Comparing Word Embedding Models for Turkish Language

Word embeddings are a way of representing words in a mathematical form so that they can be easily used in computer programs, especially in tasks related to language. These representations are fixed-length vectors that aim to capture the meaning of words based on their context. There are two main types of word embeddings: static and contextual. Static embeddings assign a single vector to a word, regardless of how it is used in different situations. In contrast, contextual embeddings provide different vectors for a word, depending on its specific usage in sentences.

Word embeddings can help in various language-related tasks such as understanding the part of speech of a word, answering questions, and recognizing named entities like people or places. The study of word embeddings has evolved since the late 1990s and early 2000s, starting with techniques like latent semantic analysis and moving towards more advanced models like Word2Vec and FastText.

#Types of Word Embedding Models

Word embeddings can be categorized into two main groups:

1. Static (Non-contextual) Models: These models create one fixed vector for each word without considering the different meanings that a word might have in different contexts. Examples include Word2Vec and GloVe.

2. Contextual Models: These models generate different vectors for a word based on its context. ELMo and BERT are two common examples of this type. They create a vector representation that captures how the meaning of a word changes depending on the words around it.

Although static models are simpler, they can overlook certain nuances of words. For instance, the Turkish word "yaz" can mean "to write" or "summer," so a single vector may not capture both meanings accurately.

#Purpose of the Study

While there has been substantial research comparing different word embedding models, there has been little focus on Turkish. This study aims to compare both static and contextual models, generating static word embeddings from contextual models. This approach is particularly relevant for Turkish, which has a complex structure due to its rich morphology. The goal is to evaluate how well different models work for various language tasks in Turkish and provide insights for researchers and developers working with Turkish language data.

#Methodology

#Data Collection

For this study, two Turkish corpora were utilized: BounWebCorpus and HuaweiCorpus. These corpora contain text from various sources and serve as a foundation for training the word embeddings. The total size of the combined corpus is substantial, consisting of millions of words.

#Word Embedding Models Used

Several models were examined in this study, including:

• Word2Vec: This model can be trained using different techniques, such as Skip-gram and Continuous Bag of Words (CBOW).
• FastText: Similar to Word2Vec, but it represents each word as a combination of character n-grams, making it better at handling unknown words.
• GloVe: This model focuses on the global context of words, using statistics about word co-occurrences.
• ELMo: This model creates embeddings based on a bidirectional language model, capturing the meaning of words from both left and right contexts.
• BERT: A more advanced model that uses Transformers to create contextual embeddings.

#Conversion of Contextual Embeddings to Static Embeddings

To compare static and contextual models, two methods were used to convert contextual embeddings into static ones:

1. Pooling Method: Collects the embeddings of a word in various contexts and averages them to create a single, static representation.

2. X2Static Method: Integrates contextual information into a static model to produce a more fitting static embedding.

#Evaluation of Word Embeddings

#Intrinsic Evaluation

For intrinsic evaluation, the quality of the word embeddings was assessed through analogy and similarity tasks. Analogy tasks focus on identifying relationships between words, such as "man is to woman as king is to queen." Similarity tasks measure how closely related two words are in meaning.

The study divided these tasks into semantic and syntactic categories to assess how well the models can capture different types of relations.

#Extrinsic Evaluation

Extrinsic evaluations were conducted using three main tasks: sentiment analysis, part-of-speech tagging, and named entity recognition. These tasks are practical applications where the quality of embeddings directly impacts the results. For instance, sentiment analysis determines whether a piece of text expresses a positive or negative opinion, while part-of-speech tagging assigns grammatical categories to words.

#Key Findings

#Intrinsic Results

The analysis revealed that static BERT embeddings, generated using the X2Static method, outperformed other models in many tasks. Word2Vec also performed well, particularly in semantic tasks, while FastText showed strong results due to its ability to capture morphological features relevant to Turkish.

GloVe fell short in performance, particularly with complex morphology. Aggregated contextual models underperformed compared to non-contextual models, indicating that simply averaging embeddings may not be ideal.

#Extrinsic Results

In the extrinsic evaluations, the findings mirrored those of the intrinsic tasks, with X2Static BERT and averaged Word2Vec-FastText embeddings leading the way. Word2Vec maintained a strong position, confirming its effectiveness in real-world applications.

#Importance of Static Embeddings

The research strongly indicates that static word embeddings continue to be significant in NLP tasks, especially in cases where computational efficiency and resource constraints are considerations. The static versions of contextual embeddings provide a useful alternative for many applications.

#Conclusion

This study highlights the importance of conducting thorough evaluations of word embedding models, particularly for languages like Turkish. The findings provide valuable insights for researchers and practitioners, guiding them in selecting appropriate models for specific NLP tasks. Static embeddings derived from contextual models, especially those from BERT, proved to be effective alternatives to conventional static and contextual models.

#Future Directions

Going forward, there is room for further study to assess word embedding models beyond the tasks explored in this research. Future evaluations may look into more complex tasks such as machine translation and systems designed for dialogue. The methodology developed in this research can be adapted to other languages with similar structures, expanding the impact of these findings beyond Turkish.

Overall, understanding word embeddings' roles and capabilities remains essential for advancements in natural language processing, and this research contributes to the ongoing efforts in the field.