Text & Semantic Analysis Machine Learning with Python by SHAMIT BAGCHI

Semantic analysis techniques involve extracting meaning from text through grammatical analysis and discerning connections between words in context. This process empowers computers to interpret words and entire passages or documents. Word sense disambiguation, a vital aspect, helps determine multiple meanings of words. This proficiency goes beyond comprehension; it drives data analysis, guides customer feedback strategies, shapes customer-centric approaches, automates processes, and deciphers unstructured text.

Several different research fields deal with text, such as text mining, computational linguistics, machine learning, information retrieval, semantic web and crowdsourcing. Grobelnik [14] states the importance of an integration of these research areas in order to reach a complete solution to the problem of text understanding. The review reported in this paper is the result of a systematic mapping study, which is a particular type of systematic literature review [3, 4].

Computer Science > Computation and Language

According to psycho-physiological parallelism54, modern cognitive science builds on fusion of physical and information descriptions outlined above, constituting complementary sides of the same phenomena55,56,57,58,59,60,61,62,63. In this approach, firing frequency of distributed ensembles of neurons functions as a code of cognitive algorithms and signals64,65. Detailed correspondence between these cognitive and physiological perspectives is established by dual-network representation of cognitive entities and neural patterns that encode them59,66,67. This survey investigates the existing and recent advancements in the semantic text classification field and highlights strengths in comparison to the traditional text classification approach. This section presents a summary comparison with respect to a number of key criteria. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation.

Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. In the above sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data. Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text.

Languages

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). Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language.

We do not present the reference of every accepted paper in order to present a clear reporting of the results. 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]. 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. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots.

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. Traditionally, text mining techniques are based on both a bag-of-words representation and application of data mining techniques.

For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs. You can proactively get ahead of NLP problems by improving machine language understanding. By knowing the structure of semantic analysis of text sentences, we can start trying to understand the meaning of sentences. We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors. And if we want to know the relationship of or between sentences, we train a neural network to make those decisions for us.

Need of Meaning Representations

It is not our objective to present a detailed survey of every specific topic, method, or text mining task. This systematic mapping is a starting point, and surveys with a narrower focus should be conducted for reviewing the literature of specific subjects, according to one’s interests. Dagan et al. [26] introduce a special issue of the Journal of Natural Language Engineering on textual entailment recognition, which is a natural language task that aims to identify if a piece of text can be inferred from another. The authors present an overview of relevant aspects in textual entailment, discussing four PASCAL Recognising Textual Entailment (RTE) Challenges. They declared that the systems submitted to those challenges use cross-pair similarity measures, machine learning, and logical inference.

This allows to build explicit and compact cognitive-semantic representations of user’s interest, documents, and queries, subject to simple familiarity measures generalizing usual vector-to-vector cosine distance. The result is more precise estimation of subjective relevance judgments leading to better composition of search result pages40,41,42,43. Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension. Its prowess in both lexical semantics and syntactic analysis enables the extraction of invaluable insights from diverse sources. Semantic analysis is a crucial component of natural language processing (NLP) that concentrates on understanding the meaning, interpretation, and relationships between words, phrases, and sentences in a given context. It goes beyond merely analyzing a sentence’s syntax (structure and grammar) and delves into the intended meaning.

External knowledge sources

It’s not just about understanding text; it’s about inferring intent, unraveling emotions, and enabling machines to interpret human communication with remarkable accuracy and depth. From optimizing data-driven strategies to refining automated processes, semantic analysis serves as the backbone, transforming how machines comprehend language and enhancing human-technology interactions. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations.