Natural processing Language :
Basically, this kind of language processing focuses on developing algorithms that are mostly language specific or ‘perform well only on closed-domain text’.
The tutorials given below takes a detailed view of several human language technical aspects and current approaches that use Natural Processing Language for web specific knowledge tasks. Tutorials :
Research at Google : everything at one place [check here]
There are 231 publications on NLP. Applications such as information extraction, machine translation, sentiment analysis and question answering needs both the syntactic and semantic analysis.
At Google, they support numerous Google products in multiple languages at web-scale to solve these problems.
The site is concentrating to develop algorithms to predict part-of-the speech in syntactic level. And on the other hand, on semantic level, they focus on problems such as noun-phrase extraction.
The tutorials published by Google covers all the current aspects of NLP.
It would be simpler to you to go through all these tutorials as it covers most of the syllabus.
At Data Community [check here]
There are 2 parts in the tutorial given by Benjamin Benqfort. This article mainly deals with the introduction to Natural Language processing. And then the PART 1- Big Data and NLP.
PART 2- The “Foo” of Big Data
By the end of the tutorial you will learn about “the magic of Big Data”.
Getting started on Natural Language processing with Python [check here]
This is a pdf file. The tutorial not only deals with the introduction on NLP also with its primary features and to illustrate the articles it uses Python programming language.
[If you are unfamiliar with Python Programming, here is a link to those tutorials [check here]
and there are few other references for Python programming language at the end of the article
Programmers [check here]
This is a forum where you are able to post your queries and get answered by experts or other programmers. You will have an opportunity of learning other opinions about a particular algorithm or anything for that matter. You could get more resources for solving your question. Having group discussion and participating might help you in implementing your idea in a better way.
Natural Language Processing Tutorial [check here]
This is by Vik Paruchuri.
This article will serve you as an introduction to NLP. You will be travelling from tokenization to feature extraction to create a model using machine language algorithm.
The given examples in this code are done in R. They can be easily translated into other languages. You can also get other sources of this particular post in github.
ACM Digital Library [check here]
A great source of knowledge. Their main aim of the article is to investigate whether the previous findings of the tutorial dialogue can be generalized using different domains.
They first present methods based on unifying their prior coding and analysis methods. Second, they show you their complete collection of prior findings regarding student dialogue behaviors and learning. Not only generalizing across corpora, but their system yields always new findings. Lastly, they throw light on the main subject of Natural language processing that can be used to automate some of these analysis.
Natural Language Processing for the Semantic Web tutorial [check here]
This tutorial will be outlined into 3 parts.
Introduction to ontologies and the Semantic web. This particular section mainly covers the foundation of the tutorial, an introduction to the Semantic web, describing various types of ontologies and their role for Natural Language Processing developers.
Natural Language Processing and ontology engineering.
This section will deal with the further concept of ontology engineering and describe the ways in which Natural Language Processing can be used. This section will cover the use of controlled natural language, the representation of linguistic in ontology and few other techniques for better ontology learning from unstructured text. And moreover, this section also covers a brief challenges for Natural Language Processing such as the use of more expressive ontology, handling of logical inconsistencies and other ontology techniques.
Finally, it shall deal with the illustration on the techniques with examples of real research applications. They mostly will cover topics such as ontology- based isolation of information, semantic annotation and overall knowledge of ontology.
Since all the NLP tools are to be presented in open source, this tutorial will provide you with the required skills which are easy to apply and not require any special software. The article is targeted on the audience looking to proceed into semantic web applications, to learn about the role of ontology and it is used. For this tutorial, no previous knowledge of semantic web or of ontology is necessary.
Natural Language Processing [check here]
It’s a free video tutorial on NLP. The site consists of 40 videos talking from introduction, stages of NLP to probabilistic parsing and algorithms.
These video lectures are by IIT Bombay, India under the program NPTEL.