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Semantic Engine
(Part I)

Natural Language Processing and Semantics

To process a text analysis, the Semantic Engine operates in 6 stages:

  1. Sentence and Proposition Hashing,
  2. Ambiguity Solving (with respect to the words of the text),
  3. Identification of Equivalent classes (senses),
  4. Statistics, detection of Bundles and Episodes,
  5. Detection of the Most Characteristic Parts of text,
  6. Layout and display of the result.

Words are grouped together in several main Word categories. Among these, six are of interest to us:

An analysis is highly complex. During the process, the software will:

Propositional hashing (Lexical Analysis)

To simplify the analysis, the Semantic Engine divides the text into propositions (simple sentences). This first stage is based on a scrutiny of the punctuation, and on a complex process of syntax analysis, which will not be detailed here. This yields highly reliable co-occurrence statistics (Relations), since it is not possible for two words to fit into the same grammatical proposition if they are not closely connected. Propositional hashing is bound to involve errors (propositions that are either too short or too long), but this does not affect the results.

Ambiguity solving (Semantic and Lexical Analysis)

The automatic interpretation of words in any living language, either written or spoken, requires the solving of numerous ambiguities:

One of the main functions of this software is to solve these ambiguities by means of a set of Artificial Intelligence problem-solving algorithms. Though a perfect result is impossible to achieve, the error rate is low enough to guarantee an accurate analysis of your text.

Read the next part:
Word Categories and Semantics


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