The Themes of NLP

- Overview 

Natural Language Processing (NLP) involves several tasks for extracting information from text, including Theme extraction, which uses part-of-speech patterns to identify and score themes; Sentiment analysis, which determines the emotional tone of text; and Named entity recognition, which identifies and categorizes named entities like people and places. 

Other related themes are Text classification, Topic modeling, Keyword extraction, Text summarization, and Information extraction.

These diverse tasks can be generalized into three core themes: syntax, semantics, and relations. 

• Syntax: The study of sentence structure and grammar.
• Semantics: The study of the meaning of words, phrases, and sentences.
• Relations: The study of how different parts of the text or different texts relate to each other.

- Examples of NLP Themes for Information Extraction 

Examples of NLP themes for information extraction:

• 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. 

- The Fundamental NLP Techniques

• Lemmatization and stemming: These are often the first steps in an NLP project, used for normalizing text data by reducing words to their base or root form (e.g., "running" becomes "run").
• Tokenization: The process of breaking down text into smaller units (tokens), such as words or sentences.
• Normalization: A general process encompassing stemming and lemmatization, ensuring text is in a consistent format.
• Sentence segmentation and phrase identification: Identifying the boundaries of sentences and significant phrases within text.
• Word sense disambiguation: Determining the correct meaning of a word that has multiple possible meanings based on the context in which it appears.
• Parsing: Analyzing the grammatical structure of sentences to understand the relationships between words and their role in the sentence.