This book “Resources of Machine Translation Between Arabic and English” represents a crucial milestone in the ongoing efforts to advance the field of computational linguistics. It aims to propel the Arabic language forward, enabling it to keep pace with the rapid developments in natural language processing (NLP). Beyond this, the book strives to balance the processing of two distinct languages—Arabic and English—while building on previous efforts and introducing innovative contributions. This has opened new horizons for researchers and professionals in the broader field of NLP, particularly in the computational processing of the Arabic language.
The strength and impact of the study presented in this book, especially in the realm of machine translation, stem from its primarily linguistic focus. It establishes a clear scientific methodology for improving the outputs of machine translation between Arabic and English, thereby paving the way for linguistic researchers to delve into the fields of “computational linguistics” or “language computing.” The author has successfully developed a model that can be followed to enhance machine translation outputs between any two languages, regardless of their nature.
This book serves as a vital reference for researchers in the field of natural language processing and the application of artificial intelligence to language services. It presents a relatively novel scientific study in the Arab world and lays the groundwork for Arab researchers to explore a fertile and promising field, especially in light of global technological advancements. The author has skillfully integrated manual capabilities with automated tools to achieve the desired objectives. The study is enriched with a wealth of tools and observational procedures to identify the differences between machine translation and human translation, aiming to pinpoint and address issues. Despite the existence of numerous bilingual and terminological lexicons, the dynamic nature of language necessitates continuous updates and additions to these resources.
Moreover, the study does not merely focus on identifying the problems of machine translation between Arabic and English. Its primary goal is to establish a methodology for extracting these problems with minimal effort and maximum efficiency, leveraging available tools and technologies to solve them. This is evident in the study’s use of semantic similarity measurement tools for sentence pairs, integrated with the BERT (Bidirectional Encoder Representations from Transformers) technology provided by Google. The author not only utilized this technology but also conducted an in-depth analysis, identifying some of its shortcomings when applied to the Arabic language. The study contributes a linguistic resource that can enhance the outputs of this technology in the future, marking one of its key strengths and distinguishing features.
Another notable aspect of the study is its use of linguistic statistics to describe data and results, ensuring precision and clarity in information gathering and result interpretation. In parallel, the study employs morphological analyzers, automatic annotators, and alignment techniques to linguistically process corpus texts and extract linguistic information that can be utilized to improve the efficiency and outputs of machine translation