How does Semantics Analysis Work?

 

Automated semantic analysis works with the help of machine learning algorithms. 
By feeding semantically enhanced machine learning algorithms with samples of text, you can train machines to make accurate predictions based on past observations. There are various sub-tasks involved in a semantic-based approach for machine learning, including word sense disambiguation and relationship extraction:

This method begins with a language-independent step of analyzing the set of words in the text to understand the dictionary meanings and their definition. This step is termed ‘lexical semantics‘.
Subsequently, words or elements are parsed. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings.

Upon word parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms. 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.

For example, ‘Blackberry is known for its sweet taste’ may directly refer to the fruit, but ‘I got a blackberry’ may refer to a fruit or a Blackberry product. As such, context is vital in semantic analysis and requires additional information to assign a correct meaning to the whole sentence or language.
Semantic analysis also takes into account signs and symbols and collocations (words that often go together). 

Technically, semantic analysis involves:
Data processing.
Defining features, parameters, and characteristics of processed data
Data representation
Defining grammar for data analysis
Assessing semantic layers of processed data
Performing semantic analysis based on the linguistic formalism


For us humans, there is nothing more simple than recognizing the meaning of a sentence based on the punctuation or intonation used. But for machines, the challenge is scale. And that’s where semantic analysis tools are particularly useful. 
Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. They allow computers to analyze, understand and treat different sentences.