2402 01495 A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
It involves words, sub-words, affixes (sub-units), compound words, and phrases also. This article is part of an ongoing blog series on Natural Language Processing (NLP). I hope after reading that article you can understand the power of NLP in Artificial Intelligence.
Beyond latent semantics, the use of concepts or topics found in the documents is also a common approach. The concept-based semantic exploitation is normally based on external knowledge sources (as discussed in the “External knowledge sources” section) [74, 124–128]. As an example, explicit semantic analysis [129] rely on Wikipedia to represent the documents by a concept vector. In a similar way, Spanakis et al. [125] improved hierarchical semantic text analysis clustering quality by using a text representation based on concepts and other Wikipedia features, such as links and categories. This mapping shows that there is a lack of studies considering languages other than English or Chinese. The low number of studies considering other languages suggests that there is a need for construction or expansion of language-specific resources (as discussed in “External knowledge sources” section).
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
Chatbots, virtual assistants, and recommendation systems benefit from semantic analysis by providing more accurate and context-aware responses, thus significantly improving user satisfaction. Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them. Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis. As discussed earlier, semantic analysis is a vital component of any automated ticketing support. It understands the text within each ticket, filters it based on the context, and directs the tickets to the right person or department (IT help desk, legal or sales department, etc.).
Apart from sample and word count information, we additionally include (a) quantities pertaining to the POS information useful for the POS disambiguation method and (b) the amount of semantic information minable from the text. The POS annotation count and the synset/concept counts are expressed as ratios with respect to the number of words per document. Example of the disambiguation phase of the context-embedding disambiguation strategy. A candidate word is mapped to its embedding representation and compared to the list of available synset vectors. The synset with the vector representation closest to the word embedding is selected.
Semantic Analysis Examples
Figure 5 presents the domains where text semantics is most present in text mining applications. Health care and life sciences is the domain that stands out when talking about text semantics in text mining applications. This fact is not unexpected, since life sciences have a long time concern about standardization of vocabularies and taxonomies. The building of taxonomies and ontologies is such a common practice in health care and life sciences that World Wide Web Consortium (W3C) has an interest group specific for developing, evaluating, and supporting semantic web technologies for this field [32].
