WHAT IS NATURAL LANGUAGE PROCESSING (NLP)?

Natural Language Processing (NLP) has been one of the most active research areas in data science for years. It is at the intersection of Machine Learning and Linguistics. Its purpose is to extract information and meanings from the textual content. It is exercised in our everyday life:

• Text translation (Deep Learning for example)
• Spell checker
• Automatic content summary
• Vocal synthesis
• Text Classification
• Next word prediction on smartphone
• Extract named entities from text
• Etc.

A subset of NLP is the semantic analysis that uses various computational methods dedicated to human language processing. It is important to differentiate between automatic language processing and semantic analysis.

NLP IS BASED ON DIFFERENT APPROACHES:

• Linguistics, with the a priori establishment of rules by studying a language.
• Statistical, based on the analysis of large corpora, from which the machine will extract rules thanks to machine learning.
• Hybrid, located between linguistics and statistics which allows obtaining better results.

SEMANTIC ANALYSIS TYPICALLY PASSES THROUGH TWO ANALYSIS STAGES:

• Lexical or morphological: allows a text to be divided into lexemes (words and expressions).
• Syntax: relies on grammatical rules to define what functions words have within a text, and the relationships between them (e.g. relationship between subject and object).

The semantic analysis makes it possible to bring structure to the unstructured textual data, in order to extract entities, terms, and relationships.

Bee4sense, OPPSCIENCE‘s data analysis platform integrates advanced indexing and semantic search functions allowing users to implement text mining functions. These technologies operate on three levels:

• Word level (term analysis): the system processes the relevance of specific terms within a documentary corpus, using statistical methods of keyword management.
• The sentence level (morphosyntactic analysis): the system processes the nature of the words in the sentences (noun, verb, adjective, complement, etc.) to help identify information and key entities.
• The speech level: the system treats the text as a graph and analyzes the relationships detected between the entities, regardless of their position in the entire document as well as its length.

In addition, Bee4sense makes it possible to make corrections and to pass them on both at the level of the semantic rules and in the indexed history.

THE MAIN ACCESSIBLE SEMANTIC FUNCTIONS OF Bee4Sense ARE:

• Morphosyntactic analysis: identifies the nature of a term (verb, noun, adjective) and its lemma (standard form which does not take gender, number, and inflected forms into account).
• The search for terms according to their absolute or respective position, or by following navigation criteria within the document.
• The extraction of named entities (organizations, people, functions, places, currency, date, etc.).
• Terminology analysis, i.e. the extraction and structuring of terminologies to identify relevant terms in a specific field and structure them according to simple relationships (computer => desktop computer, laptop).
• Analysis of relationships between concepts (parents/children, subsidiaries, competition, etc.).
• The extraction of facts or events (calendar, political, economic, etc.).
• The analysis of feelings or opinions.
• A categorization module based on a predefined classification plan (supervised classification).
• Document clustering (unsupervised classification).
• Trend analysis (statistical frequency of occurrence of a concept or term on a time scale).