Evaluating Word Embedding Models: Methods and Experimental Results (1901.09785v2)

Abstract: Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evalutors.

Evaluating Word Embedding Models: Methods and Experimental Results

This paper presents a comprehensive evaluation of word embedding models, exploring both intrinsic and extrinsic evaluation methods. The authors aim to delineate the strengths and weaknesses of various word representations by employing a wide range of evaluators, highlighting the correlation between intrinsic metrics and downstream NLP tasks.

Word embeddings, a cornerstone of modern NLP, are real-valued vector representations capturing semantic and syntactic meanings. Despite their pervasive application in tasks like semantic analysis, dependency parsing, and machine translation, identifying a universally optimal word embedding model remains elusive. This paper categorizes evaluation methods into intrinsic and extrinsic evaluators. Intrinsic evaluators assess word models independently of specific NLP tasks, focusing on syntactic and semantic word relationships, whereas extrinsic evaluators measure performance as input features in downstream tasks.

Intrinsic Evaluators

1. Word Similarity: This method measures the correlation between the vector distance of word pairs and human-perceived similarity. Evaluators such as cosine similarity are frequently used, although they face challenges distinguishing semantic similarity from relatedness, as well as issues with low correlation in specific tasks.
2. Word Analogy: Analogical reasoning is evaluated through tasks such as solving proportional analogies, revealing strengths in capturing semantic similarities but showing limitations in identifying complex lexical relations like antonyms.
3. Concept Categorization: This involves grouping words into categorical subsets, which reflects the embedding's ability to organize semantic clusters. However, the task faces challenges due to subjectivity and data set issues.
4. Outlier Detection: This less common task identifies words that do not fit into a group, serving as a test for semantic coherence within vector space models. Its efficacy as an intrinsic evaluator is limited by varying dataset standards and its reliance on human reasoning.
5. QVEC: This method measures alignment with linguistic dimensions derived from annotated corpora. Despite a recall-oriented approach, it is critiqued for relying on man-made vectors that can lead to less representative downstream performance evaluation.

Extrinsic Evaluators

The paper utilizes extrinsic evaluators by incorporating word embeddings in NLP tasks such as part-of-speech tagging, chunking, named-entity recognition, sentiment analysis, and neural machine translation. These tasks emphasize the applied utility of word models over abstract vector space characteristics.

Experimental Findings and Implications

Through extensive experiments, the authors assess popular models including SGNS, CBOW, GloVe, FastText, ngram2vec, and Dict2vec, using datasets like WS-353, Google Analogy, AP Concept Categorization, and various sentiment analysis corpora. The results reveal that SGNS-based models generally outperform others in both intrinsic and extrinsic evaluations, underscoring their robustness in different tasks. However, these findings also suggest that model performance is highly sensitive to the choice of evaluators and tasks—a consistent theme across all model evaluations.

The paper highlights that intrinsic evaluations, such as word similarity and analogy, align more closely with extrinsic tasks like sentiment analysis and translation, suggesting their suitability for preliminary model assessment. Conversely, evaluators such as concept categorization and QVEC exhibit greater variance in correlation with downstream task performance.

Future Directions

The paper concludes by advocating for the development of more holistic evaluation metrics that effectively capture word embedding quality across a broader array of tasks. This includes developing computationally efficient intrinsic evaluators with high predictive power for extrinsic performance. The exploration of task-specific embedding models, optimized through domain-specific data, also presents a promising avenue for future research. This aligns with the increasing recognition of the importance of embedding subspaces in encoding nuanced linguistic properties and relationships—a reflection of the complexity inherent in human language understanding.