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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.
Below are the few major components of NLP.
It involves segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc.
It refers to the proper ordering of words.
Pragmatic Analysis is part of the process of extracting information from text.
Natural Language Processing can be used for
Some real-life example of NLP is IOS Siri, the Google assistant, Amazon echo.
NLP Terminology is based on the following factors:
Weights and Vectors:
TF-IDF, length(TF-IDF, doc), Word Vectors, Google Word Vectors
Part-Of-Speech Tagging, Head of sentence, Named entities
Sentiment Dictionary, Sentiment Entities, Sentiment Features
Supervised Learning, Train Set, Dev(=Validation) Set, Test Set, Text Features, LDA.
Entity Extraction, Entity Linking,dbpedia, FRED (lib) / Pikes.
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.
According to The Stanford Natural Language Processing Group :
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.
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.
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.
Sequence learning is a method of teaching and learning in a logical manner.
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 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.
Natural Language Processing Related Tutorials
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