Description: Lexical cohesion refers to the use of related words and phrases within a text to establish meaningful connections and coherence. This linguistic phenomenon allows ideas to flow naturally, facilitating the reader’s understanding of the message. Lexical cohesion is achieved through various mechanisms, such as the repetition of key terms, the use of synonyms, antonyms, and derived words, as well as the inclusion of expressions that refer to previously mentioned concepts. By creating a network of relationships between words, ambiguity is avoided, and the structure of the discourse is reinforced. In the field of natural language processing (NLP), lexical cohesion is fundamental for the development of algorithms that analyze and generate text, as it enables machines to understand and produce language more effectively. The ability to identify and utilize lexical cohesion is essential for applications such as machine translation, text summarization, and content generation, where clarity and coherence are paramount for effective communication.
Uses: Lexical cohesion is used in various applications of natural language processing, such as machine translation, where it is crucial for maintaining the coherence of the translated text. It is also applied in automatic summarization systems, where the goal is to condense information without losing the original meaning. Additionally, it is fundamental in text generation, such as in chatbots and virtual assistants, which must produce coherent and relevant responses. In the educational field, it is used to teach students to write more effectively, helping them understand how to connect ideas and maintain fluency in their texts.
Examples: An example of lexical cohesion can be observed in a text discussing the environment. If ‘pollution’ is mentioned and then synonyms like ‘contamination’ or related terms such as ‘waste’ and ‘debris’ are used, a network of connections is established that reinforces the topic. Another example would be in an article about technology, where terms like ‘artificial intelligence,’ ‘machine learning,’ and ‘algorithms’ are repeated, creating cohesion that helps the reader follow the thread of the argument.
