Introduction
Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the rudiments – is tied in with showing machines how to comprehend human dialects and extract the true meaning of the text. This is additionally why machine learning is regularly essential for NLP based products.
In any case, for what reason are endless organizations around the world are keen on NLP nowadays? Basically on the grounds that these advances can give them a wide range of important bits of knowledge and arrangements that address language-related issues consumers may encounter while interacting with a product.
What is an NLP Library?
Before, no one but specialists could be part of natural language handling products that necessarily required knowledge on arithmetic, AI, and semantics.
Presently, engineers can utilize instant ready-made tools that improve text pre-processing so they can focus on building AI models. There are numerous tools and libraries made to take care of NLP issues.
Tech giants like Microsoft, Google, Apple, or Facebook are emptying a large number of dollars into this line of exploration to control their chatbots, remote helpers, recommendation engines, and other products controlled by AI.
Because of this, an issue of “what Python NLP library to pick” may rise regularly.
I have picked 6 brave NLP libraries that can be useful for anybody.
Here they are, in no specific order:
CoreNLP from Stanford group
Analysing text information utilizing Stanford’s CoreNLP makes text analysis examination simple and efficient. With only a couple lines of code, CoreNLP takes into account the extraction of a wide range of text properties, for example, named-entity recognition or part-of-speech tagging. CoreNLP is written in Java and expects Java to be installed on your computing device but offers programming interfaces for famous programming languages like Python.
The only function needed to deal with NLP/NLU related projects utilizing py-coreNLP is nlp.annotate().
Inside the capacity, one can indicate what sort of examination CoreNLP ought to execute.
In this showing, I will investigate four distinct sentences with various sentiments.
The entirety of this should be possible in one single line of code yet for readability purposes, it’s better to extend it more than a few lines.
text = "This movie was actually neither that funny, nor super witty. The movie was meh. I liked watching that movie. If I had a choice, I would not watch that movie again."
result = nlp.annotate(text,properties={
'annotators': 'sentiment',
'outputFormat': 'json',
'timeout': 1000,
})
The annotators parameter determines what sort of examinations CoreNLP will do.
For this situation, I’ve determined that I need CoreNLP to do sentiment analysis.
The JSON output format will permit me to effortlessly file into the outcomes for additional examination.
I’d prefer to call attention to that CoreNLP gives numerous different functionalities (named-entity recognition, part-of-speech tagging, lemmatization, stemming, tokenization, and so on) that can all be accessed without having to run any additional computations.
A complete list of all parameters for nlp.annotate() can be found
here
To sum up, CoreNLP’s efficiency is the thing that makes it so advantageous.
You just need to determine what analysis you’re keen on once and evade pointless calculations that may back you off when working with bigger data sets.
In the event that you’d prefer to find out about the subtleties of how CoreNLP functions and what choices there are, I’d suggest perusing the documentation.
CoreNLP Use Cases
- Grammar and syntax checking
- Entity extraction
- Sentiment analysis
NLTK, the most widely-mentioned NLP library for Python
We can’t discuss NLP in Python without referencing Natural Language Toolkit (NLTK). NLTK is one of the most far reaching NLP libraries and the most acclaimed Python NLP library.
NLTK is generally mainstream in training and exploration. It has prompted numerous advancements in text analysis. It has a ton of pre-trained models and corpora which encourages us to dissect things without any problem. It is an incredible library when you require a particular blend of algorithms.
The expectation to absorb information is steep and more often than not, it’s fairly moderate and regularly doesn’t coordinate the requests of genuine word creation use.
NLTK functionalities
Tokenization, POS, NER, classification, sentiment analysis, access to corpora
NLTK Use Cases
- Quick prototyping for POCs
- Can be used for Removing stop words and persons names in your recommendation systems
- Sentiment Analysis to check if product review is positive or negative.
Gensim, a library for document similarity analysis
Gensim is another very helpful NLP library for Python. Gensim was principally developed for topic modeling. However, it presently underpins an assortment of other NLP undertakings, for example, changing words over to vectors (word2vec), document to vectors (doc2vec), discovering text similarity, and text synopsis.
On the off chance that you are new to topic modeling, it is a procedure to extract the underlying topics from enormous volumes of text. Gensim gives algorithms like LDA and LSI the vital advancement to build top notch topic models. You may contend that topic models and word embedding are accessible in different bundles like scikit, R and so forth. However, the width and extent of facilities to assemble and assess topic models are unmatched in Gensim, in addition to a lot more helpful facilities for text processing.
It is an incredible bundle for handling texts, working with word vector models, (for example, Word2Vec, FastText and so on) and for building topic models.
Additionally, another huge plus with Gensim is: it lets you handle huge content records without stacking the whole text document in memory.
So if you are building a project that will require you to process huge amounts of data then Gensim is the library for you.
Gensim use cases
- Converting words and document to vectors
- Finding text similarity
- Text summarization
SpaCy, an industrial-strength NLP library built for performance
spaCy is an advanced NLP library accessible in Python and Cython.
It is intended for execution and working along with deep learning frameworks, for example, TensorFlow or PyTorch.
It accompanies pre-prepared statistical models and word vectors.
It highlights tokenization for more than 50 languages, convolutional neural network models for labelling, parsing and named entity recognition.
Spacy gives a one-stop-shop for tasks commonly used across any NLP based products, including:
- Tokenization
- Lemmatisation
- Part-of-speech tagging
- Entity recognition
- Dependency parsing
- Sentence recognition
- Word-to-vector transformations
- Many convenience methods for cleaning and normalising text
Spacy use cases
- Part of speech tagging
- Rule-Based Matching- This a new addition in spacy, you can find phrases or words in text using user defined rules.
- Best for production grade projects
Polyglot
This somewhat lesser-realized library is one of my top picks since it offers an expansive scope of analysis and great language inclusion.
On account of NumPy, it likewise works super quickly.
Utilizing polyglot is like spaCy – it’s extremely proficient, direct, and essentially, a great decision for products including a language spaCy doesn’t support. The library stands apart from the group additionally in light of the fact that it demands the use of a dedicated command in the command line through the pipeline systems. Certainly worth an attempt.
Polyglot use cases
- Language detection, 196 languages are supported
- Sentiment Analysis: Can be a really cool feature to have in a chatbot.
scikit–learn
This helpful NLP library furnishes engineers with a wide scope of algorithms for building AI models. It offers numerous capacities for utilizing the bag of-words technique for making highlights to handle classification problems. The quality of this library is the natural classes techniques. Additionally, scikit-learn has phenomenal documentation that assists developers with capitalizing on its highlights.
In any case, the library doesn’t utilize neural organizations for text pre-processing. So in the event that you’d prefer to do more perplexing pre-processing assignments like POS labelling for your content corpora, it’s smarter to utilize other NLP libraries and afterwards return to scikit-learn for building your models.
Scikit-learn use cases
- Document and text classification related NLP problem.
Conclusions
With Python’s broad NLP libraries Python developers can manufacture text content preparing applications successfully and help their associations increase important bits of knowledge from text data.
There are numerous Python NLP libraries that give explicit functionalities. Picking the best NLP library for your project or undertaking is tied in with knowing which functionalities are accessible and how they contrast with one another.
After you get a firm grasp on these some of the best NLP libraries for Natural Language Processing, you will be able to gain proficiency with some other libraries in a significant small timeframe. I am certain, notwithstanding, there will be no requirement for that, as NLTK with TextBlob, SpaCy, Gensim, and CoreNLP can cover practically all necessities of any NLP problem.
