Advanced Concepts, Methods, and Applications in Semantic Computing: 9781799866978: Computer Science & IT Books

Data privacy and security pose significant concerns, as semantic analysis requires access to large volumes of text data, potentially containing sensitive information. It is imperative that organizations handle and protect user data responsibly, ensuring compliance with privacy regulations and implementing robust security measures.Bias and fairness are additional ethical considerations in semantic analysis. AI models are trained on historical data, which may contain biases or reflect societal inequalities. It is crucial to address and mitigate biases to ensure that AI systems provide fair and unbiased analysis and decision-making.Additionally, transparency and explainability are important facets of ethical AI. Users should have insight into how AI systems interpret and analyze their data, and AI developers must strive to create models that are interpretable and provide understandable explanations for their decisions.

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. Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022.

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Semantic search is an advanced information retrieval technique that aims to improve the accuracy and relevance of search results by... Document retrieval is the process of retrieving specific documents or information from a database or a collection of documents. In the ever-evolving landscape of artificial intelligence, generative models have emerged as one of AI technology's most captivating and... 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. BERT-as-a-Service is a tool that simplifies the deployment and usage of BERT models for various NLP tasks.

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].

Similar to Semantic Analysis: theory, applications and use cases

Semantic analysis is a branch of general linguistics which is the process of understanding the meaning of the text. The process enables computers to identify and make sense of documents, paragraphs, sentences, and words as a whole. Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans.

For this, the language dataset on which the sentiment analysis model was trained must be exact and large. Semantic Technology defines and links data on the Web (or within an enterprise) by developing languages to express rich, self-describing interrelations of data in a form that machines can process. Thus, machines are not only able to process long strings of characters and index tons of data. They are also able to store, manage and retrieve information based on meaning and logical relationships.

The age of getting meaningful insights from social media data has now arrived with the advance in technology. The Uber case study gives you a glimpse of the power of Contextual Semantic Search. It’s time for your organization to move beyond overall sentiment and count based metrics. Companies have been leveraging the power of data lately, but to get the deepest of the information, you have to leverage the power of AI, Deep learning and intelligent classifiers like Contextual Semantic Search and Sentiment Analysis. At Karna, you can contact us to license our technology or get a customized dashboard for generating meaningful insights from digital media.

These findings suggest generalized topic areas, as well as identify subgroups who are more likely to provide additional information in their response that may add insight into future epidemiologic and military research. It is generally acknowledged that the ability to work with text on a semantic basis is essential to modern information retrieval systems. As a result, the use of LSI has significantly expanded in recent years as earlier challenges in scalability and performance have been overcome. LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri[20] in the early 1970s, to a contingency table built from word counts in documents.

• ② Make clear the relevant elements of English language semantic analysis, and better create the analysis types of each element.
• All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost.
• Over time, you'd understand not just the words and their definitions but also the deeper meaning behind sentences, the context, the nuances, and the emotions.
• Previous analyses on military populations used human assisted computer analysis, but generally had less sophisticated methodologies [21].

For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. 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. Communicating a negative attitude with backhanded compliments might make sentiment analysis technologies struggle to determine the genuine context of what the answer is truly saying. Semantic Technology helps enterprises discover smarter data, infer relationships and extract knowledge from enormous sets of raw data in various formats and from various sources.

Many methods of natural language processing have been applied to biological sequences. The N-grams of whole genome protein sequences have been analyzed and some statistical features have been extracted (Ganpathiraju et al., 2002). The probabilistic models from speech recognition have been employed to enhance the protein domain discovery (Coin et al., 2003).

• By identifying relevant events and trends that may affect stock prices or market conditions, semantic analysis can help investors make more informed decisions and potentially improve their returns.
• The sentences of corpus are clustered according to the length, and then the semantic analysis model is tested with sentences of different lengths to verify the long sentence analysis ability of the model.
• This allowed for identifying semantic similarities among open text responses to determine clusters of responses with high contextual similarity (e.g., noting that "welding fumes" and "asbestos" have similar meaning within the context of this study).
• It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites.

We help businesses and companies build an online presence by developing web, mobile, desktop, and blockchain applications. Interestingly, news sentiment is positive overall and individually in each category as well. In the initial analysis Payment and Safety related Tweets had a mixed sentiment.

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A more focused, sympathetic response to consumers may come from this practice. Also, one may examine a client’s interactions across different platforms using this information and make the required adjustments because, at times, people react more favorably than others on some channels. Polysemy refers to a relationship between the meanings of words or phrases, although slightly different, and shares a common core meaning under elements of semantic analysis. Lexical semantics plays an important role in semantic analysis, allowing machines to understand relationships between lexical items like words, phrasal verbs, etc. But before getting into the concept and approaches related to meaning representation, we need to understand the building blocks of semantic system.

By analyzing the meaning and context of words and sentences, semantic analysis empowers AI systems to extract valuable insights from textual data. In this article, we will delve into the intricacies of semantic analysis, exploring its key concepts and terminology, and delving into its various applications across industries. The impact of semantic analysis transcends industries, with various sectors adopting AI-driven language processing techniques to enhance their operations. In customer service, sentiment analysis enables companies to gauge customer satisfaction based on feedback collected from multiple channels. As AI technology continues to advance, we can anticipate even more innovative applications of semantic analysis across industries. Based on a review of relevant literature, this study concludes that although many academics have researched attention mechanism networks in the past, these networks are still insufficient for the representation of text information.

It is a very active research direction in the field of machine learning, and has many important practical applications. This paper mainly studies the application of semantic analysis in text classification of computer technology. Experimental results show that the proposed method can effectively reduce the dimension of feature space and improve the performance of text classification.

Natural Language Processing in News Classification: Unleashing the Power of AI in Media

We anticipate the emergence of more advanced pre-trained language models, further improvements in common sense reasoning, and the seamless integration of multimodal data analysis. As semantic analysis develops, its influence will extend beyond individual industries, fostering innovative solutions and enriching human-machine interactions. The Apache OpenNLP library is an open-source machine learning-based toolkit for NLP. It offers support for tasks such as sentence splitting, tokenization, part-of-speech tagging, and more, making it a versatile choice for semantic analysis. The synergy between humans and machines in the semantic analysis will develop further.

Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. Once you’ve had a chance to be blown away by the results, share your sentiment and keyword dashboard with the rest of your team (just click on the ‘share’ button in the top right-hand corner). Analyze the positive language your competitors are using to speak to their customers and weave some of this language into your own brand messaging and tone of voice guide. Process unstructured data to go beyond who and what to uncover the why – discover the most common topics and concerns to keep your employees happy and productive. Listening to the voice of your customers, and learning how to communicate with your customers – what works and what doesn’t – will help you create a personalized customer experience. Sentiment analysis can read beyond simple definition to detect sarcasm, read common chat acronyms (lol, rofl, etc.), and correct for common mistakes like misused and misspelled words.

Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. With search engines increasingly relying on semantic analysis, implementing effective search engine optimization (SEO) strategies becomes paramount. By aligning their strategies with semantic analysis principles, they can ensure that their content resonates with both users and search algorithms, leading to greater visibility and organic traffic. The financial sector is another area where semantic analysis is making a significant impact. Financial institutions are increasingly using AI-based text understanding techniques to analyze news articles, social media posts, and other text data to inform their investment strategies. By identifying relevant events and trends that may affect stock prices or market conditions, semantic analysis can help investors make more informed decisions and potentially improve their returns.

This cross-sectional investigation is part of the larger Millennium Cohort Study, which was designed in the late 1990s to determine how military service may affect long-term health [6]. The probability-based sample, representing approximately 11.3 percent of the 2.2 million men and women in service as of October 2000, was provided by the Defense Manpower Data Center (DMDC) in California. Of the 77,047 individuals who enrolled (36 percent response rate) from July 2001 to June 2003 in Panel 1, 55,021 (71 percent follow-up rate) completed the first follow-up questionnaire between June 2004 and February 2006. In addition to Panel 1, the invited participants of Panel 2 were randomly selected from military personnel with 1 to 2 years of service as of October 2003, and 31,110 enrolled (25 percent response rate). Marines and women were over sampled in this panel in order to ensure sufficient power among women as well as the most likely group of combat deployers. This investigation began with 108,157 consenting participants who completed a questionnaire from either Panel 1 (baseline and/or follow-up) or Panel 2 baseline.

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