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Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications.
Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before.
In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.
Here are some syntax techniques that can be used:
Lemmatization: It entails reducing the various inflected forms of a word into a single form for easy analysis.
Morphologicalsegmentation: It involves dividing words into individual units called morphemes.
Word segmentation: It involves dividing a large piece of continuous text into distinct units.
Part-of-speech tagging: It involves identifying the part of speech for every word.
Parsing: It involves undertaking grammatical analysis for the provided sentence.
Sentence breaking: It involves placing sentence boundaries on a large piece of text.
Stemming: It involves cutting the inflected words to their root form.
Semantics refers to the meaning that is conveyed by a text. Semantic analysis is one of the difficult aspects of Natural Language Processing that has not been fully resolved yet. It involves applying computer algorithms to understand the meaning and interpretation of words and how sentences are structured.
Here are some techniques in semantic analysis:
Named entity recognition (NER): It involves determining the parts of a text that can be identified and categorized into preset groups. Examples of such groups include names of people and names of places.
Word sense disambiguation: It involves giving meaning to a word based on the context.
Natural language generation: It involves using databases to derive semantic intentions and convert them into human language.