Semantic and Syntax analysis are two main techniques used with natural language processing.

The syntax, an arrangement of words in a sentence to make grammatical sense. Natural Language Processing uses syntax to assess meaning from a language based on grammatical rules. Syntax techniques include:

Parsing. It is the grammatical analysis of a sentence. Example: A NLP algorithm is fed the sentence, “The dog barked.” Parsing involves breaking the sentence into parts of speech — i.e., dog = noun, barked = verb. This is useful for more complex downstream processing tasks.

Word segmentation. It is the act of taking a string of text and deriving word forms from it. Example: Person scans a handwritten document into a computer. The algorithm would be able to analyze the page and recognize the words and are divided by white spaces.

Sentence breaking. The places sentence boundaries in large texts. Example: A NPL algorithm is fed the text, “The dog barked. I woke up.” The algorithm can recognize the period and that splits up the sentences using sentence breaking.

Morphological segmentation. It divides the words into smaller parts called morphemes. Example: The untestable word would be broken into [[un[[test]able]]ly], where the algorithm recognizes “un,” “test,” “able” and “ly” as morphemes. It is especially useful in machine translation and speech recognition.

Stemming. It divides the words with inflexion in them to root forms. Example: In the sentence, “The dog barked,” this algorithm would be able to recognize the root of the word “barked” is “bark.” This would be very useful if a user was analyzing the text for all instances of the word bark, as well as all of its conjugations. The algorithm can be see that they are essentially the same word even though the letters are different.

Semantics involves the use in the meaning behind words. NLP applies the algorithms to understand the meaning and structure of sentences. Semantics techniques include:

Word sense disambiguation. It derives the meaning of a word based on context. Example: The sentence consider, “The pig is in the pen.” The word pen has different meanings with using algorithm this method can understand that the use of the word pen here refers to a fenced-in area, not a writing implement.

Named entity recognition. It determines words that can be categorized into groups. Example: An algorithm using this method could analyze the semantics of the text, it would be able to differentiate between entities that are visually the same. In this sentence, “Daniel McDonald’s son went to McDonald’s and ordered a Meal,” the algorithm can recognize the two instances of “McDonald’s” as two separate entities — one a restaurant and one a person.

Natural language generation. This uses a database to determine the semantics behind words and generate a new text. Example: The algorithm automatically write a summary of findings from a business intelligence platform, mapping certain words and phrases to features of the data in the BI platform.