Representing variety at the lexical level

An information retrieval technique using latent semantic structure was patented in by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing . The method typically starts by processing all of the words in the text to capture the meaning, independent of language.
semantic text analysis

Synonymy is the phenomenon where different words describe the same idea. Thus, a query in a search engine may fail to retrieve a relevant document that does not contain the words which appeared in the query. For example, a search for “doctors” may not return a document containing the word “physicians”, even though the words have the same meaning.

Elements of Semantic Analysis

Nevertheless, the focus of this paper is not on semantics but on semantics-concerned text mining studies. This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches. Although several researches have been developed in the text mining field, the processing of text semantics remains an open research problem. The field lacks secondary studies in areas that has a high number of primary studies, such as feature enrichment for a better text representation in the vector space model.

semantic text analysis

The second phase of the process involves a broader scope of action, studying the meaning of a combination of words. It aims to analyze the importance and impact of combining words, forming a complete sentence. This approach helps a business get exclusive insight into the customers’ expressions and emotions around a brand.

Approaches to Meaning Representations

Therefore, the reader can miss in this systematic mapping report some previously known studies. 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. The review semantic text analysis reported in this paper is the result of a systematic mapping study, which is a particular type of systematic literature review . Systematic literature review is a formal literature review adopted to identify, evaluate, and synthesize evidences of empirical results in order to answer a research question. It is extensively applied in medicine, as part of the evidence-based medicine .

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’ alt=’https://metadialog.com/’ class=’aligncenter’ style=’display:block;margin-left:auto;margin-right:auto; width=’405px’/>

I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. It’s an essential sub-task of Natural Language Processing and the driving force behind machine learning tools like chatbots, search engines, and text analysis.

LSI timeline

Natural language understanding —a computer’s ability to understand language. Quixel is an open source project for text content analysis semantically. Extensive business analytics enables an organization to gain precise insights into their customers. Consequently, they can offer the most relevant solutions to the needs of the target customers.

6 Types of Metadata and Their Uses – Spiceworks News and Insights

6 Types of Metadata and Their Uses.

Posted: Thu, 20 Oct 2022 13:31:14 GMT [source]

Looking for the answer to this question, we conducted this systematic mapping based on 1693 studies, accepted among the 3984 studies identified in five digital libraries. In the previous subsections, we presented the mapping regarding to each secondary research question. In this subsection, we present a consolidation of our results and point some future trends of semantics-concerned text mining.

If this knowledge meets the process objectives, it can be put available to the users, starting the final step of the process, the knowledge usage. Otherwise, another cycle must be performed, making changes in the data preparation activities and/or in pattern extraction parameters. If any changes in the stated objectives or selected text collection must be made, the text mining process should be restarted at the problem identification step. Semantic Analysis is a subfield of Natural Language Processing that attempts to understand the meaning of Natural Language.
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’ alt=’semantic text analysis’ class=’aligncenter’ style=’display:block;margin-left:auto;margin-right:auto; width=’409px’/>

Because it uses a strictly mathematical approach, LSI is inherently independent of language. This enables LSI to elicit the semantic content of information written in any language without requiring the use of auxiliary structures, such as dictionaries and thesauri. LSI can also perform cross-linguistic concept searching and example-based categorization. For example, queries can be made in one language, such as English, and conceptually similar results will be returned even if they are composed of an entirely different language or of multiple languages. LSI helps overcome synonymy by increasing recall, one of the most problematic constraints of Boolean keyword queries and vector space models.

Parts of Semantic Analysis

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. 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.

Some are vector space based such as PCA or LSA, others such as LDA are probability based. We will be discussing Latent Semantic Analysis, or LSA, because it introduces us to new concepts we will use in the future. What semantic annotation brings to the table are smart data pieces containing highly-structured and informative notes for machines to refer to.

In this approach, only the lexical component of the texts are considered. 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 semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context.

The selection and the information extraction phases were performed with support of the Start tool . In the following subsections, we describe our systematic mapping protocol and how this study was conducted. Besides, going even deeper in the interpretation of the sentences, we can understand their meaning—they are related to some takeover—and we can, for example, infer that there will be some impacts on the business environment. In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses.

semantic text analysis

We want to assign a document to one or more classes, depending on its content. Sometimes these topics are predefined, such as when we are trying to determine the author of a work. Sometimes they are created by the model, and the criteria used by the model to create the topics tells us something about the documents in question. We develop a method for the automated detection and segmentation of speech balloons in comic books, including their carrier and tails. Our method is based on a deep convolutional neural network that was trained on annotated pages of the Graphic Narrative Corpus.

  • Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph.
  • 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.
  • The most important task of semantic analysis is to get the proper meaning of the sentence.
  • Nevertheless, the focus of this paper is not on semantics but on semantics-concerned text mining studies.
  • But in order to gain valuable insights from surveys, feedback forms, and reviews, you need to sort and analyze mountains of text data—but spreadsheets aren’t cutting it.

To address some of the limitation of bag of words model , multi-gram dictionary can be used to find direct and indirect association as well as higher-order co-occurrences among terms. The original term-document matrix is presumed too large for the computing resources; in this case, the approximated low rank matrix is interpreted as an approximation (a “least and necessary evil”). This matrix is also common to standard semantic models, though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrices are not always used. Deal with the email overload generated by customers without reading them, with our unique, content-based labels. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. A way to create automatically Q&A Systems based on DSLs (Domain-specific Languages), thus allowing the setup and the validation of the Q&B System to be independent of the implementation techniques is proposed.

Dagan et al. 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 Challenges. They declared that the systems submitted to those challenges use cross-pair similarity measures, machine learning, and logical inference. Methods that deal with latent semantics are reviewed in the study of Daud et al. . 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.

semantic text analysis

One ancient Indian language, Sanskrit, has its own unique way of embedding syntactic information within words of relevance in a sentence. Sanskrit grammar is defined in 4000 rules by PaninI reveals the mechanism of adding suffixes to words according to its use in sentence. Through this article, a method of extracting meaningful information through suffixes and classifying the word into a defined semantic category is presented.

  • A way to create automatically Q&A Systems based on DSLs (Domain-specific Languages), thus allowing the setup and the validation of the Q&B System to be independent of the implementation techniques is proposed.
  • Wimalasuriya and Dou , Bharathi and Venkatesan , and Reshadat and Feizi-Derakhshi consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task.
  • With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level.
  • Basically, stemming is the process of reducing words to their word stem.

In this case, Aristotle can be linked to his date of birth, his teachers, his works, etc. Organize your information and documents into enterprise knowledge graphs and make your data management and analytics work in synergy. SimpleX lets you automatically tag your data with keywords, sentiment analysis, and originality scores. LSI automatically adapts to new and changing terminology, and has been shown to be very tolerant of noise (i.e., misspelled words, typographical errors, unreadable characters, etc.). This is especially important for applications using text derived from Optical Character Recognition and speech-to-text conversion. LSI also deals effectively with sparse, ambiguous, and contradictory data.