2402 01495 A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation

This is how to use the tf-idf to indicate the importance of words or terms inside a collection of documents. TF-IDF is an information retrieval technique that weighs a term’s frequency (TF) and its inverse document frequency (IDF). The product of the TF and IDF scores of a word is called the TFIDF weight of that word.

The most popular example is the WordNet [63], an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64]. Schiessl and Bräscher [20] and Cimiano et al. [21] review the automatic construction of ontologies. Schiessl and Bräscher [20], the only identified review written in Portuguese, formally define the term ontology and discuss the automatic building of ontologies from texts.

Semantic Analysis Is Part of a Semantic System

According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. Complex nature of these phenomena makes them problematic to account with classical reductionist approach. Still, rational models of human choice developed in the era of mechanistic worldview hold as important limiting cases of individual and collective behavior18. We can any of the below two semantic analysis techniques depending on the type of information you would like to obtain from the given data. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly).

• Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context.
• It goes beyond merely analyzing a sentence’s syntax (structure and grammar) and delves into the intended meaning.
• Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement.

Semantic analysis is key to contextualization that helps disambiguate language data so text-based NLP applications can be more accurate. Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience. It’s an essential sub-task of Natural Language Processing (NLP) and the driving force behind machine learning tools like chatbots, search engines, and text analysis.

What Is Semantic Scholar?

Since 2019, Cdiscount has been using a semantic analysis solution to process all of its customer reviews online. This kind of system can detect priority axes of improvement to put in place, based on post-purchase feedback. The company can therefore analyze the satisfaction and dissatisfaction of different consumers through the semantic analysis of its reviews. As discussed in previous articles, NLP cannot decipher ambiguous words, which are words that can have more than one meaning in different contexts.

Besides, WordNet can support the computation of semantic similarity [70, 71] and the evaluation of the discovered knowledge [72]. 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 semantic analysis of text text mining applications. This fact is not unexpected, since life sciences have a long time concern about standardization of vocabularies and taxonomies. Among the most common problems treated through the use of text mining in the health care and life science is the information retrieval from publications of the field.

Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. Leser and Hakenberg [25] presents a survey of biomedical named entity recognition. The authors present the difficulties of both identifying entities (like genes, proteins, and diseases) and evaluating named entity recognition systems. They describe some annotated corpora and named entity recognition tools and state that the lack of corpora is an important bottleneck in the field. Some studies accepted in this systematic mapping are cited along the presentation of our mapping.

Thus, this paper reports a systematic mapping study to overview the development of semantics-concerned studies and fill a literature review gap in this broad research field through a well-defined review process. Semantics can be related to a vast number of subjects, and most of them are studied in the natural language processing field. As examples of semantics-related subjects, we can mention representation of meaning, semantic parsing and interpretation, word sense disambiguation, and coreference resolution. Nevertheless, the focus of this paper is not on semantics but on semantics-concerned text mining studies. This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches.

Search Engines:

Less than 1% of the studies that were accepted in the first mapping cycle presented information about requiring some sort of user’s interaction in their abstract. To better analyze this question, in the mapping update performed in 2016, the full text of the studies were also considered. Figure 10 presents types of user’s participation identified in the literature mapping studies. Besides that, users are also requested to manually annotate or provide a few labeled data [166, 167] or generate of hand-crafted rules [168, 169]. Jovanovic et al. [22] discuss the task of semantic tagging in their paper directed at IT practitioners. Semantic tagging can be seen as an expansion of named entity recognition task, in which the entities are identified, disambiguated, and linked to a real-world entity, normally using a ontology or knowledge base.

In this semantic space, alternative forms expressing the same concept are projected to a common representation. It reduces the noise caused by synonymy and polysemy; thus, it latently deals with text semantics. Another technique in this direction that is commonly used for topic modeling is latent Dirichlet allocation (LDA) [121]. The topic model obtained by LDA has been used for representing text collections as in [58, 122, 123]. The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46]. The high interest in getting some knowledge from web texts can be justified by the large amount and diversity of text available and by the difficulty found in manual analysis.

Journal of Information Processing and Management

Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans. For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. After the selection phase, 1693 studies were accepted for the information extraction phase.