STANDARD NLP PROJECTS

STANDARD NLP PROJECTS

Standard NLP Projects, NLP referred as natural language processing to understand the human language. We offer standard NLP projects for undergraduate, post graduate students, researchers, scientists and programmers. We implement NLP academic projects to introduce new language processing algorithm which permit computer to process and understand human language. Voice or text is given as input for this project. We implemented more than 75+ project such as machine translation, sentence understanding, probabilistic parsing, tagging, bio medical information extraction; automatic question and warning are the application developed by NLP projects. The main aim of standard NLP projects is to ensure robust, broad coverage of natural language processing in various languages.
STANDARD NLP PROJECTS

CLASSIFICATION IN NLP:

We implement classification methods to find which class belongs to particular instances. It includes part of speech classification, fluency ranking, authorship attribution are deal with NLP projects. We support maximum entropy and Naive Bayes classification.

PROBLEMS IN NLP:

The major challenging problem in NLP is undertaken by us in M.Tech projects are understanding and generating human language. We implement basic NLP to support English language which permits same word with various meaning. Differentiating homonym is the major process in NLP. Complexity happened in language irregularity and different ambiguity for simple sentence.

STEPS INVOLVED IN NLP PROCESS:

We undergone various steps to process the NLP in an efficient way based on Springer papers are:

  • MORPHOLOGICAL PROCESSING:

It is an initial step in NLP which collect input as sentence from user (or) other computing devices.

  • SYNTAX ANALYZING:

In this phase we analyze the input sentence based on lexicon and grammar.

  • SEMANTIC ANALYSIS:

We follow some semantic rules to understand the part of speech.

  • PRAGMATIC ANALYSIS:

It is a last step to generate NLP. It transforms the original input information into contextual valid information.

PACKAGES USED IN NLP PROJECTS:

We import various packages to develop NLP projects and they described as:

  • OPENNLP TOOL:

It contains a set of NLP tool which utilized by maxent packages. It mainly used for detect sentence, shadow and full syntactic parser, tokenize and named entry detector.

  • MAXENT:

We refer maxent as Maximum entropy model and use this package as matured java training package.

  • CCG LIBRARY:

It denoted for combinatory categorical grammar to support parsing and realization process.

  • WORD FREAK:

It like a java based annotation tool.

  • OPENNLP:

Composed various set of NLP components and tools.

STANDARD NLP PROJECTSADVANTAGE:

We implement NLP advantages as:
  • No previous training is required to process NLP
  • Reduce the burden of learning syntax
  • Convenient to understand human language and reply as human understandably format.

STANDARD NLP PROJECTS DISADVANTAGES:

We overcome the following measures in our projects are:
  • More keystrokes are required.
  • Output accuracy is unpredictable.
  • This process requires detail clarification about human speech.
  • Language is pronounced properly to get an accurate result.
  • Text data input retrieve better output whereas voice data input receive some
unpredictable result / output.

STANDARD NLP PROJECTSAPPLICATION:

We implement NLP applications in our projects are:
  • Machine Learning and Translation
  • Question and Answering
  • Text Mining
  • Online Dictionary Spelling Correction
  • Story Understanding System
  • Text Categorization and E-Mail Routing

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