Understanding Semantic Analysis NLP

In that case, it becomes an example of a homonym, as the meanings are unrelated to each other. Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023. This article does not contain any studies with human participants performed by any of the authors. In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data. Semantic Analysis is a topic of NLP which is explained on the GeeksforGeeks blog.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them. It represents the general category of the individuals such as a person, city, etc.

Statistical approach

Readers can refer to online resources like Wikipedia or academic databases such as the Web of Science. While this process may be time-consuming, it is an essential step towards improving comprehension of The Analects. From readers cognitive enhancement perspective, this approach can significantly improve readers’ understanding and reading fluency, thus enhancing reading efficiency. This study employs sentence alignment to construct a parallel corpus based on five English translations of The Analects. Subsequently, this study applied Word2Vec, GloVe, and BERT to quantify the semantic similarities among these translations.

• In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text.
• Sentiment analysis is widely applied to reviews, surveys, documents and much more.
• The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc.
• Semantics is the branch of linguistics that focuses on the meaning of words, phrases, and sentences within a language.
• The Jennings’ translation considered the readability of the text and restructured the original text, which was a very reader-friendly innovation at the time.

Grammatical rules are applied to categories and groups of words, not individual words. Another remarkable thing about human language is that it is all about symbols. According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the 1990s. The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks.

Stop words removal

The choice of method often depends on the specific task, data availability, and the trade-off between complexity and performance. In this example, we tokenize the input text into words, perform POS tagging to determine the part of speech of each word, and then use the NLTK WordNet corpus to find synonyms for each word. We used Python and the Natural Language Toolkit (NLTK) library to perform the basic semantic analysis.

In conclusion, this study presents critical findings and provides insightful recommendations to enhance readers’ comprehension and to improve the translation accuracy of The Analects for all translators. Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them.

As delineated in the introduction section, a significant body of scholarly work has focused on analyzing the English translations of The Analects. However, the majority of these studies often omit the pragmatic considerations needed to deepen readers’ understanding of The Analects. Given the current findings, achieving a comprehensive understanding of The Analects’ translations requires considering both readers’ and translators’ perspectives. The table presented above reveals marked differences in the translation of these terms among the five translators.

This study conduct triangulation method among three algorithms to ensure the robustness and reliability of the results. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. The following examples are taken from the Wikipedia page on lexical semantics. And based, on this overlap the goal is to find the synset definition which has maximum overlap with the input sentence.

Legal and Healthcare NLP

Note how some of them are only serve as subtasks for solving larger problems. Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.

This forms the major component of all results in the semantic similarity calculations. Most of the semantic similarity between the sentences of the five translators is more than 80%, this demonstrates that the main body of the five translations captures the semantics of the original Analects quite well. While, as humans, it is pretty simple for us to understand the meaning of textual information, it is not so in the case of machines.

The original text of The Analects was segmented using a method that divided it into 503 sections based on natural section divisions. This study further subdivided these segments using punctuation marks, such as periods (.), question marks (?), and semicolons (;). However, it is crucial to note that these subdivisions were not exclusively reliant on punctuation marks. Instead, this study followed the principle of dividing the text into lines to make sure that each segment fully expresses the original meaning. Finally, each translated English text was aligned with its corresponding original text.

• This is primarily due to their ubiquity and the negligible unique semantic contribution they make.
• It goes beyond the surface-level analysis of words and their grammatical structure (syntactic analysis) and focuses on deciphering the deeper layers of language comprehension.
• The y-axis represents the semantic similarity results, ranging from 0 to 100%.
• Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation.
• To learn more about such techniques to represent words as vectors and understand the context by analyzing the neighborhood in which the word is used refer to the post, Distributional Semantics | Techniques to represent words as vectors.

Read more about https://www.metadialog.com/ here.