Scale-Dependent Relationships in Natural Language.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Machine Learning

Background:

• Natural language sequences exhibit statistical dependencies across various scales.
• Current statistical learning models, like Word2Vec, often impose a fixed sampling scale, limiting their ability to capture long-range dependencies.
• This limitation means that relationships beyond the predefined scale remain undetected by the algorithm.

Purpose of the Study:

• To investigate how different context window scales in Word2Vec embeddings influence the encoding of linguistic relationships.
• To determine if specific scales preferentially capture certain syntactic and semantic information.
• To explore the impact of scale variation on word neighborhoods within embeddings.

Main Methods:

• Systematically generated a family of Word2Vec embeddings by manipulating the context window size.
• Assessed the encoded linguistic relationships using analogical reasoning tasks (Google Analogies dataset).
• Evaluated semantic similarity using human rating datasets (WordSim-353 and SimLex-999).

Main Results:

• Different context window scales preferentially encode distinct linguistic relationships.
• Specific scales were found to emphasize different syntactic and semantic relations between words.
• The neighborhood of a word in the embedding space varied significantly with changes in the sampling scale.

Conclusions:

• Individual sampling scales capture only a subset of a word's meaningful relationships.
• The findings underscore the limitations of fixed-scale models in representing comprehensive semantic meaning.
• There is a need to develop scale-free models for a more complete understanding of semantic meaning in language.