Semantic Text Analysis Technology Application in Assessing Current Threats and Software Vulnerabilities

As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use. One of the most promising applications of semantic analysis in NLP is sentiment analysis, which involves determining the sentiment or emotion expressed in a piece of text. This can be used to gauge public opinion on a particular topic, monitor brand reputation, or analyze customer feedback. By understanding the sentiment behind the text, businesses can make more informed decisions and respond more effectively to their customers’ needs. Natural language processing (NLP) and machine learning (ML) techniques underpin sentiment analysis.

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. The researchers conducting the study must define its protocol, i.e., its research questions and the strategies for identification, selection of studies, and information extraction, as well as how the study results will be reported. The main parts of the protocol that guided the systematic mapping study reported in this paper are presented in the following.

Relationship Extraction

The process enables computers to identify and make sense of documents, paragraphs, sentences, and words as a whole. For example, in opinion mining for a product, semantic analysis can identify positive and negative opinions about the product and extract information about specific features or aspects of the product that users have opinions about. Previous approaches to semantic analysis, specifically those which can be described as using templates, use several levels of representation to go from the syntactic parse level to the desired semantic representation. The different levels are largely motivated by the need to preserve context-sensitive constraints on the mappings of syntactic constituents to verb arguments.

Ontologies rely on structured and hierarchical knowledge bases that define the concepts, categories, and relationships in a domain. Lastly, semantic networks use graphs or networks that connect words or terms with semantic relations such as synonyms, hypernyms, or hyponyms. 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). Besides, linguistic resources as semantic networks or lexical databases, which are language-specific, can be used to enrich textual data. Thus, the low number of annotated data or linguistic resources can be a bottleneck when working with another language.

Tasks involved in Semantic Analysis

The correctness of English semantic analysis directly influences the effect of language communication in the process of English language application [2]. Machine translation is more about the context knowledge of phrase groups, paragraphs, chapters, and genres inside the language than single grammar and sentence translation. Statistical approaches for obtaining semantic information, such as word sense disambiguation and shallow semantic analysis, are now attracting many people’s interest from many areas of life [4]. To a certain extent, the more similar the semantics between words, the greater their relevance, which will easily lead to misunderstanding in different contexts and bring difficulties to translation [6]. A subfield of natural language processing (NLP) and machine learning, semantic analysis aids in comprehending the context of any text and understanding the emotions that may be depicted in the sentence.

• To build the document vector, we fill each dimension with a frequency of occurrence of its respective word in the document.
• In the “Systematic mapping summary and future trends” section, we present a consolidation of our results and point some gaps of both primary and secondary studies.
• Statistical approaches for obtaining semantic information, such as word sense disambiguation and shallow semantic analysis, are now attracting many people’s interest from many areas of life [4].
• The authors argue that search engines must also be able to find results that are indirectly related to the user’s keywords, considering the semantics and relationships between possible search results.
• I chose frequency Bag-of-Words for this part as a simple yet powerful baseline approach for text vectorization.
• Based on English grammar rules and analysis results of sentences, the system uses regular expressions of English grammar.

The results show that this method can better adapt to the change of sentence length, and the period analysis results are more accurate than other models. ChemicalTagger has been developed in a modular manner using the Java framework, making individual components such as tokenisers, vocabularies and phrase grammars easily replaceable. This facilitates metadialog.com the study of a wide range of chemical subdomains which vary in syntactic style, vocabulary and semantic abstraction. Moreover, it is possible to convert ChemicalTagger’s output into CML [22] using a ChemicalTagger2CML converter. Thus, identified phrase-based chemistry such as solutions, reaction and procedures can converted into computable CML.

External knowledge sources

In a similar way, Spanakis et al. [125] improved hierarchical clustering quality by using a text representation based on concepts and other Wikipedia features, such as links and categories. When looking at the external knowledge sources used in semantics-concerned text mining studies (Fig. 7), WordNet is the most used source. This lexical resource is cited by 29.9% of the studies that uses information beyond the text data. WordNet can be used to create or expand the current set of features for subsequent text classification or clustering.

Powerful machine learning tools that use semantics will give users valuable insights that will help them make better decisions and have a better experience. It is a crucial component of Natural Language Processing (NLP) and the inspiration for applications like chatbots, search engines, and text analysis using machine learning. Grammatical analysis and the recognition of links between specific words in a given context enable computers to comprehend and interpret phrases, paragraphs, or even entire manuscripts.

A Review for Semantic Analysis and Text Document Annotation Using Natural Language Processing Techniques

[8] [6] Our research is more similar to the work of Ravi since we also worked with raw text and examining it through k-grams. We became interested in their work with neural networks as a more effective similarity ranking, since we struggled with our similarity algorithm throughout the project. However, in an effort to limit the scope of our project, we did not incorporate any neural network methods into our method. Semantic analysis is a technique that involves determining the meaning of words, phrases, and sentences in context. This goes beyond the traditional NLP methods, which primarily focus on the syntax and structure of language.

By incorporating semantic analysis, AI systems can better understand the nuances and complexities of human language, such as idioms, metaphors, and sarcasm. This has opened up new possibilities for AI applications in various industries, including customer service, healthcare, and finance. 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.

Systematic mapping planning

Besides the top 2 application domains, other domains that show up in our mapping refers to the mining of specific types of texts. We found research studies in mining news, scientific papers corpora, patents, and texts with economic and financial content. 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.

Next, we ran the method on titles of 25 characters or less in the data set, using trigrams with a cutoff value of 19678, and found 460 communities containing more than one element. The table below includes some examples of keywords from some of the communities in the semantic network. Semantic analysis is the understanding of natural language (in text form) much like humans do, based on meaning and context.

Why Chinese or Japanese? Comparing the Difficulty of Learning Each Language

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 application of text mining methods in information extraction of biomedical literature is reviewed by Winnenburg et al. [24]. The paper describes the state-of-the-art text mining approaches for supporting manual text annotation, such as ontology learning, named entity and concept identification. They also describe and compare biomedical search engines, in the context of information retrieval, literature retrieval, result processing, knowledge retrieval, semantic processing, and integration of external tools.

As these are basic text mining tasks, they are often the basis of other more specific text mining tasks, such as sentiment analysis and automatic ontology building. Therefore, it was expected that classification and clustering would be the most frequently applied tasks. Traditionally, text mining techniques are based on both a bag-of-words representation and application of data mining techniques. In order to get a more complete analysis of text collections and get better text mining results, several researchers directed their attention to text semantics. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms. The activities performed in the pre-processing step are crucial for the success of the whole text mining process.