- The paper surveys intrinsic and extrinsic methods used to evaluate word embeddings, providing a typology and discussing challenges and datasets.
- Intrinsic evaluation assesses embeddings against human judgments or semantic structures via conscious, subconscious, thesaurus-based, or linguistic-driven analysis.
- Extrinsic evaluation measures embeddings' utility in downstream NLP tasks, while challenges include semantics obscurity, dataset bias, subjectivity, and lack of correlation.
A Survey of Word Embeddings Evaluation Methods
Amir Bakarov's paper, "A Survey of Word Embeddings Evaluation Methods," offers a comprehensive examination of the methodologies employed to evaluate word embeddings. Despite the widespread utilization of distributional semantic models (DSMs) in NLP, rigorous evaluations of their efficacy remain unexplored. Bakarov's work explores both intrinsic and extrinsic evaluation methods, offering a hierarchical typology that distinguishes between multiple evaluation avenues, while systematically organizing challenges and discussing available evaluation datasets.
Intrinsic evaluation methods focus on assessing word representations against human judgments or semantic structures implicitly contained within language corpora. Bakarov categorizes intrinsic evaluations into classes based on consciousness, thesauri, and linguistic-driven analysis. Conscious intrinsic evaluation involves tasks like semantic similarity judgment and word analogy, relying heavily on human perception. Subconscious evaluation ventures into experimental frontiers, including neuroimaging and eye-tracking, tapping into less consciously biased data. Thesaurus-based evaluations utilize structured semantic networks or knowledge bases, while linguistic-driven methods, such as phonosemantic analysis, incorporate natural language patterns and statistical co-occurrence to gauge embeddings quality.
Extrinsic evaluation methods, in contrast, appraise the utility of embeddings in performing downstream NLP tasks, such as semantic role labeling and sentiment analysis. These methods integrate word embeddings as features within supervised machine learning models and judge them based on task performance metrics. However, as Bakarov notes, extrinsic evaluation can be controversial due to lack of correlation between performance across different tasks and complexities in creating task-specific datasets.
The paper elucidates various challenges inherent in evaluating word embeddings. Notably, the semantics evaluation is encumbered by the obscurity regarding word relationships and the adequacy of supervised datasets. A significant focus is on biases within evaluation methods aligned with DSM tasks, grappling with implicit prejudices in linguistic training data. Furthermore, intrinsic evaluations may suffer from subjectivity, while extrinsic methods face issue with dataset adequacy and correlation.
Looking toward future developments, Bakarov raises the importance of addressing biases and extending methodologies to accommodate emerging multi-language and multi-sense embeddings. He suggests increasing efforts in constructing language-independent evaluation frameworks, which would neutralize the evident English-centric dataset limitations.
Bakarov's typology and examination serve as an instrumental reference for researchers in the field of lexical semantics and NLP. His synthesis emphasizes the need for standardized and robust evaluation methodologies, fostering academic discourse toward more refined DSM evaluation strategies. As AI and DSM applications proliferate, the exploration of evaluation methods as proposed by Bakarov is crucial to the advancement of language processing technologies.