NLP: Mathematizing Meaning and Context in Language

This article explores the mathematization of meaning and context in NLP through distributional semantics and contextualized word representations.

In this article, we uncover various methods used to ascertain the similarity between words and sentences, including lexical similarity, semantic similarity, syntactic similarity, machine learning methods, and hybrid approaches. However, our main focus lies on distributional similarity, a technique that analyzes the contextual patterns in which words or sentences appear.

1. SIMILARITY BETWEEN WORDS/SENTENCES

There are several methods to ascertain similarity between two words or sentences. Here are some common approaches:

1. Lexical Similarity: This method focuses on comparing the words or terms themselves. It can involve techniques such as comparing the spelling, pronunciation, or part-of-speech tags of the words. Measures like edit distance or Levenshtein distance can be used to quantify the difference between two words based on their characters.

2. Semantic Similarity: This approach aims to determine the similarity based on the meaning of words or sentences. It often utilizes techniques from…