…”