Semantic text classification: A survey of past and recent advances

Semantic Analysis Guide to Master Natural Language Processing Part 9

semantic text analysis

Such models include BERT or GPT, which are based on the Transformer architecture. The conduction of this systematic mapping followed the protocol presented in the last subsection and is illustrated in Fig. The selection and the information extraction phases were performed with support of the Start tool [13].

semantic text analysis

Thanks to machine learning and natural language processing (NLP), semantic analysis includes the work of reading and sorting relevant interpretations. Artificial intelligence contributes to providing better solutions to customers when they contact customer service. These proposed solutions are more precise and help to accelerate resolution times. The application of semantic analysis methods generally streamlines organizational processes of any knowledge management system. Academic libraries often use a domain-specific application to create a more efficient organizational system. By classifying scientific publications using semantics and Wikipedia, researchers are helping people find resources faster.

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.

  • Relationship extraction is a procedure used to determine the semantic relationship between words in a text.
  • The formal semantics defined by Sheth et al. [28] is commonly represented by description logics, a formalism for knowledge representation.
  • As you can see, this approach does not take into account the meaning or order of the words appearing in the text.

Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience. However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive. In semantic text analysis Table A1, we list the mapping between label index and label name, for each datasets examined in the main experimental evaluation. These corpora extend our evaluation to datasets with few classes and small number of samples (as is the case with BBC) and to datasets from a radically different domain (i.e., the medical content of Ohsumed).

Semantic Textual Similarity

The authors divide the ontology learning problem into seven tasks and discuss their developments. They state that ontology population task seems to be easier than learning ontology schema tasks. Stavrianou et al. [15] also present the relation between ontologies and text mining. Ontologies can be used as background knowledge in a text mining process, and the text mining techniques can be used to generate and update ontologies. With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”.

semantic text analysis

Simple structure of the rotated loading matrix (see Figure 1) helps identify with clarity what each principal component represents. PCA is a method that allows us to identify and extract uncorrelated dimensions of maximal variability from our data. This is done through an eigenvalue decomposition of the correlation matrix of the data. Figure 1 depicts the steps involved in the eigenvalue decomposition and ensuing steps to aid in interpretation of the PCA (note the figure is oversimplified to facilitate this review). Semantic analysis transforms data (written or verbal) into concrete action plans.