Introduction to several natural language processing tools in Python

Python is loved by developers for its clear and concise syntax, ease of use and extensibility, and a rich library. Its built-in very powerful machine learning code base and math library make Python a natural development tool for natural language processing.

Then use Python for natural language processing, if you don't know these tools, you are really out.

NLTK is the leading platform for processing language data in Python. It provides an easy-to-use interface for vocabulary resources like WordNet. It also has a text processing library prepared for text classification (classificaTIon), text token (tokenizaTIon), stemming (stemming), part of speech tagging (tagging), semantic analysis (parsing), and semantic inference (semanTIc reasoning).

NLTK: NLTK is a leader in tools that use Python to handle natural languages. It provides WordNet as an excuse to handle lexical resources, as well as classification, word segmentation, stemming, annotation, grammar analysis, semantic reasoning and other libraries.

Pattern:Pattern's natural language processing tools include Part-Of-Speech Tagger, n-gram search, senTIment analysis, and WordNet. Support vector machine model for vector learning, clustering, vector machine.

TextBlob: A TextBlob is a Python library that handles text data. Some simple APIs are provided to solve some natural language processing tasks, such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and so on.

Gensim: Gensim provides the ability to model topics, file indexes, and similarity searches for large corpora. It can handle data larger than RAM memory. The authors say it is "the most powerful, efficient, and accessible software to achieve non-intervention from plain text semantic modeling."

PyNLPI: Its full name is: Python Natural Language Processing Library (sound episode: pineapple) This is a collection of various natural language processing tasks, PyNLPI can be used to process N-ary search, calculate frequency table and distribution , build a language model. He can also handle more complex data structures like priority queues, or more complex algorithms like Beam search.

spaCy: This is a commercial open source software. Combined with Python and Cython, its natural language processing power is industrially strong. It is the fastest and most advanced natural language processing tool in the field.

Polyglot: Polyglot supports the processing of massive text and multi-language. It supports word segmentation in 165 languages, recognition of language in 196, proper noun recognition in 40 languages, part-of-speech tagging in 16 languages, sentiment analysis in 136 languages, embedding in 137 languages, and morphology in 135 languages Analysis, and translation of the language in 69.

MontyLingua: MontyLingua is a free, well-trained, end-to-end English processing tool. Enter the original English text into MontyLingua and you will get a semantic explanation of the text. Suitable for tasks such as information retrieval and extraction, problem handling, and answering questions. From the English text, it extracts the active guest group, adjectives, nouns and verb phrases, person names, place names, events, dates and times, and other semantic information.

BLLIP Parser: BLLIP Parser (also known as Charniak-Johnson parser) is a statistical natural language tool that integrates component analysis and maximum entropy ordering. Includes command line and python interface.

Quepy: Quepy is a Python framework that provides the ability to transform natural language into a database query language. Conversion of different types of natural language and database query languages ​​can be easily implemented. So, with Quepy, you can implement your own natural language query database system with just a few lines of code. GitHub: https://github.com/machinalis/quepy

HanNLP: HanLP is a Java toolkit consisting of a series of models and algorithms that aim to popularize the application of natural language processing in production environments. It is not just a participle, but a complete function such as lexical analysis, syntactic analysis, and semantic understanding. HanLP features full-featured, high-performance, clear architecture, new corpus, and customizable features. Document usage instructions: Python calls the natural language processing package HanLP and how the rookie calls HanNLP

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