Java or Python for Natural Language Processing [closed]

Solution 1:

Java vs Python for NLP is very much a preference or necessity. Depending on the company/projects you'll need to use one or the other and often there isn't much of a choice unless you're heading a project.

Other than NLTK (www.nltk.org), there are actually other libraries for text processing in python:

• TextBlob: http://textblob.readthedocs.org/en/dev/
• Gensim: http://radimrehurek.com/gensim/
• Pattern: http://www.clips.ua.ac.be/pattern
• Spacy:: http://spacy.io
• Orange: http://orange.biolab.si/features/
• Pineapple: https://github.com/proycon/pynlpl

(for more, see https://pypi.python.org/pypi?%3Aaction=search&term=natural+language+processing&submit=search)

For Java, there're tonnes of others but here's another list:

• Freeling: http://nlp.lsi.upc.edu/freeling/
• OpenNLP: http://opennlp.apache.org/
• LingPipe: http://alias-i.com/lingpipe/
• Stanford CoreNLP: http://stanfordnlp.github.io/CoreNLP/ (comes with wrappers for other languages, python included)
• CogComp NLP: https://github.com/CogComp/cogcomp-nlp

This is a nice comparison for basic string processing, see http://nltk.googlecode.com/svn/trunk/doc/howto/nlp-python.html

A useful comparison of GATE vs UIMA vs OpenNLP, see https://www.assembla.com/spaces/extraction-of-cost-data/wiki/Gate-vs-UIMA-vs-OpenNLP?version=4

If you're uncertain, which is the language to go for NLP, personally i say, "any language that will give you the desired analysis/output", see Which language or tools to learn for natural language processing?

Here's a pretty recent (2017) of NLP tools: https://github.com/alvations/awesome-community-curated-nlp

An older list of NLP tools (2013): http://web.archive.org/web/20130703190201/http://yauhenklimovich.wordpress.com/2013/05/20/tools-nlp

Other than language processing tools, you would very much need machine learning tools to incorporate into NLP pipelines.

There's a whole range in Python and Java, and once again it's up to preference and whether the libraries are user-friendly enough:

Machine Learning libraries in python:

• Sklearn (Scikit-learn): http://scikit-learn.org/stable/
• Milk: http://luispedro.org/software/milk
• Scipy: http://www.scipy.org/
• Theano: http://deeplearning.net/software/theano/
• PyML: http://pyml.sourceforge.net/
• pyBrain: http://pybrain.org/
• Graphlab Create (Commerical tool but free academic license for 1 year): https://dato.com/products/create/

(for more, see https://pypi.python.org/pypi?%3Aaction=search&term=machine+learning&submit=search)

• Weka: http://www.cs.waikato.ac.nz/ml/weka/index.html
• Mallet: http://mallet.cs.umass.edu/
• Mahout: https://mahout.apache.org/

With the recent (2015) deep learning tsunami in NLP, possibly you could consider: https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

I'll avoid listing deep learning tools out of non-favoritism / neutrality.

Other Stackoverflow questions that also asked for NLP/ML tools:

• Machine Learning and Natural Language Processing
• What are good starting points for someone interested in natural language processing?
• Natural language processing
• Natural Language Processing in Java (NLP)
• Is there a good natural language processing library
• Simple Natural Language Processing Startup for Java
• What libraries offer basic or advanced NLP methods?
• Latest good languages and books for Natural Language Processing, the basics
• (For NER) Entity Extraction/Recognition with free tools while feeding Lucene Index
• (With PHP) NLP programming tools using PHP?
• (With Ruby) https://stackoverflow.com/questions/3776361/ruby-nlp-libraries

Solution 2:

The question is very open ended. That said, rather than choose one, below is a comparison depending on the language that you would like to use (since there are good libraries available in both languages).

Python

In terms of Python, the first place you should look at is the Python Natural Language Toolkit. As they note in their description, NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

There is also some excellent code that you can look up that originated out of Google's Natural Language Toolkit project that is Python based. You can find a link to that code here on GitHub.

Java

The first place to look would be Stanford's Natural Language Processing Group. All of software that is distributed there is written in Java. All recent distributions require Oracle Java 6+ or OpenJDK 7+. Distribution packages include components for command-line invocation, jar files, a Java API, and source code.

Another great option that you see in a lot of machine learning environments here (general option), is Weka. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.