A Survey of Semantic Analysis Approaches SpringerLink
In this comprehensive article, we will embark on a captivating journey into the realm of semantic analysis. We will delve into its core concepts, explore powerful techniques, and demonstrate their practical implementation through illuminating code examples using the Python programming language. Get ready to unravel the power of semantic analysis and unlock the true potential of your text data. Sentence-level perception and semantic analysis described above can be scaled to paragraphs, chapters, whole texts, and even larger structures, addressing the problem of computational scalability95,148,149.
Additionally, it delves into the contextual understanding and relationships between linguistic elements, enabling a deeper comprehension of textual content. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. Text semantics are frequently addressed in text mining studies, since it has an important influence in text meaning. This paper reported a systematic mapping study conducted to overview semantics-concerned text mining literature. Thus, due to limitations of time and resources, the mapping was mainly performed based on abstracts of papers.
Expert Systems with Applications
Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future. As text semantics has an important role in text meaning, the term semantics has been seen in a vast sort of text mining studies. However, there is a lack of studies that integrate the different research branches and summarize the developed works.
By accurately identifying and categorizing named entities, NER enables machines to gain a deeper understanding of text and extract relevant information. Semantic analysis, also known as semantic parsing or computational semantics, is the process of extracting meaning from language by analyzing the relationships semantic analysis of text between words, phrases, and sentences. Semantic analysis aims to uncover the deeper meaning and intent behind the words used in communication. To summarize, natural language processing in combination with deep learning, is all about vectors that represent words, phrases, etc. and to some degree their meanings.
Advantages of semantic analysis
The resulting model quantifies subjective familiarity between cognitive entities that is an essential in knowledge systems36,124. In texts, it allows to extract and quantify meaning relations between concepts, requested for semantic analysis of natural language data125,126,127. Simplicity and interpretability of the model, in accord with the positive results reported above, exemplifies advantage of quantum approach to cognitive modeling discussed in the beginning of this section. Whether using machine learning or statistical techniques, the text mining approaches are usually language independent. However, specially in the natural language processing field, annotated corpora is often required to train models in order to resolve a certain task for each specific language (semantic role labeling problem is an example).
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]. Specifically for the task of irony detection, Wallace [23] presents both philosophical formalisms and machine learning approaches. The author argues that a model of the speaker is necessary to improve current machine learning methods and enable their application in a general problem, independently of domain. He discusses the gaps of current methods and proposes a pragmatic context model for irony detection.