The Sense Analysis service determines the meanings of words in context (see Semantics), including both common words and proper nouns. This is often known as Word Sense Disambiguation” (WSD).
It is based on a combination of several machine learning algorithms and rule-based components. It handles well-capitalized documents, poorly-capitalized documents (e.g., tweets, informal documents) and Internet search queries. The generated senses can be correlated to corresponding entities in other well-known namespaces (e.g., Wikipedia).
Here is a visual example of semantic analysis of an ambiguous text by our tools:
Our online cloud API allows you to boost your social media filtering, sharpen your sentiment analysis and get precise automatic text analytics. Get your text analysis now, using c++, java, php, ruby or any language able to communicate through REST services. It’s free for testing or R&D purposes!
Learn about Semantic Analysis
The “Concepts” heading in the sidebar contains several documents that should be read to understand what the tool provides and how to use it.
Getting Started with Sense Analysis
WSD Sample Output
See wsd examples of sense analysis results for random documents and queries. A typical application filtering scheme is used to classify the results returned at each position.
WSD Coding Examples
We provide Coding Examples in a few popular programming languages to get you to a working solution very quickly.