How Semantic Analysis Impacts Natural Language Processing

Among other external sources, we can find knowledge sources related to Medicine, like the UMLS Metathesaurus [95–98], MeSH thesaurus [99–102], and the Gene Ontology [103–105]. Methods that deal with latent semantics are reviewed in the study of Daud et al. [16]. The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions.

With a semantic analyser, this quantity of data can be treated and go through information retrieval and can be treated, analysed and categorised, not only to better understand customer expectations but also to respond efficiently. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result. All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost.

Semantic text analysis istio

This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines. Read on to find out more about this semantic analysis and its applications for customer service. Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites.

However, there is a lack of studies that integrate the different research branches and summarize the developed works. This paper reports a systematic mapping about semantics-concerned text mining studies. Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries. The produced mapping gives a general summary of the subject, points some areas that lacks the development of primary or secondary studies, and can be a guide for researchers working with semantics-concerned text mining. It demonstrates that, although several studies have been developed, the processing of semantic aspects in text mining remains an open research problem. As previously stated, the objective of this systematic mapping is to provide a general overview of semantics-concerned text mining studies.

Reinforcing the company’s customer self-service solutions

The analysis of the data is automated and the customer service teams can therefore concentrate on more complex customer inquiries, which require human intervention and understanding. Further, digitised messages, received by a chatbot, on a social network or via email, can be analyzed in real-time by machines, improving employee productivity. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings.

• The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc.
• Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience.
• Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community.
• We do not present the reference of every accepted paper in order to present a clear reporting of the results.
• Depending on how QuestionPro surveys are set up, the answers to those surveys could be used as input for an algorithm that can do semantic analysis.

In the post-processing step, the user can evaluate the results according to the expected knowledge usage. The formal semantics defined by Sheth et al. [28] is commonly represented by description logics, a formalism for knowledge representation. The application of description logics in natural language processing is the theme of the brief review presented by Cheng et al. [29]. The first step of a systematic review or systematic mapping study is its planning.

WordNet can be used to create or expand the current set of features for subsequent text classification or clustering. The use of features based on WordNet has been applied with and without good results [55, 67–69]. Besides, WordNet can support the computation of semantic similarity [70, 71] and the evaluation of the discovered knowledge [72].

This is a good survey focused on a linguistic point of view, rather than focusing only on statistics. The authors discuss a series of questions concerning natural language issues that should be considered when applying the text mining process. Most of the questions are related to text pre-processing and the authors present the impacts of performing or not some pre-processing activities, such as stopwords removal, stemming, word sense disambiguation, and tagging. The authors also discuss some existing text representation approaches in terms of features, representation model, and application task. The set of different approaches to measure the similarity between documents is also presented, categorizing the similarity measures by type (statistical or semantic) and by unit (words, phrases, vectors, or hierarchies).

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