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The DIFFERENCE is that the heuristics for DECISION trees evaluate the average quality of a number of disjointed sets while rule LEARNERS only evaluate the quality of the SET of instances that is covered with the CANDIDATE rule.

The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.

The GENERAL principle of an ensemble METHOD is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a SINGLE MODEL. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used SEQUENTIALLY to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

SEQUENCE learning is a method of TEACHING and learning in a logical MANNER.

Sequence learning is a method of teaching and learning in a logical manner.

• Sequence PREDICTION
• Sequence GENERATION
• Sequence recognition
• SEQUENTIAL decision

PAC (Probably APPROXIMATELY Correct) learning is a learning framework that has been INTRODUCED to analyze learning ALGORITHMS and their STATISTICAL efficiency.

PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.

Dependency PARSING is ALSO known as Syntactic Parsing. It is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated USING some parsing ALGORITHMS. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the SEMANTIC analysis stage.

Dependency Parsing is also known as Syntactic Parsing. It is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated using some parsing algorithms. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the semantic analysis stage.

PRAGMATIC Analysis:

It deals with outside word KNOWLEDGE, which means knowledge that is external to the documents and/or queries. Pragmatics analysis that focuses on what was described as interpreted by what it actually meant, deriving the VARIOUS ASPECTS of LANGUAGE that require real-world knowledge.

Pragmatic Analysis:

It deals with outside word knowledge, which means knowledge that is external to the documents and/or queries. Pragmatics analysis that focuses on what was described as interpreted by what it actually meant, deriving the various aspects of language that require real-world knowledge.

According to The Stanford NATURAL Language Processing Group :

• A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, ADJECTIVE, etc.
• PoS taggers use an algorithm to label TERMS in text bodies. These taggers make more complex CATEGORIES than those defined as basic PoS, with tags such as “noun-plural” or even more complex labels. Part-of-speech categorization is taught to school-age children in English GRAMMAR, where children perform basic PoS tagging as part of their education.

According to The Stanford Natural Language Processing Group :

Tf–IDF or TF IDF stands for term frequency–inverse document frequency. In information retrieval TF IDF is is a NUMERICAL statistic that is INTENDED to reflect how important a word is to a document in a collection or in the collection of a SET.

Tf–idf or TF IDF stands for term frequency–inverse document frequency. In information retrieval TF IDF is is a numerical statistic that is intended to reflect how important a word is to a document in a collection or in the collection of a set.

NLP Terminology is based on the FOLLOWING factors:

Weights and Vectors:

TF-IDF, length(TF-IDF, doc), Word Vectors, Google Word Vectors

TEXT Structure:

Part-Of-Speech Tagging, Head of sentence, Named entities

Sentiment ANALYSIS:

Sentiment Dictionary, Sentiment Entities, Sentiment Features

Text Classification:

Supervised Learning, Train Set, Dev(=Validation) Set, Test Set, Text Features, LDA.

Machine READING:

Entity Extraction, Entity LINKING,dbpedia, FRED (lib) / Pikes.

NLP Terminology is based on the following factors:

Weights and Vectors:

TF-IDF, length(TF-IDF, doc), Word Vectors, Google Word Vectors

Text Structure:

Part-Of-Speech Tagging, Head of sentence, Named entities

Sentiment Analysis:

Sentiment Dictionary, Sentiment Entities, Sentiment Features

Text Classification:

Supervised Learning, Train Set, Dev(=Validation) Set, Test Set, Text Features, LDA.

Machine Reading:

Entity Extraction, Entity Linking,dbpedia, FRED (lib) / Pikes.

Natural Language Processing can be used for

• Semantic Analysis
• AUTOMATIC summarization
• Text classification
• Question Answering

Some real-life example of NLP is IOS Siri, the GOOGLE assistant, AMAZON echo.

Natural Language Processing can be used for

Some real-life example of NLP is IOS Siri, the Google assistant, Amazon echo.

Below are the few major components of NLP.

Entity extraction:

It involves segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc.

Syntactic analysis:

It refers to the proper ordering of words.

Pragmatic analysis:

Pragmatic Analysis is PART of the PROCESS of extracting INFORMATION from TEXT.

Below are the few major components of NLP.

Entity extraction:

It involves segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc.

Syntactic analysis:

It refers to the proper ordering of words.

Pragmatic analysis:

Pragmatic Analysis is part of the process of extracting information from text.

Natural LANGUAGE Processing or NLP is an automated way to understand or analyze the natural languages and extract required information from such DATA by applying MACHINE learning Algorithms.

Natural Language Processing or NLP is an automated way to understand or analyze the natural languages and extract required information from such data by applying machine learning Algorithms.