A Survey of Word Embeddings Evaluation Methods

Abstract: Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora. Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges.

• The paper provides a comprehensive survey of evaluation techniques, categorizing 16 intrinsic and 12 extrinsic methods for word embeddings.
• It examines techniques such as word semantic similarity, word analogies, and neural activation patterns to assess embedding quality.
• The study highlights challenges like cross-lingual evaluation and bias issues, emphasizing the need for universally applicable methods.

A Survey of Word Embeddings Evaluation Methods

Introduction

The paper "A Survey of Word Embeddings Evaluation Methods" (1801.09536) by Amir Bakarov provides an exhaustive analysis of techniques used to evaluate word embeddings. Word embeddings serve as distributed representations of words within a corpus, essential for various NLP applications. Despite their ubiquitous use, consensus on their evaluation remains elusive. The survey categorizes evaluation methodologies into intrinsic and extrinsic methods, highlighting existing challenges and examining 16 intrinsic and 12 extrinsic methods.

Extrinsic Evaluation Methods

Extrinsic evaluation involves assessing word embeddings based on performance in specific downstream NLP tasks. These tasks range from Part-of-Speech Tagging and Named Entity Recognition to Sentiment Analysis and Semantic Role Labeling. The premise is that embeddings effective in one task should exhibit reliability across others. However, this hypothesis is not consistently upheld due to differing feature reliance across tasks. Extrinsic methods are criticized for high complexity in constructing gold-standard datasets and demonstrating limited correlation with intrinsic measures.

Intrinsic Evaluation Methods

Intrinsic evaluation measures the embeddings' ability to replicate human judgments on semantic and syntactic relationships. These methods fall into four categories:

1. Conscious Intrinsic Evaluation
   • Word Semantic Similarity: Assesses alignment between human semantic similarity judgments and embedding-induced metrics.
   • Word Analogy: Involves solving analogy puzzles (e.g., "king - man + woman = queen") to gauge relational awareness within embeddings.
   • Thematic Fit: Tests embeddings' capacity to differentiate thematic roles by predicting the most semantically appropriate noun for given verbs.
2. Subconscious Intrinsic Evaluation
   • Semantic Priming: Leverages psycholinguistic experiments where word recognition is faster following semantically related primes.
   • Neural Activation Patterns: Utilizes fMRI and EEG to examine representation alignment with neural responses during word processing.
3. Thesaurus-Based Evaluation
   • Thesaurus Vectors: Compares embeddings against document-based inverted index vectors mapping to knowledge categories (e.g., WordNet super-senses).
4. Linguistic-Driven Methods
   • Phonosemantic Analysis: Explores relationships between sound patterns and meanings through Levenshtein distances on phonetic transcriptions.
   • Bi-gram Co-Occurrence Frequency: Utilizes corpus-based frequency measures of bi-gram occurrences to appraise semantic cohesion.

Challenges and Future Directions

The survey identifies a range of unresolved challenges. One is the applicability of evaluation strategies to multi-language and multi-sense embeddings, which differ fundamentally from traditional mono-language, mono-sense models. Moreover, consideration of potential biases arising from gender or racial stereotyping within embeddings represents a critical research avenue. Additionally, the reliance on extensive language-specific datasets underscores a need for universally applicable evaluation methods.

Conclusion

Bakarov's paper synthesizes an extensive exploration of word embedding evaluation methodologies, delineating existing tools and spotlighting the field's ongoing challenges. As NLP continues evolving, developing robust, fair, and comprehensive evaluation mechanisms remains pivotal to harnessing the full potential of word embeddings across diverse linguistic landscapes.