![]() You are not likely to find libraries implementing these, however. It gives a very good overview of the state-of-the-art of distributional methods, which use word co-occurrence statistics to define a measure for word similarity. Become a Full-Stack Data Scientist Power Ahead in your AI ML Career No Pre-requisites Required Download Brochure The thesis is this: Take a line of sentence, transform it into a vector. So, if you're interested in exploring this problem a little more in-depth, I highly recommend reading Chapter 20.7 in Speech and Language Processing by Jurafsky and Martin, some of which is available through Google Books. Sentence similarity is one of the most explicit examples of how compelling a highly-dimensional spell can be. Both philosophers and linguists have tried to come up with an answer for literally thousands of years, and there's no end in sight. But what would be a representation of the meaning of, say, 'chair'? In fact, what is the exact meaning of 'chair'? If you think long and hard about this, it will twist your mind, you will go slightly mad, and finally take up a research career in Philosophy or Computational Linguistics to find the truthâ„¢. Semantic similarity is about the meaning closeness, and lexical similarity is about the closeness of the word set. To compute similarity, you need an appropriate represenation of the meaning of a word. While the example above is about images, semantic matching is not restricted to the visual modality. ![]() ![]() If Perl is not your language of choice, check the WordNet project page at Princeton, or google for a wrapper library.Äetermining word similarity is a complicated issue, and research is still very hot in this area. Semantic matching is a technique to determine whether two or more elements have similar meaning. Use the WordNet::Similarity Perl package. There's a short and a long answer to this.
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