Roadmap to Natural Language Processing (NLP)

Introduction

Due to the development of Big Data during the last decade. organizations are now faced with analysing large amounts of data coming from a wide variety of sources on a daily basis.

Natural Language Processing (NLP) is the area of research in Artificial Intelligence focused on processing and using Text and Speech data to create smart machines and create insights.

One of nowadays most interesting NLP application is creating machines able to discuss with humans about complex topics. IBM Project Debater represents so far one of the most successful approaches in this area.

Preprocessing Techniques

Some of the most common techniques which are applied in order to prepare text data for inference are:

• **Tokenization: **is used to segment the input text into its constituents words (tokens). In this way, it becomes easier to then convert our data into a numerical format.
• **Stop Words Removal: **is applied in order to remove from our text all the prepositions (eg. “an”, “the”, etc…) which can just be considered as a source of noise in our data (since they do not carry additional informative information in our data).
• **Stemming: **is finally used in order to get rid of all the affixes in our data (eg. prefixes or suffixes). In this way, it can in fact become much easier for our algorithm to not consider as distinguished words which have actually similar meaning (eg. insight ~ insightful).

All of these preprocessing techniques can be easily applied to different types of texts using standard Python NLP libraries such as NLTK and Spacy.

Additionally, in order to extrapolate the language syntax and structure of our text, we can make use of techniques such as Parts of Speech (POS) Tagging and Shallow Parsing (Figure 1). Using these techniques, in fact, we explicitly tag each word with its lexical category (which is based on the phrase syntactic context).

Figure 1: Parts of Speech Tagging Example [1].

Modelling Techniques

Bag of Words

Bag of Words is a technique used in Natural Language Processing and Computer Vision in order to create new features for training classifiers (Figure 2). This technique is implemented by constructing a histogram counting all the words in our document (not taking into account the word order and syntax rules).

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8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

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Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

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Natural Language Processing (NLP) Tutorial with Python & NLTK

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

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Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

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Why NLP Suffers From The Issue Of Underrepresented Languages

Natural language processing (NLP) has made several remarkable breakthroughs in recent years by providing implementations for a range of applications including optical character recognition, speech recognition, text simplification, question-answering, machine translation, dialogue systems and much more.

With the help of NLP, systems learn to identify spam emails, suggest medical articles or diagnosis related to a patient’s symptoms, etc. NLP has also been utilised as a critical ingredient in case of crucial decision-making systems such as criminal justice, credit, allocation of public resources, sorting a list of job candidates, to name a few.

However, despite all these critical use cases, NLP is still lagging and faces the problem of underrepresentation. For instance, one of the significant limitations of NLP is the ambiguity of words in languages. The ambiguity and imprecise characteristics of the natural languages make NLP difficult for machines to implement.

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Natural Language Processing (NLP) based Chatbots

Natural Language Processing (NLP)

Natural Language Processing, also known as NLP, is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data.

NLP enables the computer to acquire meaning from inputs given by users. It is a branch of informatics, mathematical linguistics, machine learning, and artificial intelligence.

An NLP based chatbot is a computer program or artificial intelligence that communicates with a customer via textual or sound methods.

The relation between Linguistics, Artificial Intelligence, Machine Learning, Deep Learning and NLP.

Various NLP engines available in the market are Google’s Dialogflow, Wit.ai (Facebook), Watson Conversation Service (IBM), Lex (Amazon), and more.

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