Singularity in Natural language Processing

The Singularity is very old phenomena in Science and physics. For example electromagnetism shows singularity behavior between the electricity and magnetism. Similarly, Ebert Einstein later on, came up with the string theory. He combined all four different fundamental forces(gravitational, electromagnetic, weak and strong interactions) together and explained all with one formula. The Singularity phenomena brings the simplicity and independence in system. The physics and natural language are not much different. As researchers look both of them in terms of mathematics. You can treat the collations unit, word group as mass and attractions between the words can be treated as semantics which revolve around them and distance(Physics) is equivalent to word, sentence difference and even syntax.
Coming to the point, we have n-numbers of natural language tools, which extract or tag different type of information. How intelligently can we used maximum of them together. The whole system architecture some times depends on the order of execution and what are all the resources and tools one going to used. Can't we used them in parallel? Can't we able to design the perfect data structure which hold all type of information and which has fundamental operations like update, insert and delete? A data-structure which can hold the information at word level, word group level, parse tree level, sentence level. A data-structure which can hold information given be basic NLP toolkit like tagger, morph-analyzer to complex NLP toolkit like information extraction. A data-structure where we can map different resources like word net, frame net, propbank and others.

Key Phrase Extraction tools

Key Phrase Extraction is used to extract most frequent words which are significant with respect to the applications. Key phrase extraction are most frequently used in search engine for advertisement. Some analysts also project Key phrase as topic/concept/short summery. Some of the tools for key phrase extractions are
1)Carrot2:- It is a great tool for key phrase extraction. It uses two algorithm STC and lingo. Lingo search complete key phrase with some other constraints key phrase. STC is kind of Suffix Trie. Lingo works better than STC. With STC, lingo it also uses TF-IDF, LSA. If you more number of related documents then carrot works great. It also provides flexibility in input. You can give input as indexed documents which are indexed from Nabble, Solr, google search desktop or you can also index yourself from XML.
2)KEA:- It is standard algorithm for Key phrase extraction. It provides provision of learning from RDF dictionary(in SKOS format). The dictionary will contains hierarchical taxonomy. It also gives options for Machine learning. It uses Weka for Machine learning. The document can be less in number but should have large in size. Right now it is plugged in GATE. If you don't  use RDF dictionary or large sized documents for training then this tool will not work well.
3)Maui:- It is basic KEA(mentioned above) tool but also gives options to boost taxonomy from Wikipedia.
4)wikiFier:- Like Maui, It also uses wikipedia to boost concept for Key phrase extraction.
5)Stanford topic Modeling tool:- The tools uses LDA for learning topic. It takes input and output in CSV format. It also provides options for Machine learning.
6)Mallet:- It is similar to Stanford, which is used for learning topic words.

Anaphora resolution Tools

Anaphora resolution is the core linguistic problem, it is defined to identify the antecedent of pronouns and nouns. I still remember the computational linguistic-2 class where Dr. Laxmi madam, used to teach such types of typical linguistics problems, sometimes with no answers how computer science can solve the hard-coded linguistics. Anyways in current world many tools try to approach the problem of anaphora resolution but still results are not much promising. I will talk about some of the tools in this post.
1) GuiTAR:- First of all sorry to music lover. This tool is not for you guys. The tool comes in three versions. It uses the Charniak's parser input in Minimally associated XML format(MAS-XML). It mark the output in XML(with tags. It uses generic discourse model which uses partial implementation of Mitov approach for anaphora resolution which is one of oldest approach for anaphora resolution. It also uses Viera & Posesio (Definite Description)DD algorithm which gives it a significant improvement. It also gives lexical chains.
2) BART:- It is John Hopkins University Tool for anaphora resolution. It comes up with Machine learning model and build up in java. Like GuiTAR, BART also take input/output in XML format. It uses alternative Parser/NER for pre-processing.
One can use YumCha or Charniak's Parser and Caracafe/Stanford NER for NER purpose. Similarly it provides flexibility in ML models. One can uses Weka/Maxent/SVM-light.
I suggest you this tool if you good amount of training corpus for ML models.
3) MARS:- It is build on Mitov principle. It uses certain linguistic rules. If your data has noises, then I will not suggest you to use this tool.
4)JAVA RAP:- It is anaphora resolution tool, working on Lipin and Leass rules. It identifies 3rd person pronoun antecedents. Like BART and GuiTAR, it also uses Charniak's parser.

Natural Language Processing tools

Here I will share some of the natural language processing(NLP) open source tools, information extraction, NER tools.

1. NLP Package

• NLTK:- . It is python package which is facilitated by many tools and resources.It is easy to use, as it is designed in python.It is provided with book and tutorial. The tools are mainly used for tagging, chunking, splitting, tokenizer and for other functions. It is also provided with resources and readers to interpet the resources.
• GATE:- The Gate is the java based tool which is mainly used for corpus creation(known as GATE document), chunking, tagging, NER and information extraction. Many packages and tools are plugged in with GATE. It supports pattern based grammars, so it can be used for languages which don't have much resources.It is used for corpus annotation, information extraction, NER, chunking, tagging(mainly grammar based pattern, which are written in java)
• Stanford package:- The package contains CRF based tagger, NER, topic modeling(using LDA) and classifier. The stanford parser gives both dependency and pharse structure parser output.
  
• Opennlp toolkit:- The tool kit is written in JAVA. It supports chunking, tagging and basic NLP application.
  
• Montylingua Package:- The package is used for chunking, tagging, splitting, tokenizing. Their claim is that they use common-sense which is in form of grammar. But their common sense is hard to understand at least for me. The package is provided with java and python.
  

• Mallet package:- It is machine learning toolkit for classification, Sequence tagging and topic modeling. Sequence tagger can be used for any type of tagging application. Topic Modeling tool uses LDA.
  

2. POS-Tagging

• MBT Tagger:- It is memory based tagger and generator.
• TreeTagger:-A decision tree based tagger from the University of Stuttgart (Helmut Scmid). It's language independent, but comes complete with parameter files for English, German, Italian, Dutch, French, Old French, Spanish, Bulgarian, and Russian. It uses language. It also uses lemma and other morph information to disambiguate the POS-tag.
  
• SVM Toolkit:- is tagger using SVM light. SVM is machine learning algorithm. It is trained on walt-street journal corpus.
  
• Accopost:- Open source C taggers originally written by by Ingo Schröder. Implements maximum entropy, HMM trigram, and transformation-based learning. C source available under GNU public license.
• MXPOST:- Java POS tagger. A sentence boundary detector (MXTERMINATOR) is also included
• FnTbl:- A fast and flexible implementation of Transformation-Based Learning in C++. Includes a POS tagger, but also NP chunking and general chunking models.
• MuTBl:- An implementation of a Transformation-based Learner (a la Brill), usable for POS tagging and other things by Torbjörn Lager.
• YamCha:-SVM-based NP-chunker, also usable for POS tagging, NER, etc. C/C++ open source. Won CoNLL 2000 shared task.
• Lingua Tagger:- Perl POS tagger.
• TnT POS tagger:- Trainable for various languages, comes with English and German pre-compiled models. The tagger uses vertibi algorithm for second order Marchov model.
  
• AMALGAM Tagger:- The AMALGAM Project also has various other useful resources, in particular a web guide to different tag sets in common use. The tagging is actually done by a (retrained) version of the Brill tagger (q.v.).

3. Named Entity Recongnation and Information Extraction:-

• Stanford NER:- A Java Conditional Random Field sequence model with trained models for Named Entity Recognition. Java. GPL. By Jenny Finkel.
• Lingpipe:-Tools include statistical named-entity recognition, a heuristic sentence boundary detector, and a heuristic within-document coreference resolution engine. Java. GPL. By Bob Carpenter, Breck Baldwin and co.
• ANNIE:-It is grammar based NER plus information extraction tool. It uses pattern based Grammar which is written in JAPE(Java language).
  
• OpenClasis:- Automated information extraction web service from Thomson Reuters (Free limited version)