NLP Best Practices

In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora.

This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.

Overview

The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems The content is based on…

Scenario	Models	Description	Languages
Text Classification	BERT, XLNet, RoBERTa	Text classification is a supervised learning method of learning and predicting the category or the class of a document given its text content.	English, Hindi, Arabic
Named Entity Recognition	BERT	Named entity recognition (NER) is the task of classifying words or key phrases of a text into predefined entities of interest.	English
Text Summarization	BERTSum	Text summarization is a language generation task of summarizing the input text into a shorter paragraph of text.	English
Entailment	BERT, XLNet, RoBERTa	Textual entailment is the task of classifying the binary relation between two natural-language texts, text and hypothesis, to determine if the text agrees with the hypothesis or not.	English
Question Answering	BiDAF, BERT, XLNet	Question answering (QA) is the task of retrieving or generating a valid answer for a given query in natural language, provided with a passage related to the query.	English
Sentence Similarity	BERT, GenSen	Sentence similarity is the process of computing a similarity score given a pair of text documents.	English
Embeddings	Word2Vec fastText GloVe	Embedding is the process of converting a word or a piece of text to a continuous vector space of real number, usually, in low dimension.	English
Sentiment Analysis	Dependency Parser GloVe	Provides an example of train and use Aspect Based Sentiment Analysis with Azure ML and Intel NLP Architect .	English

Scenario

Models

Description

Languages

Text Classification

BERT, XLNet, RoBERTa

Text classification is a supervised learning method of learning and predicting the category or the class of a document given its text content.

English, Hindi, Arabic

Named Entity Recognition

BERT

Named entity recognition (NER) is the task of classifying words or key phrases of a text into predefined entities of interest.

English

Text Summarization

BERTSum

Text summarization is a language generation task of summarizing the input text into a shorter paragraph of text.

English

Entailment

BERT, XLNet, RoBERTa

Textual entailment is the task of classifying the binary relation between two natural-language texts, text and hypothesis, to determine if the text agrees with the hypothesis or not.

English

Question Answering

BiDAF, BERT, XLNet

Question answering (QA) is the task of retrieving or generating a valid answer for a given query in natural language, provided with a passage related to the query.

English

Sentence Similarity

BERT, GenSen

Sentence similarity is the process of computing a similarity score given a pair of text documents.

English

Embeddings

Word2Vec
fastText
GloVe

Embedding is the process of converting a word or a piece of text to a continuous vector space of real number, usually, in low dimension.

English

Sentiment Analysis

Dependency Parser
GloVe

Provides an example of train and use Aspect Based Sentiment Analysis with Azure ML and Intel NLP Architect .