The Themes of NLP

- Overview 

Natural Language Processing (NLP) has several themes that involve extracting information from text, including:

• Theme extraction: Uses part-of-speech patterns to extract noun phrases as themes, and then scores their relevance using lexical chaining. This can help identify trends, understand people's feelings, and differentiate between opinions.
• Sentiment analysis: Analyzes words in a text to determine its overall sentiment, which can be categorized as positive, negative, or neutral.
• Named entity recognition: Helps machines identify and categorize named entities in text data. This can improve the efficiency of information extraction and has many applications across industries.
• Text summarization: Summarizes a text, such as a paragraph or document, into a shorter text, such as a sentence, paragraph, or a few words.
• Text classification: Helps organize and categorize text to make it easier to use and understand. For example, this can be used to label tasks by urgency or automatically identify negative comments.
• Topic modeling: Uses algorithms to identify the main topics or themes in a large text collection. The algorithms analyze how often words appear together and group them based on similarities.
• Keyword extraction: Identifies the most important words or phrases in a piece of text. This can be used to extract themes and key information for content analysis, search engine optimization (SEO), and topic modeling.
• Information extraction: Automatically extracts structured information from unstructured or semi-structured text data. For example, Spark NLP's RegexMatcher allows users to define regular expressions to extract specific patterns from text data. 

Even though the study of NLP covers a diverse range of tasks, most of them can be generalized to three themes: syntax, semantics and relations. 

[More to come ...]