# Enhancing Word Embeddings for Improved Semantic Alignment
## 1 Introduction  
To effectively process text, it is necessary to represent it numerically, which allows computers to analyze a document's content. Creating an appropriate representation of a text is fundamental to achieving good results from machine learning algorithms. Traditional approaches to text representation treat documents as points in a high-dimensional space, where each axis corresponds to a word in the vocabulary. In this model, documents are represented as vectors with coefficients indicating the frequency of each word in the text (e.g., using the TF-IDF in a vector space model (VSM) [1]). But this approach is limited by its inability to capture the semantics of words. When using word-based representations in a VSM, we cannot distinguish between synonyms (words with similar meanings but different forms) and hononyms (words with the same form but different meanings). For example, the words \"car\" and \"automobile\" may be treated as completely different entities in word space, even though they have the same meaning. Such information can be provided to the NLP system by an external database. A good example that provides such information is the WordNet dictionary [2], which is organized in the form of a semantic network that contains relationships between synsets that describe groups of words that have the same meaning [3]. Other approaches may include interactions with humans that enrich the vector representations with semantic features [4]. Nevertheless, in the context of NLP, word embeddings have a lot of potential compared to the use of semantic networks. One advantage is their ability to perform classical algebraic operations on vectors, such as addition or multiplication. To address the problem of semantic word representation, an extension of the VSM was introduced using a method known as word embeddings. In this model, each word is represented as a vector in a multidimensional space, and the similarity between vectorsreflects the semantic similarity between words. Models such as Word2 Vec [5], GloVe [6], FastText [7] and others learn word representations in an unsupervised manner. The training is based on the context in which words appear in a text corpus, which allows some elementary relationships between them to be captured [8]. In the research presented in our paper, we refer to these embeddings as original embeddings (OE), which were used in the experiments as GloVe vectors. Using vector-based representations, geometric operations become possible, enabling basic semantic inference. A classic example, proposed by [PERSON] [5], is the operation king-man + woman = queen, which demonstrates how vectors can capture hierarchical relationships between words [9]. These operations enhance the modeling of language dependencies, enabling more advanced tasks like machine translation, semantic searches, and sentiment analysis. Our study aims to explore methods for creating semantic word embeddings, namely embeddings that carry precise meanings with a strong reference to WordNet. We present methods for building improved word embeddings based on semi-supervised techniques. In our experiments, we refer to these embeddings as neural embeddings (NE). After applying the alignment we refer to them as Fine-tuned Embeddings (FE). Our approach extends the method proposed in [10], modifying word vectors so that geometric operations on them correspond to fundamental semantic relationships. We call this method geometrical embeddings (GE). The word embeddings generated by our approach have been evaluated in terms of their usefulness for text classification, clustering, and the analysis of class distribution in the embedded representation space. The rest of this paper is organized as follows: Section 2 describes various methods for creating word embeddings, both supervised and unsupervised. Section 3 presents our approach, including the dataset, the preprocessing methods and techniques used to create semantic word embeddings, and the evaluation methodology. Section 4 presents the experimental results.