An Introduction to Semantic Matching Techniques in NLP and Computer Vision by Georgian Georgian Impact Blog
![semantic techniques](data:image/jpeg;base64,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)
Semantics is an essential component of data science, particularly in the field of natural language processing. Applications of semantic analysis in data science include sentiment analysis, topic modelling, and text summarization, among others. With this improved foundation in linguistics, Lettria continues to push the boundaries of natural language processing for business. Our new semantic classification translates directly into better performance in key NLP techniques like sentiment analysis, product catalog enrichment and conversational AI. This guide details how the updated taxonomy will enhance our machine learning models and empower organizations with optimized artificial intelligence. All these technologies form a powerful combination that enables computers to understand and process human language more accurately than ever.
It’s a good idea to let users view the deleted subtree and move nodes out of it. The cells are not explicitly created; instead, the state implicitly contains such a semantic techniques cell as soon as its row and column exist. Of course, until a cell is actually used, it remains in a default state (blank) and doesn’t need to be stored in memory.
How Can I Measure The Success Of My Semantic SEO Efforts?
With the PLM as a core building block, Bi-Encoders pass the two sentences separately to the PLM and encode each as a vector. The final similarity or dissimilarity score is calculated with the two vectors using a metric such as cosine-similarity. Semantic matching is a technique to determine whether two or more elements have similar meaning. Semantics (from Ancient Greek σημαντικός (sēmantikós) ‘significant’)[a][1] is the study of reference, meaning, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics and computer science. Sharpen your skills and become a part of the hottest trend in the 21st century.
By leveraging a semantic layer, users can easily identify and retrieve the data they need, regardless of where it is stored. This reduces the need for IT to provide data sets and reports on an ad-hoc basis. In a world of ever-evolving technology and search engine optimization, leveraging sentiment analysis for your website is key. Sentiment analysis has become an increasingly popular method to optimize websites through semantic search. When someone inputs a query into their voice or visual search, the result should be tailored to meet the user’s expectations more accurately. These technologies have enabled us to take our SEO strategies further than ever before as we’re now able to capture meaningful information about user intent beyond simply matching keywords with content on web pages.
Bridging Excellence: The Fusion of Good Manufacturing Practices and Data Science in Industry
Data also plays a critical role in helping inform decisions about what type of content should be created or optimized for better visibility within search engine rankings. For instance, if keyword research reveals that users typically ask questions such as “How do I…” content creators know to focus on creating helpful tutorials instead of static web pages with only facts. Additionally, incorporating advanced techniques such as entity extraction into your strategy allows you to understand the context behind each query and develop solutions accordingly. Search Engine Optimization (SEO) professionals can now access powerful tools to optimize content for specific queries and create targeted campaigns based on user’s needs.
Understanding HTML Techniques for Improving Web Accessibility – MUO – MakeUseOf
Understanding HTML Techniques for Improving Web Accessibility.
Posted: Fri, 14 Apr 2023 07:00:00 GMT [source]