# Contemporary Approaches in Evolving Language Models
## 3 Neural Network Models
### Word Representation
#### 3.1.4 Co-Occurrence Matrix
The same author and his colleagues [31] conducted experiments using the Distributed Memory (DM) and Distributed Bag of Words (DBoW) algorithms of Doc2 Vec, revealing that an insufficient volume of data negatively affects sentiment analysis results. In Ref. [32], the embedding of words was expanded, leveraging subword information for the Arabic language in the domain of machine translation tasks. [PERSON]. [PERSON] introduced a model enhancing both the accuracy and efficiency of the clustering of Arabic articles, achieved through the application of word-embedding-based clustering and discretization techniques. [PERSON] et al. [33] proposed an unsupervised score-based method that amalgamated four distinct techniques: the vector space model, continuous bag of words (CBOW), clustering, and a statistically based method. This method surpassed other methods in the extraction of key sentences from Arabic text. In Ref. [34], the authors compared the Word2 Vec and Lemma2 Vec models in tasks pertaining to the disambiguation of Arabic words in context. They achieved this without resorting to external resources or any specific contextual/sense embedding model. Their findings revealed that both Lemma2 Vec and Word2 Vec performed commendably across various sentence characteristics. [PERSON] et al. [35] crafted three datasets for the Tatar language and compared the performance of Skip-gram, FastText, and GloVe models on datasets of both the Tatar and English languages. Models trained on English datasets generally outperformed their Tatar counterparts, with differences particularly notable in semantic and syntactic categories. The study suggests that the higher effectiveness of English models may be related to the nature of the questions posed and discrepancies in the training corpora. However, some correlations between Tatar and English models are observed in specific categories. The lack of large parallel corpora for both languages is acknowledged as a limitation in the comparison. In Ref. [36], the word2 vec and GloVe methods were implemented for semantic, syntactic, and capital evaluation tasks based on an Azerbaijan dataset. The results highlighted that enhancing the dimensionality of continuous vectors and training data amplifies the effectiveness of word vectors for both architectures. Conversely, it was observed that compared to word2 vec, GloVe produced more accurate word embeddings for semantic evaluation tasks in Azerbaijan. In contrast, for syntactic evaluation tasks, the inverse was true. Assessment of the Results from Applying Methods Like Word2 Vec, Skip-Gram, FastText, and GloVe for the Language Modeling of Agglutinative Languages  
In the Chinese, Japanese, and Korean languages, the FastText method offers significant advantages in text classification tasks over byte-level one-hot encoding. This underscores the importance of effective text representation, especially for languages that employ an ideographic writing system. The Turkish language, with its unique morphology and structure, demonstrates that methods like Word2 Vec are successfully employed across various scenarios, including text classification and sentiment analysis. However, the specific choice in word modeling approach, for instance, opting between CBOW and SkipGram or employing Doc2 Vec, may be contingent on the task at hand and data availability. This highlights the importance of the experimental validation of diverse methods to optimize performance across different NLP applications. Furthermore, when exploring semantic relations, such as hypernymy in the Turkish language, word-embedding-based methods, augmented by semantic projection and biased classification, can provide deeper and more insightful representations of relationships between words. For the Arabic language, the focus is placed on morphological word embedding, considering its intricate morphology. There is also an emphasis on employing various methods for clustering and key sentence extraction to enhance content analysis and automated text annotation efficacy.