# Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts
## 3 Related Work  
In this section we review studies that use neural embeddings as a language representation model for tasks in Arabic NLP. A growing number of scholars are employing word or document embedding as a representational model for information retrieval (IR) and NLP-related tasks. This approach has been shown to significantly improve performance in many NLP tasks. [PERSON] et al. [9] introduced the semantically enhanced term frequency (SMTF) approach, which is a modified method used to compute term frequency. Representations of words are distributed in a vector space (word embedding) based on semantic similarities. This approach seeks to enhance the relevance scores that enable distinct but semantically related terms to be matched with one another. Semantically similar terms are integrated and weighted without modifying the document or query growth functions. Document scoring was improved and mismatches caused by the bag-of-words assumptions were decreased by enhancing the document term frequency among the target query term and its semantically similar word. The retrieval process for documents written in Arabic depends on the bag-of-words paradigm, which is based on semantic similarities between word vectors. The SMTF was evaluated at the inText REtrieval Conference (TREC), and the results demonstrated that incorporating the enhanced term frequency in standard probabilistic IR models produced significant improvements in comparison with baseline bag-of-word models. Researchers expanded the scope of this initial work by comparing SMTF-based extensions with word-embedding-based IR models. For example, [PERSON] et al. [10] developed an approach to deal with term mismatches in IR by incorporating words embedding semantic similarities into existing IR models. They achieved this by following an axiomatic approach to semantic term matching. Their work was an extension of existing IR frameworks, including the BM25, as well as information-based and language models. The effectiveness of these models depends on the integration of word similarity into the scoring functions of previous models. As part of the same study, [PERSON] et al. also conducted several experiments on the standard Arabic TREC data set, using title description fields on the Terrier 3.5 IR platform. They investigated three neural word embeddingmodels: the CBOW, the skip-gram model (SKIP-G), and the GloVe. The SKIP-G and GloVe produced results that were similar and significantly better than the baseline CBOW. The models significantly outperformed both the semantic indexing approach and the three word-embedding-based IR models (LM+WE, NTLM, and GLM). [PERSON] et al. [11] investigated different vectorized space representation techniques in Arabic. The authors used CBOW, SKIP-G, and GloVe to construct word representations in a vector space. Each of the models was programmed to contain a wide range of Arabic text sources. Following the normalization process, the three models were aligned to the combined corpus. Intrinsic and extrinsic evaluations were then conducted to assess the impacts on two NLP applications: IR and short-answer grading. The neural word embedding models, including word2 vec and GloVe, outperformed the previous query around expansion and enhanced the retrieval process more than other semantic expansion techniques. Furthermore, these models, compared with previous models, have performed better on information retrieval tasks. [PERSON] et al. [12] developed a technique that used a generalized language model (GLM) as part of a word-embedding-based information retrieval language model. They used vectors to embed words and model term dependencies to derive the transformation probabilities between words. Using the GLM, a generative process transformed a term, t, into a term t observed in the query. This work was based on the assumption that words in a query could be generated independently in one of three ways: LM baseline, transformation via document sampling, or transformation via collection sampling.