3 Powerful Python Libraries for NLP in Indian Languages

ByJordan Brown

Introduction

India is home to an incredible diversity of languages. With 22 scheduled languages and over 19,500 dialects, the linguistic landscape of India is truly awe-inspiring. However, this presents unique challenges when it comes to Natural Language Processing (NLP). While great strides have been made in NLP for English and a handful of other languages, Indian languages have traditionally been underserved.

Fortunately, this is starting to change. In recent years, there has been a surge of interest in developing NLP tools and techniques specifically tailored for Indian languages. This article will introduce you to three of the most powerful and popular Python libraries for NLP in Indian languages: iNLTK, Indic NLP, and StanfordNLP.

We‘ll dive into the key features and capabilities of each library, walk through code examples demonstrating their usage, and discuss the latest research trends shaping the future of Indian language NLP. Whether you‘re a beginner just getting started with NLP or an experienced practitioner looking to expand your linguistic horizons, this article will equip you with the knowledge and tools you need to embark on your Indian language NLP journey. Let‘s get started!

The Challenges of Indian Language NLP

Before we jump into the libraries, it‘s important to understand the unique challenges posed by Indian languages from an NLP perspective. Some key issues include:

  • Script Diversity: Indian languages use a variety of different scripts, such as Devanagari, Bengali, Telugu, Tamil, etc. NLP models need to be able to handle this diversity.

  • Morphological Richness: Indian languages exhibit rich morphology, with extensive use of inflections, case markers, postpositions, etc. This leads to a high number of word forms and increases data sparsity.

  • Lack of Annotated Data: Compared to English, there is a paucity of large-scale, annotated datasets for Indian languages. This makes it harder to train robust, high-performing models.

  • Code-Mixing: Due to India‘s multilingual environment, code-mixing (i.e. the mixing of multiple languages within a single utterance) is extremely common. NLP systems need to gracefully handle such mixed input.

  • Dialectal Variation: Each Indian language has numerous dialects which can differ substantially in terms of vocabulary, pronunciation, and grammar. NLP models should be dialect-aware.

With these challenges in mind, let‘s now take a look at how the iNLTK, Indic NLP, and StanfordNLP libraries are tackling Indian language NLP.

Library 1: iNLTK

iNLTK (Natural Language Toolkit for Indic Languages) is a powerful Python library for NLP in Indian languages. It provides a unified API to access a wide range of NLP tasks such as tokenization, embedding, similarity, and more.

Some of the key features of iNLTK are:

  • Supports 12 major Indian languages: Hindi, Bengali, Gujarati, Kannada, Malayalam, Marathi, Nepali, Oriya, Panjabi, Sanskrit, Tamil and Urdu.
  • Uses state-of-the-art deep learning models like ULMFiT, BERT, etc. under the hood to power its functionality.
  • Provides both pretrained models and the ability to train your own models.
  • Simple, easy to use API.

To give you a taste of what iNLTK can do, here are a few examples:

Tokenization

from inltk.inltk import tokenize

text = "आज मौसम अच्छा है। हम आज खेल सकते हैं।" 
tokens = tokenize(text, "hi")
print(tokens)

Output:

[‘आज‘, ‘मौसम‘, ‘अच्छा‘, ‘है‘, ‘।‘, ‘हम‘, ‘आज‘, ‘खेल‘, ‘सकते‘, ‘हैं‘, ‘।‘]

Embedding

from inltk.inltk import get_embedding

embedding = get_embedding("आज मौसम अच्छा है", "hi")
print(embedding.shape)

Output:

(768,)

Similarity

from inltk.inltk import similarity

score = similarity("आप खाना पसंद करते हो", "आपको स्वादिष्ट भोजन पसंद है", ‘hi‘)
print(score)

Output:

0.67055

As you can see, iNLTK makes it extremely convenient to perform a variety of NLP tasks on Indian language text. It takes care of the low-level details and lets you focus on your application.

Library 2: Indic NLP

Indic NLP is another excellent Python library for Indian language NLP. It provides a set of tools for low-level text processing tasks that are common across Indian languages.

Some of the key capabilities of Indic NLP are:

  • Supports around 18 major Indian languages.
  • Provides modules for text normalization, tokenization, word segmentation, script conversion, transliteration, and more.
  • Includes Unicode-based resources for Indian language computing.
  • Leverages the linguistic commonalities between Indian languages to provide general solutions.

Here are some examples showing how to use Indic NLP:

Tokenization

from indicnlp.tokenize import sentence_tokenize, indic_tokenize  

sentences = sentence_tokenize("आज मौसम अच्छा है। हम आज खेल सकते हैं।", lang=‘hi‘)
print(sentences)

tokens = indic_tokenize(sentences[0], lang=‘hi‘)
print(tokens)

Output:

[‘आज मौसम अच्छा है।‘, ‘हम आज खेल सकते हैं।‘]
[‘आज‘, ‘मौसम‘, ‘अच्छा‘, ‘है‘, ‘।‘]

Script Conversion

from indicnlp.transliterate.unicode_transliterate import UnicodeIndicTransliterator

input_text = "आज मौसम अच्छा है।"
print(UnicodeIndicTransliterator.transliterate(input_text,"hi","te"))

Output:

ఆజ మౌసమ అచ్ఛా హై.

Word Segmentation

from indicnlp.tokenize import trivial_tokenize_indic
from indicnlp.morph import unsupervised_morph 

tokens = trivial_tokenize_indic("प्रदूषणकारी")
print(tokens)

morphemes = unsupervised_morph.morfessor.MorfessorMorph(lang=‘hi‘).morph_data_line(‘ ‘.join(tokens))
print(morphemes)

Output:

[‘प्रदूषणकारी‘]
[‘प्रदूषण‘, ‘कारी‘]

Indic NLP excels at low-level text processing tasks that form the foundation of NLP pipelines. Its modules can help standardize and streamline the early stages of Indian language text processing.

Library 3: StanfordNLP

StanfordNLP is a Python NLP library built on top of the highly acclaimed Java-based Stanford CoreNLP library. While not exclusively focused on Indian languages, StanfordNLP does provide excellent support for Hindi and Urdu, among 53 other languages.

Some of the highlights of StanfordNLP are:

  • Provides state-of-the-art models for a range of NLP tasks including tokenization, lemmatization, POS tagging, dependency parsing, and more.
  • Models are built on top of modern deep learning architectures and trained on large-scale, high-quality annotated datasets.
  • Provides a simple, unified API for accessing all functionality.
  • Includes visualization utilities for generating dependency parses.

Here are a few examples of using StanfordNLP for Hindi NLP:

Pipeline

import stanfordnlp

stanfordnlp.download(‘hi‘)
nlp = stanfordnlp.Pipeline(lang=‘hi‘)

doc = nlp("राम ने खाना खाया")
doc.sentences[0].print_dependencies()

Output:

राम ने खाना खाया
└────── nsubj
        └────── case
                └────── obj
                        └────── root

POS Tagging

doc = nlp("राम बाजार गया")

for token in doc.sentences[0].tokens:
    print(f‘{token.text}\t{token.upos}‘)

Output: 

राम PROPN
बाजार NOUN
गया VERB

NER

doc = nlp("अहमदाबाद गुजरात का एक शहर है") 

for ent in doc.ents:
  print(f‘{ent.text}\t{ent.type}‘)

Output:

अहमदाबाद LOC
गुजरात LOC

StanfordNLP provides access to highly accurate, deep learning-based models for core NLP tasks in Indian languages. It‘s an excellent choice if you need reliable, performant models out of the box.

Comparison and Trends

So how do these three libraries stack up against each other? Here‘s a quick comparison:

  • iNLTK excels at high-level NLP tasks and provides the most comprehensive functionality among the three. It also supports the largest number of Indian languages.

  • Indic NLP specializes in low-level text processing tasks that form the bedrock of NLP pipelines. It‘s a great choice if you need to standardize and streamline early-stage text processing.

  • StanfordNLP provides state-of-the-art models for core NLP tasks in Hindi and Urdu. It‘s the go-to library if you need reliable, high-performance models for these languages.

In terms of the latest trends, there‘s a lot of exciting research happening in Indian language NLP. Some key areas include:

  • Exploiting transfer learning and multilingual models to overcome the lack of annotated data.
  • Developing more robust techniques for code-mixed text processing.
  • Leveraging adversarial learning to build dialect-invariant NLP models.
  • Using deep learning architectures like Transformers to push the state-of-the-art on Indian language NLP tasks.

Future Outlook

While the iNLTK, Indic NLP, and StanfordNLP libraries have made significant strides, there‘s still a lot of room for improvement in Indian language NLP. Some key challenges and opportunities include:

  • Building larger-scale, high-quality datasets for Indian languages to fuel data-hungry deep learning models.
  • Developing more advanced techniques for handling code-mixing, dialectal variation, and other Indian language specific phenomena.
  • Leveraging recent breakthroughs in pretraining and transfer learning to build more powerful, generalizable models.
  • Fostering collaboration between academia and industry to drive research and development.

As more resources and efforts are devoted to this space, we can expect to see rapid advances in the coming years. The future of Indian language NLP looks very bright indeed!

Conclusion

In this article, we took a deep dive into three of the most powerful and popular Python libraries for NLP in Indian languages: iNLTK, Indic NLP, and StanfordNLP. We explored the key features and capabilities of each library, walked through code examples demonstrating their usage, and discussed the latest research trends and future directions.

As we‘ve seen, while Indian language NLP poses unique challenges, these libraries provide a robust and ever-expanding toolkit to tackle them. Whether you‘re building chatbots, sentiment analyzers, machine translation systems, or any other NLP application, these libraries can help you get the job done.

Of course, this is just the tip of the iceberg. There are many other excellent libraries, tools, and resources out there for Indian language NLP. Some other notable mentions include:

The field of Indian language NLP is rapidly evolving, with new breakthroughs happening all the time. To stay up-to-date with the latest developments, I recommend following leading researchers and organizations in this space, such as:

  • CFILT: Center For Indian Language Technology at IIT Bombay
  • AIML: Center for Artificial Intelligence and Machine Learning at IIIT Hyderabad
  • NLP at ISI: NLP research group at Indian Statistical Institute

I hope this article has piqued your interest in Indian language NLP and equipped you with some powerful tools to get started. As you embark on your NLP journey, remember that you‘re not just working with data and algorithms, but with the very fabric of human language and cognition. NLP has the power to bridge the digital divide, preserve linguistic diversity, and unlock new frontiers of human-machine interaction.

So go forth and code, explore, and innovate. The future of Indian language NLP is in your hands!

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