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A Structured Vector Space Model for Word Meaning in Context

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The document presents a novel structured vector space model (SVS) for computing word meanings in context, addressing limitations of existing models that overlook syntactic structure. The SVS model integrates lexical meaning with selectional preferences, allowing for more accurate representations of word meanings based on their contextual relationships. The paper evaluates the SVS model against state-of-the-art methods in predicting paraphrase appropriateness and demonstrates its effectiveness through experiments, highlighting its potential for improving natural language processing tasks.

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How to represent words as vectors?

The process of creating word vectors involves training a machine learning model on a large corpus of text. During training, the model learns to predict the surrounding words given a target word. This process is known as word2vec, and it results in word vectors that capture the contextual information of words.

How to represent a vector in text?

Vector notation In printed works, such as text books, it is common practice to use bold face to indicate that a symbol, such as v, represents a vector. In written work, where bold face is not available, the symbols representing vectors are underlined.

What does a word vector look like?

In simpler terms, a word vector is a row of real-valued numbers (as opposed to numbers) where each point captures a dimension of the words meaning and where semantically similar words have similar vectors.

What is the vector space representation of text?

Vector space model or term vector model is an algebraic model for representing text documents (or more generally, items) as vectors such that the distance between vectors represents the relevance between the documents. It is used in information filtering, information retrieval, indexing and relevancy rankings.

How to do vectors on word?

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