{"id":2711,"date":"2023-11-16T17:35:39","date_gmt":"2023-11-16T17:35:39","guid":{"rendered":"https:\/\/unosdiasconbobby.org\/?p=2711"},"modified":"2024-02-05T18:32:38","modified_gmt":"2024-02-05T18:32:38","slug":"semantic-analysis-linguistics-wikipedia","status":"publish","type":"post","link":"https:\/\/unosdiasconbobby.org\/2023\/11\/16\/semantic-analysis-linguistics-wikipedia\/","title":{"rendered":"Semantic analysis linguistics Wikipedia"},"content":{"rendered":"
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LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. With structure I mean that we have the verb (\u201crobbed\u201d), which is marked with a \u201cV\u201d above it and a \u201cVP\u201d above that, which is linked with a \u201cS\u201d to the subject (\u201cthe thief\u201d), which has a \u201cNP\u201d above it. This is like a template for a subject-verb relationship and there are many others for other types of relationships. And single qubit states \\(\\left| \\psi _a\\right\\rangle\\) and \\(\\left| \\psi _b\\right\\rangle\\) represent marginal cognitive models of text perceived through isolated conceptual distinctions A and B.<\/p>\n<\/p>\n
However, with the advancement of natural language processing and deep learning, translator tools can determine a user\u2019s intent and the meaning of input words, sentences, and context. Beyond latent semantics, the use of concepts or topics found in the documents is also a common approach. The concept-based semantic exploitation is normally based on external knowledge sources (as discussed in the \u201cExternal knowledge sources\u201d section) [74, 124\u2013128]. As an example, explicit semantic analysis [129] rely on Wikipedia to represent the documents by a concept vector. 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. Wimalasuriya and Dou [17], Bharathi and Venkatesan [18], and Reshadat and Feizi-Derakhshi [19] consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task.<\/p>\n<\/p>\n
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. We found considerable differences in numbers of studies among different languages, since 71.4% of the identified studies deal with English and Chinese. When considering semantics-concerned text mining, we believe that this lack can be filled with the development of good knowledge bases and natural language processing methods specific for these languages. Besides, the analysis of the impact of languages in semantic-concerned text mining is also an interesting open research question.<\/p>\n<\/p>\n
The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics. Following this, the relationship between words in a sentence is examined to provide clear understanding of the context. It is normally based on external knowledge sources and can also be based on machine learning methods [36, 130\u2013133]. A systematic review is performed in order to answer a research question and must follow a defined protocol. The protocol is developed when planning the systematic review, and it is mainly composed by the research questions, the strategies and criteria for searching for primary studies, study selection, and data extraction.<\/p>\n<\/p>\n
Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can\u2019t do anything with it because it doesn\u2019t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it\u2019s not fully solved yet. Semantic analysis, a natural language processing method, entails examining the meaning of words and phrases to comprehend the intended purpose of a sentence or paragraph.<\/p>\n<\/p>\n