Syntax and semantic analysis are two main techniques used with natural language processing. Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules. Syntax techniques used include parsing (grammatical analysis for a sentence), word segmentation (which divides a large piece of text to units), sentence breaking (which places sentence boundaries in large texts), morphological segmentation (which divides words into groups) and stemming (which divides words with inflection in them to root forms).
Semantics involves the use and meaning behind words. NLP applies algorithms to understand the meaning and structure of sentences.
Techniques that NLP uses with semantics include word sense disambiguation (which derives meaning of a word based on context), named entity recognition (which determines words that can be categorized into groups), and natural language generation (which will use a database to determine semantics behind words).
Recent approaches to NLP are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. Deep learning models require huge amounts of labeled data to train on and identify relevant correlations, and assembling this kind of big data set is a hurdle.
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Earlier approaches to NLP involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers’ intent from many examples,similar to how a child would learn human language.
Three tools used commonly for NLP include NLTK, Gensim, and Intel NLP Architect. NTLK, Natural Language Toolkit, is an open source python modules with data sets and tutorials.
Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is also another Python library for deep learning topologies and techniques.