Natural Language Processing Tools: The Latest Trends and Developments

Natural Language Processing Tools: The Latest Trends and Developments


Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing the way we interact with and extract insights from textual data. With a multitude of tools and libraries available, NLP is empowering businesses and researchers across various domains. In this article, we will dive into the latest trends and developments in NLP, providing insights into the capabilities and common use cases of popular NLP tools and libraries.

Let’s start with the latest trends in NLP that will be in the limelight in the upcoming year 2024.

1. Pretrained Language Models

Pretrained language models like GPT-3, BERT, and RoBERTa have taken the NLP landscape by storm. These models, developed by OpenAI and Google, among others, have demonstrated extraordinary capabilities in understanding and generating human-like text. GPT-3, for instance, is renowned for its text generation prowess, making it ideal for chatbots and content generation. In 2024, GPT-4 is expected to revolutionize the industry with its mind-blowing capabilities.

2. Multimodal NLP

The integration of text with other modalities like images and audio is a growing trend. NLP models are evolving to process and generate content across different types of data, enabling applications in computer vision, speech recognition, and content generation.

3. Few-shot and Zero-shot Learning

Advancements in few-shot and zero-shot learning have made it possible for NLP models to perform tasks with very few examples or even without any examples in some cases. This capability enhances their versatility and usability.

4. Explainability and Interpretability

As NLP applications find their way into critical domains like healthcare and legal, there is an increasing emphasis on making NLP models more interpretable and explainable. Techniques to understand and explain model predictions are gaining traction.

5. Low-resource Languages

Efforts to apply NLP to low-resource languages are expanding. Cross-lingual transfer learning and unsupervised learning techniques are being explored to address the challenges posed by languages with limited training data.

6. Bias Mitigation

NLP models have been found to inherit biases present in their training data. Addressing bias in NLP systems has become a pivotal research focus, with techniques being developed to detect and mitigate bias.

7. Industry Adoption

NLP is being widely adopted across various industries. It finds applications in healthcare (clinical text analysis), finance (sentiment analysis), customer support (chatbots), and content generation (automated content creation), among others.

8. Open-Source Tools

The NLP community continues to release open-source libraries and tools that facilitate the development and deployment of NLP models and applications.

9. Multilingual NLP

With the globalization of businesses, there’s a growing need for NLP models that can work with multiple languages. Multilingual NLP models and techniques are gaining importance.

10. Conversational AI

Conversational AI systems, including chatbots and virtual assistants, have become more sophisticated, with improved natural language understanding and generation capabilities.

Comparison of NLP Tools and Libraries

Now, let’s take a closer look at some of the most widely used NLP tools and libraries, comparing their features and common use cases:

| Sr. No. | Tool/Library | Developer | Pretrained Models | Ease of Use | Customization | Community Support | Text Summarization | Text Generation | Sentiment Analysis | Named Entity Recognition | Tokenization | Part-of-Speech Tagging | Parsing | Text Classification | Custom NLP Tasks | Cost | Notable Features | | 1 | GPT-3 | OpenAI | Yes | Moderate | Limited | Large | Moderate | High | Moderate | Moderate | N/A | N/A | N/A | Moderate | High | Paid API | Powerful language generation | | 2 | Google NLP API | Google | Yes | Easy | Limited | Large | Moderate | Low | Moderate | High | High | Moderate | Low | Moderate | Low | Paid API | Text analysis, sentiment analysis | | 3 | Microsoft Azure Text Analysis API | Microsoft | Yes | Easy | Limited | Large | Moderate | Low | Moderate | High | High | Low | Low | Moderate | Low | Paid API | Text analysis, entity recognition | | 4 | Apache OpenNLP | Apache | No | Moderate | Extensive | Moderate | Low | Low | Moderate | Moderate | High | High | High | Low | High | Open Source | Various NLP tasks, tokenization | | 5 | NLTK | Open Source Community | No | Moderate | Extensive | Large | Moderate | Low | Moderate | Moderate | High | High | High | Moderate | High | Open Source | NLP toolkit, text processing | | 6 | SpaCy | Explosion AI | Yes | Easy | Extensive | Large | Moderate | Moderate | Moderate | High | High | High | High | Low | High | Open Source | Named entity recognition, parsing | | 7 | Stanford CoreNLP | Stanford University | Yes | Moderate | Limited | Moderate | Moderate | Moderate | Moderate | High | High | High | High | Moderate | High | Open Source | Part-of-speech tagging, parsing | | 8 | Text Blob | TextBlob Developers | No | Easy | Limited | Moderate | Low | Low | Low | High | High | High | Low | High | Open Source | Simple NLP tasks, sentiment analysis | | 9 | AllenNLP | Allen Institute | Yes | Moderate | Extensive | Moderate | Moderate | High | Moderate | High | High | High | High | High | Open Source | Deep learning-based NLP framework | | 10 | PyTorch | Facebook | No | Moderate | Extensive | Large | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Open Source | Deep learning framework | | 11 | BERT | Google | Yes | Moderate | Limited | Large | High | High | High | High | High | High | High | High | Pretrained Model | Bidirectional contextual embeddings | | 12 | Word2Vec | Google | Yes | Easy | Limited | Large | Low | Low | Low | Low | High | Low | Low | Low | Pretrained Model | Word embeddings | | 13 | Hugging Face Transformers | Hugging Face | Yes | Easy | Extensive | Large | High | High | Moderate | High | High | High | High | High | Open Source | Pretrained models, NLP pipelines | | 14 | Gensim | Radim Řehůřek | No | Moderate | Extensive | Large | Low | Low | Low | High | Low | Low | Low | Low | Open Source | Word embeddings, topic modeling | | 15 | MonkeyLearn | MonkeyLearn | Yes | Easy | Limited | Moderate | Moderate | Low | High | Low | Low | Low | Low | High | High | Paid Service | Text classification, custom models | | 16 | Lexalytics | Lexalytics | Yes | Moderate | Extensive | Moderate | High | Low | High | High | Low | Low | Low | High | High | Paid Service | Text analysis, sentiment analysis | | 17 | BytesView | BytesView | Yes | Easy | Limited | Limited | High | Low | High | High | Low | Low | Low | High | High | Paid Service | Text analysis, sentiment analysis | | 18 | Scikit-learn | Open Source Community | No | Easy | Extensive | Large | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | High | Customizable | Open Source | General-purpose ML library | | 19 | TensorFlow | Google | No | Moderate | Extensive | Large | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Customizable | Open Source | Deep learning framework | | 20 | IBM Watson | IBM | Yes | Moderate | Limited | Large | Moderate | Low | Moderate | Moderate | High | Low | Low | Moderate | High | Paid Service | NLP services, Watson Studio | | 21 | CogCompNLP | University of Illinois | Yes | Moderate | Extensive | Moderate | Moderate | Moderate | High | High | High | High | Moderate | High | Open Source | Various NLP tools and resources |

Sample Code Snippets

Now let’s take a look at some sample code snippets for the above tools and libraries:

1. GPT-3 (OpenAI)(

  • Description: GPT-3 is a state-of-the-art language model known for text generation and completion.
  • Sample Code:
    import openai 
    prompt = "Translate the following English text to French: 'Hello, how are you?'" 
    response = openai.Completion.create( engine="text-davinci-002", prompt=prompt, max_tokens=50 ) 

2. Google NLP API (Google Cloud)(

  • Description: Google NLP API provides various NLP capabilities, including sentiment analysis and entity recognition.
  • Sample Code:
    from import language_v1
    client = language_v1.LanguageServiceClient()
    text = "I love this product! It's amazing."
    document = language_v1.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT)
    sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment
    print(f"Sentiment Score: {sentiment.score}")

3. Microsoft Azure Text Analysis API (Microsoft Azure)(

  • Description: Azure Text Analysis API offers text analysis features like sentiment analysis and entity recognition.
  • Sample Code:
    from import TextAnalyticsClient
    from azure.core.credentials import AzureKeyCredential
    key = "<your-api-key>"
    endpoint = "<your-endpoint>"
    credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=credential)
    documents = ["Microsoft was founded by Bill Gates in 1975."]
    result = text_analytics_client.recognize_entities(documents=documents)
    for entity in result[0].entities:
        print(f"Entity: {entity.text}, Type: {entity.category}")

4. Apache OpenNLP(

  • Description: Apache OpenNLP is an open-source library for NLP tasks like tokenization and part-of-speech tagging.
  • Sample Code:
    from import SimpleTokenizer
    tokenizer = SimpleTokenizer()
    text = "Apache OpenNLP is a toolkit for natural language processing."
    tokens = tokenizer.tokenize(text)

5. NLTK (Natural Language Toolkit)(

  • Description: NLTK is a comprehensive NLP library for various tasks, including tokenization and sentiment analysis.
  • Sample Code:
    import nltk
    sentence = "NLTK is a leading platform for building Python programs to work with human language data."
    words = nltk.word_tokenize(sentence)
    tagged_words = nltk.pos_tag(words)

6. SpaCy(

  • Description: SpaCy is an NLP library known for its speed and accuracy in tasks like named entity recognition.
  • Sample Code:
    import spacy
    nlp = spacy.load("en_core_web_sm")
    text = "Apple Inc. is headquartered in Cupertino, California."
    doc = nlp(text)
    for ent in doc.ents:
        print(f"Entity: {ent.text}, Type: {ent.label_}")

7. Stanford CoreNLP(

  • Description: Stanford CoreNLP provides NLP tools like part-of-speech tagging and parsing.
  • Sample Code:
    from stanfordcorenlp import StanfordCoreNLP
    nlp = StanfordCoreNLP('<path-to-Stanford-CoreNLP>')
    text = "The movie was great!"
    sentiment = nlp.annotate(text, properties={'annotators': 'sentiment', 'outputFormat': 'json'})

8. Text Blob(

  • Description: Text Blob simplifies text processing tasks like sentiment analysis.
  • Sample Code:
    from textblob import TextBlob
    text = "I love this product! It's amazing."
    analysis = TextBlob(text)
    sentiment = analysis.sentiment.polarity

9. AllenNLP(

  • Description: AllenNLP is a framework for deep learning research in NLP, providing tools for various tasks, including text classification.
  • Sample Code:
    from allennlp.predictors.predictor import Predictor
    predictor = Predictor.from_path("<path-to-model>")
    sentence = "This is a positive review."
    result = predictor.predict(sentence)
    label = result["label"]
    print(f"Predicted Label: {label}")

10. PyTorch(

  • Description: PyTorch is a deep learning framework that can be used to build and train custom NLP models, such as text generators.
  • Sample Code: (Simplified example, actual code would depend on model architecture)
    import torch
    import torch.nn as nn
    import torch.optim as optim
    # Define a simple RNN model for text generation
    class RNN(nn.Module):
        # Define your model here...
    # Train the model and generate text...

11. BERT(

  • Description: BERT is a pretrained transformer model for various NLP tasks, including text classification and named entity recognition.
  • Sample Code (using Hugging Face Transformers library):
    from transformers import BertTokenizer, BertForSequenceClassification
    import torch
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
    input_text = "This is a sample sentence."
    input_ids = tokenizer.encode(input_text, add_special_tokens=True)
    inputs = torch.tensor([input_ids])
    outputs = model(inputs)

12. Word2Vec(

  • Description: Word2Vec is an embedding technique to represent words as vectors, useful for various NLP tasks.
  • Sample Code (using Gensim library):
    from gensim.models import Word2Vec
    sentences = [['this', 'is', 'a', 'sentence'], ['another', 'example', 'sentence']]
    model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)
    vector = model.wv['example']

13. Hugging Face(

  • Description: Hugging Face Transformers is a library that provides access to a wide range of pretrained transformer models for NLP tasks.
  • Sample Code (using a transformer for text classification):
    from transformers import pipeline
    classifier = pipeline('sentiment-analysis')
    result = classifier("I really enjoyed this movie!")

14. Gensim(

  • Description: Gensim is a library for topic modeling and document similarity analysis, including Word2Vec and Doc2Vec models.
  • Sample Code (Word2Vec example shown above).

15. MonkeyLearn(

  • Description: MonkeyLearn is a text analysis platform that offers prebuilt models and custom model training for tasks like sentiment analysis.
  • Sample Code (accessing a MonkeyLearn model):
    from monkeylearn import MonkeyLearn
    ml = MonkeyLearn('<your-api-key>')
    data = ["I love this product!", "Terrible experience"]
    model_id = '<your-model-id>'
    result = ml.classifiers.classify(model_id, data)

16. Lexalytics(

  • Description: Lexalytics is an NLP platform that provides sentiment analysis, entity recognition, and more, with an emphasis on accuracy.
  • Sample Code (using Lexalytics’ Python SDK):
    from lexalytics import Lexalytics
    config = {'username': '<your-username>', 'password': '<your-password>'}
    lx = Lexalytics(config)
    text = "This is a great product!"
    sentiment = lx.sentiment(text)

17. BytesView(

  • Description: BytesView is a text analysis platform that offers sentiment analysis, entity recognition, and other NLP features.
  • Sample Code (using BytesView’s API):
    import requests
    url = "<BytesView-API-Endpoint>"
    data = {"text": "I'm really impressed with this service!"}
    response =, json=data)
    sentiment = response.json()['sentiment']

18. Scikit-learn(

  • Description: Scikit-learn is a versatile machine learning library that includes NLP-related modules for tasks like text classification.
  • Sample Code (text classification with a simple machine learning model):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    # Load your text data and labels...
    vectorizer = CountVectorizer()
    X_train = vectorizer.fit_transform(train_data)
    classifier = MultinomialNB(), train_labels)

19. TensorFlow(

  • Description: TensorFlow is a deep learning framework that can be used for NLP tasks, including building custom neural network models.
  • Sample Code (text classification with TensorFlow Keras):
    import tensorflow as tf
    from tensorflow import keras
    model = keras.Sequential([
        keras.layers.Embedding(input_dim=num_words, output_dim=embedding_dim),
        keras.layers.Dense(1, activation='sigmoid')
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']), y_train, epochs=5, batch_size=32)

20. IBM Watson(

  • Description: IBM Watson offers a suite of AI and NLP services, including Watson Natural Language Understanding for text analysis.
  • Sample Code (using Watson Natural Language Understanding):
    from ibm_watson import NaturalLanguageUnderstandingV1
    from ibm_watson.natural_language_understanding_v1 import Features, SentimentOptions
    natural_language_understanding = NaturalLanguageUnderstandingV1(
    response = natural_language_understanding.analyze(
        text='IBM Watson is a powerful AI platform.',
    sentiment = response.result['sentiment']['document']['score']

21. CogCompNLP(

  • Description: CogCompNLP is a comprehensive NLP library with various tools and models for text analysis.
  • Sample Code (using CogCompNLP’s annotators):
    from cogcompnlp.core.pipeline import Pipeline from
        cogcompnlp.core.document import Document pipeline = Pipeline()
        pipeline.set\_reader(reader) pipeline.add\_component(mention\_ner)
        pipeline.initialize() text = "John works at a tech company in San Francisco."
        doc = Document(text) pipeline.process(doc) mentions =

These additional examples cover a wide range of NLP tools and libraries and demonstrate their capabilities for various NLP tasks. Depending on your specific use case and requirements, you can choose the tool or library that best suits your needs.


In conclusion, the field of Natural Language Processing (NLP) is a rapidly growing field with powerful tools and libraries that cater to diverse text analysis needs. In this article, we’ve explored a wide spectrum of these NLP tools and libraries, each offering unique features and capabilities.

Choosing the right tool or library depends on the specific use case, expertise level, and infrastructure considerations. As the NLP field continues to advance, staying informed about the latest trends and developments is crucial for harnessing the full potential of these tools in tackling real-world challenges and unlocking insights from textual data.

Below are some reference sources that I generally prefer for my research and analysis that can help you as well in your journey.

  • Academic Research Papers: You can find research papers related to NLP on academic websites, libraries, and research paper search engines like Google Scholar, arXiv, and ACL Anthology.

  • Official Documentation: The official documentation for each NLP tool or library can typically be found on their respective websites or GitHub repositories. For example, you can search for “SpaCy official documentation” or “NLTK GitHub” to find the documentation for specific libraries.

  • Community Discussions and Forums: Online forums like Stack Overflow, Reddit, and GitHub issues for specific projects are places where users discuss their experiences, issues, and benchmarks.

  • NLP Benchmark Datasets: NLP benchmark datasets can often be found on websites associated with universities or research organizations. For example, the Stanford NLP Group provides benchmark datasets (Stanford NLP), and other datasets can be found on platforms like Kaggle (Kaggle).

  • NLP Competitions: Kaggle (Kaggle Competitions) is a popular platform for machine learning competitions, including NLP tasks. You can find benchmarking and competition results there.

If you are a beginner in this field, then this article will help you understand where to start as per your specific interests out of the various scopes of NLP.

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