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    <value>These are the basic Natural Language Processing (NLP) techniques to extract data from text are:
    
    Text PreProcessing (Stop Word Removal, Text Normalization)
    Tokenization (Ngrams, Bigrams, Trigrams)
    Named Entity Recognition (Entity Lists)
    Keyword Detection. (word frequency)(Term Document Matrix)
    Topic Modeling 
    Sentiment Analysis (polarity)
    Document Classification (Topic / Polarity / Sentiment)
 
Document Analysis (corpus)
(To be broken into Paragraphs) Identified by a Blank Newline
(To be broken into Sentences) Identified by a Full Stop. Each Document to be analyzed individually then compared with other documents in the Corpus. Paragraphs can be compared for similarity, as well as their topical match; 
i.e.: Identify documents which speak about a set of specific topics, Word frequency can also discover similar authors.

Sentence Analysis (detailed)
Sentences can be analyzed as a whole; as it identifies a single thought or idea. Sentences can be broken into Components, Such as Subject/ Predicate, or Noun Phrase/Verb Phrases, or N-grams (unknown chucks)Sentences can be identified as a standard Sentence type, Such as declarative or Command or Exclamatory or interrogative etc., or custom sentence type or intention such as, Request, Enquiry, Answer, Question, Description, Sentimental, Locational, Event planning etc.

Text Pre-Processing
Required to remove unwanted elements from the text, 
Required to normalize text to a universal format.

Stop-Word Removal:
The removal of commonly used words which are only used to create a sentence such as, the, on, in of, but

Tokenization 
For example, consider a simple sentence: "NLP information extraction is fun''. This could be tokenized into;

    One-word (sometimes called unigram token): NLP, information, extraction, is, fun
    Two-word phrase (bigram tokens): NLP information, information extraction, extraction is, is fun, fun NLP
    Three-word sentence (trigram tokens): NLP information extraction, information extraction is, extraction is fun


Named Entity Recognition:
Extracting the entities in the text is the most fundamental and useful approach in NLP. 
It emphasizes the text's most important ideas and references.
It entails identifying important information in a text and categorizing it into a set of predetermined categories. 
Named entity recognition (NER) extracts entities from text such as individuals, places, organizations, dates, and so on.

Entity Extraction
Extract Entities from text using Named Entity Recognition (NER). NER labels sequences of words in a text which are the names of things, such as person and company names. This implementation labels 3 classes: person, organization and location.
By creating lists on specific topics such as a list of names / Locations , Organizations etc. These can be used to identify these words in the text, as the entity type. The entitys extracted can be extracted or the sentence containing the entity. 

Entity Discovery
Entity extraction and entity lists can also be used to discover patterns in sentences. such as ;

&lt;Person&gt; founded &lt;company&gt;, on &lt;date&gt;  at &lt;location.

Such sentences can be transposed into patterns for Entity Discovery, 

#A founded #B 

#A = Unknown Person
#B = Unknown Company

On #A at #B

A# Unknown Date Pattern
#B Unknown Location

Sentences or Phrases Captured contain targeted data which can be associated with a question which can be used for retrieval later, from the knowledge base built.

User Supply's Single information
Who Founded the company = &lt;PERSON&gt;, &lt;Stored Sentence&gt;.
When was the &lt;company&gt; founded = &lt;DATE&gt;, &lt;Stored Sentence&gt;.
Where was the &lt;comapny&gt; founded = &lt;LOCATION&gt;, &lt;Stored Sentence&gt;.

User Supply's multiple information. 
Where did &lt;person&gt; found &lt;company&gt; = &lt;LOCATION&gt;, &lt;Stored Sentence&gt;.
When did &lt;person&gt; Found &lt;Company&gt; = &lt;DATE&gt;, &lt;Stored Sentence&gt;.


KeyWord Extraction:
Keyword extraction extracts relevant terms and phrases from within a text. These are terms that help to summarize the text, are significant to the writer’s viewpoint, or significant to the overall concept of the text.
Here A list is created of keywords similar to entity lists, But these will be based around a specific topic or theme or concept. these keyword lists when a term is detected from the list can identify the topic being targeted. 
This technique is used in topic modeling and sentiment detection. Once a topic is detected the keyword can be exposed (extracted) or the whole sentence containing the keyword Extracted as a predicate or phrase which is also targeted to the detected topic.

Word Frequency (keyword Extraction)
Word frequency consists of listing the words and phrases that repeat the most within a text. this enables for the discovery of keyword and phrase lists. Keyword extraction methods often make use of linguistic information about texts and the words they contain. 
Sometimes, morphological or syntactic information (such as the part-of-speech of words or the relations between words in a dependency grammar representation of sentences) is used to determine what keywords should be extracted.

Word Collocations and Co-occurrences
Also known as N-gram statistics, word collocations and co-occurrences help understand the semantic structure of a text and count separate words as one.
Collocations are words that frequently go together. The most common types of collocations are bi-grams (two terms that appear adjacently, like ‘customer service’, ‘video calls’ or ‘email notification’) and tri-grams (a group of three words, like ‘easy to use’ or ‘social media channels’).
Co-occurrences, on the other hand, refer to words that tend to co-occur in the same corpus. They don’t necessarily have to be adjacent, but they do have a semantic proximity.

TF-IDF
TF-IDF stands for term frequency–inverse document frequency, a formula that measures how important a word is to a document in a collection of documents.
This metric calculates the number of times a word appears in a text (term frequency) and compares it with the inverse document frequency (how rare or common that word is in the entire data set).
Multiplying these two quantities provides the TF-IDF score of a word in a document. The higher the score is, the more relevant the word is to the document.

Sentiment analysis
The most basic sentiment analysis result is a three-point scale: positive, negative, and neutral. 
In more complicated circumstances, the result might be a numeric score that can be categorized into as many categories as needed.

Topic Modeling 
Lists can be used with associated keywords; Such as Cat, Fur, Legs, and Mammals etc. to create an associated topic list. These lists can be used to identify the "Topic or Sentiment" of the sentence as a whole. This can also be useful to identify groups of general information which cannot be chunked. Such captured sentences or statements can be used to return general information discovered on a specific topic. 
If a user requested data all data pertaining to a particular topic it can be retrieved as a grouped result of phrases or predicates collected associated with the topic. 



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