In the realm of natural language processing, comparing the effectiveness of different word embedding models is crucial for optimizing text analysis tasks, particularly synonym extraction. This article delves into a comparative study between two prominent models, Word2vec and fastText, assessing their performance across various dimensions and synonym expression patterns. When we consider a Synonym For Comparing these models, we might think of evaluating, contrasting, or benchmarking their capabilities. This exploration aims to provide a comprehensive understanding of their strengths and weaknesses in identifying synonyms, especially within specialized domains like radiation technology.
Overall Performance: fastText’s Edge in Synonym Extraction
Across the board, fastText demonstrated superior performance compared to Word2vec in synonym extraction tasks. Notably, the fastText model utilizing the Continuous Bag of Words (CBOW) architecture achieved peak performance at 300 dimensions. Furthermore, a remarkable consistency in scores was observed across different vector dimensions with fastText CBOW, with minimal variation. In contrast, Word2vec models employing the skip-gram approach generally outperformed CBOW, except when using 100-dimensional representations. These findings underscore that for synonym extraction, particularly in the context of radiation technology, fastText with CBOW architecture, and vector dimensions between 300 and 400, emerges as the most effective approach. However, it is important to note that for practical automation, extracting terms from multiple ranks and implementing robust filtering is essential to refine synonym selection due to the tendency of synonyms to appear in lower ranks.
Synonym Expression Patterns: N-grams and Model Behavior
An in-depth analysis of seven synonym expression patterns revealed that fastText surpassed Word2vec in four specific categories: “transliteration variants,” “different Japanese forms with the same pronunciation and meaning,” “Japanese shortened forms,” and “plural expressions.” A key characteristic shared by these categories is the subtle difference in character count among words within the synonym sets, implying a common n-gram. In simpler terms, synonyms in these categories often share a common sequence of characters.
Figure 5 illustrates the distribution of word vectors using t-distributed Stochastic Neighbor Embedding (t-SNE), a dimension reduction technique. Focusing on “Japanese shortened forms,” the visualization reveals that fastText tends to cluster terms with the same n-gram closer together. Conversely, Word2vec exhibits wider and overlapping clusters for words with shared n-grams. This observation further suggests fastText’s advantage in detecting synonyms with common n-grams at higher rankings.
Interestingly, the optimal architecture and vector dimensions varied across different synonym categories. Skip-gram architecture proved suitable only for “different Japanese forms with the same pronunciation and meaning.” The fastText CBOW model tends to identify synonyms based on shared n-grams, while skip-gram leaned towards extracting synonym pairs lacking common n-grams. Consequently, CBOW becomes more advantageous when synonym sets frequently contain common n-grams; otherwise, skip-gram may perform comparably or even better. Regarding vector dimensions, increasing dimensionality generally improved or maintained performance. While larger vector dimensions often enhance accuracy, as supported by previous research, the ideal vector size can be influenced by the specific characteristics of the synonym sets. This aspect warrants further investigation.
In contrast, for categories like “conversion to transliteration,” “Japanese words and English acronyms,” and “Japanese and English words,” Word2vec demonstrated comparable or superior performance to fastText across various vector dimensions. However, the overall accuracy in these categories was significantly lower, peaking at 50% for “Japanese words and English acronyms” and remaining below 30% for others. This lower accuracy, compared to categories with shared n-grams, suggests that fastText struggles with synonym sets lacking common character strings, necessitating exploration of methods to enhance Word2vec’s performance in such scenarios.
Furthermore, observations indicated that when generating outputs involving English words and abbreviations, Japanese words tended to be ranked lower. This is likely due to the models being primarily trained on Japanese corpora, limiting exposure to English expressions. However, accuracy for English words and abbreviations improved significantly when the models were restricted to output only alphabet characters. This implies that specifying character sets can be a beneficial strategy when aiming to generate outputs in languages different from the training corpus.
Domain-Specific Synonym Patterns: Insights Across Fields
Examining specific domains, FastText with the CBOW model yielded optimal results in “Image Engineering,” “Physical Phenomena,” “Equipment,” “Radiation Therapy,” “Medicine,” and “Imaging Diagnosis.” Synonyms in these fields often include words with shared n-grams. Conversely, in “Radiation Control” and “Imaging Diagnosis,” fastText with Skip-grams was preferred, likely due to its ability to detect words that posed challenges for CBOW, contributing to improved evaluation indices. In “Informatics,” many synonyms fell under “different Japanese writings of the same meaning” and “Japanese word and Japanese transliteration,” categories characterized by fewer shared n-grams. In such cases, where common n-grams are less prevalent, Word2vec may offer a competitive edge over fastText.
Comparison with Previous Studies: Contextualizing the Findings
Comparing skip-gram and CBOW within Word2vec, prior studies have often favored CBOW for tasks like similar word detection and text classification. Conversely, research on fastText has suggested skip-gram’s superiority, particularly in sentiment analysis-based classifications. Our findings align with these existing studies for Word2vec. However, in our research, the difference in cumulative ratio between the most accurate model (CBOW) and skip-gram was marginal, approximately 1.9%. This subtle difference suggests a potential inherent advantage for skip-gram depending on the specific task.
Regarding vector dimensionality, previous research indicates that accuracy generally increases with vector size, often peaking around 300 dimensions. Our results are consistent with these observations, reinforcing the general trends in word embedding research. It is important to acknowledge that prior studies differed in focus, not specifically targeting medical terminology or synonym extraction in the same way as our research.
In conclusion, while synonyms for comparing Word2vec and fastText might include evaluating or contrasting, this study clearly benchmarks their performance in synonym extraction. fastText, particularly with CBOW, generally outperforms Word2vec, especially when synonyms share n-grams and in specific domains like radiation technology. However, the optimal model and parameters can vary depending on the specific synonym patterns and domain, highlighting the nuanced nature of word embedding model selection for effective synonym extraction.
