Nlp Python

Posted : admin On 1/26/2022

Python libraries Natural language processing (NLP) is a field located at the intersection of data science and Artificial Intelligence (AI) that – when boiled down to the basics – is all about teaching machines how to understand human languages and extract meaning from text. This is also why machine learning is often part of NLP projects. Next time we will implement this functionality, and test our Python vocabulary implementation on a more robust corpus. We will then move data from our vocabulary object into a useful data representation for NLP tasks. Finally, we will get to performing an NLP task on the data we have gone to the trouble of so aptly preparing.

People once believed that once a computer had beaten a human player at chess, machine intelligence would have surpassed human intelligence. But mastering chess for a computer turned out to be comparatively trivial, as demonstrated more than two decades ago when IBM’s Deep Blue beat Garry Kasparov, the former world chess champion.

And although machines have certainly surpassed human performance in some domains, teaching computers how to communicate via human language, something we do with little effort, has remained a challenging task.

However, owing in part to developments in algorithms and the democratization of natural-language processing (NLP) in the Python community, recently the field has seen rapid advances. What follows is an overview of the most popular NLP applications and techniques with practical implementations in Python.

Python

What is Natural-Language Processing?

Natural-language processing (NLP) allows a computer to draw insights from language data. It can also be used as any application that produces an output given some language as input, but there are more comprehensive definitions of NLP as well. Most NLP applications are powered by Python and form the core of many tools we use every day, such as email spam filtering and auto-completion in instant messaging. Below we cover more common applications of NLP.

Spell Checking

Spell checking is probably the most common NLP application, an integral part of internet search engines, email clients, and word processors. Spell checking often works in concert with other tools that increase text clarity and correctness, such as grammar correction and word completion.

Speech Recognition

Applications that perform speech recognition transcribe spoken language into text. It constitutes an essential component of Apple’s Siri and similar voice-controlled assistants. Speech recognition detects patterns in speech and transforms them into text, which a smart device then executes as a command.

Machine Translation

Now that the internet represents the main channel for high-speed communication, the need has grown for instantaneous translation. Older machine-translation systems relied on complicated handwritten rules and templates and needed the help of human translators, linguists, and computer scientists — and provided mediocre results.

Today, machine-translation systems rely on deep-learning architectures that use no rules at all, instead capturing statistical patterns in huge bodies of parallel language data.

Sentiment Analysis

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Sentiment analysis refers to the task of classifying opinions and emotions expressed within the text. This can be valuable for a company that wants to know how their customers feel about a product or a service they offer.

Rather than asking customers to fill out a survey, they can analyze online reviews or posts on social media, the source of much opinion data. Later in this article, we’ll take a look at an example of sentiment analysis in a Python natural-language processing library called TextBlob.

What are the Common NLP Techniques?

For a machine-learning system to learn, the training dataset requires proper preprocessing, which allows the model to use the relevant portion of the input and to capture meaningful statistical correlations, rather than keying on the noise in the data. Different applications require different preprocessing techniques, but their input will always need to be transformed in some way.

Some of the most common preprocessing techniques in NLP are tokenization (i.e., breaking up a sentence into smaller lexical units, such as words and phrases), stopword removal (removing words that don’t contribute to the meaning, like “the” and “of) and stemming (reducing words to their root forms so that words such as “go”, “going” and “went” count as a single token). More advanced processing techniques include part-of-speech tagging and named-entity recognition (extracting words and phrases referring to people, organizations, places, and other entities).

Why Python is a Popular Choice for NLP

Many practitioners of natural-language processing use Python: Its syntax is simple and it has a shallow learning curve, handling much of the low-level computational and logical complexity for the programmer. Writing and reading Python code is fairly intuitive, even if you’re just getting started. It boasts a large, collaborative community and numerous libraries with many tools usable out of the box, and these features make Python excellent for prototyping and experimenting — which is why it’s so popular among researchers and companies.

Python in Natural-Language Processing

Let’s look at an example of natural-language processing with Python. We’ll train a sentiment-analysis model on a movie-review dataset consisting of about 10,000 positive and negative movie reviews. For the sake of brevity, we’ll skip the preprocessing step, but you can learn more about preprocessing techniques in the Natural Language Processing Python course.

Below is an overview of the data we’re working with.

Machine-learning algorithms work only on numerical data, so we need to transform our text data into numbers. The sentiments of the reviews are already labeled numerically — 1 for a positive and 0 for a negative review — so we need to convert the reviews into numbers as well.

Before we begin, the reviews need to be ingested into a vocabulary — i.e., the set of all unique words that appear in the text. Then we’ll represent the reviews in a bag-of-words (BoW) model, which is one of the simplest ways to numerically represent text. In a BoW representation, each sentence is represented as a vector of numbers of the same length as the vocabulary, where each index in the vector corresponds to a different word from the vocabulary.

For words that were present in the original text sentence, the BoW representation will have a value of 1 (or the count of however many times each word appeared in the sentence) indexed in the vector according to the word’s position in the vocabulary, and 0 at indices of all other words that weren’t present. For a more comprehensive explanation, read this Medium article on text representation techniques.

We use scikit-learn to split our dataset into two parts, one of which we use for training our model, and another one for testing its performance. Then we create a vocabulary and convert the text reviews into a bag-of-words representation, and now our data is ready to train a logistic-regression classifier.

Our model correctly guessed the sentiment 80% of the time, not bad for such a simple model. Making the model more complex would increase its predictive power. For example, we could represent the reviews with a more sophisticated method than bag-of-words.

If you don’t want to build a model from scratch, you can often find a library that does the job. Let’s look at an example of sentiment analysis using TextBlob.

TextBlob‘s out-of-the-box sentiment analysis model performs worse than ours on the test set; we might be able to improve its performance with some fine tuning.

Now let’s dive into some of the libraries that every NLP practitioner should have in their toolkit.

Popular Python NLP Libraries

Natural Language Toolkit (NLTK)

This essential natural-language processing Python library has the tools to accomplish the majority of NLP tasks. It’s a bit slow, though, so it’s mostly used for teaching purposes.

spaCy

Whereas NLTK’s best for teaching, spaCy focuses on runtime performance. It offers state-of-the-art performance on most tasks, such as tokenizing, stemming, part-of-speech tagging, and dependency parsing. It’s fast and made for production.

TextBlob

TextBlob has a simple interface which makes it great for prototyping. It has many out-of-the-box tools, such as sentiment analysis, semantic-similarity calculation, and language translation.

scikit-learn

Scikit-learn provides general machine learning tools used in NLP, such as classes for building a pipeline and parameter-tuning methods, as well as classification, regression, and clustering algorithms.

Summary

In this article, we explained how natural-language processing technologies can produce insights using language data. We briefly covered some of the most common NLP techniques and applications. By analyzing sentiment on a dataset of movie reviews, we established two simple but effective baselines and showed how Python’s rich and accessible natural-language processing ecosystem can help its users make better and more informed decisions.

To learn more about NLP, sign up for our Natural Language Processing in Python Nanodegree.

Top Natural Language Processing (NLP) libraries for Python

Nlp

What is natural language processing (NLP)?

Natural Language Processing (NLP) is part of computer science and Artificial intelligence that helps to communicate between the computer(machine ) and humans through natural language. It makes a computer or machine to read and understand by stimulating the human natural language.

NLP is booming day by day because of the production of a large amount of data and also more unstructured data.

Nlp python

The basic task of natural language processing :

  1. Tokenization: tokenization is a process of breaking of text into smaller meaningful elements called tokens.
  2. Word Stemming and Lemmatization: The main focus of Stemming and Lemmatization is derived from the word form to its base root.
  3. Part of speech (POS) Tagging: The main work of POS tagging is to assign a label to each word with a respective grammatical component.
  4. Chunking: Chunking is picking up small pieces of information and grouping them into a bigger one.
  5. Stop Word Removal: stop words are the simple word that usually part of the grammatical structure of the sentence. This stop word removal help in sentiment analysis.
  6. Name entity recognition: It is identifying the entities such as name, location, etc it is mostly found in unstructured data.

Application of NLP:

Sentimental analysis, chatbot,speech recognition, machine translation, spell checking, keyword search, Advertisement matching.

In this article, we will know about NLP libraries and from which libraries one can start in Natural Language Processing.

Top Natural Language Processing (NLP) libraries for Python are listed below :

  1. Natural Language Toolkit (NLTK):

Natural Language Toolkit is well known and most popular python libraries used for natural language processing. It is free and opens sourced and available for Windows, Mac os, Linux operating system. It has almost 50 copras and related lexical resources. It provides an easy to use interface. NLTK comes with the text processing libraries for sentence detection, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging. It provides the particle introduction to programming for language processing.

  • SpaCy :
  • SpaCy is open-source natural language processing which is written in Cython (Cython is an extension of python design to give c like performance ). It is designed explicitly for production use -where we can develop the application which can process and understand a huge volume of data. SpaCy comes with a pre-trained statistical model and word vector and supports tokenization for many languages. It supports Windows, Linux, Mac os, and python environments such as pip, conda, etc. it can pre-process text for deep learning. It includes almost every feature such as

    Tokenization, sentence segmentation, word vector name entity recognition, and many more. In addition to this, it also includes the optimization of GPU operation.

  • Pattern :
  • The pattern is another popular natural language processing python library. It can be a powerful tool that is used in both scientific and non-scientific. It allows part of speech tagging, sentiment analysis, vector space modeling,SVM, clustering, wordnet, and n-gram. It is as simple and straight forward syntax, the syntax is such that they can be self-explanatory. The pattern supports Python 2.7 and Python 3.6. It is maintained by CLIPS and also has good documentation of it. It includes DOM parser and we crawl and offer to access to use APIs. It is one of the datamini9ng libraries which is used to parse and crawl a variety of sources such as Google, Twitter, and many more.

  • Polyglot :
  • Polyglot is one of the favorites because it offers a broad range of analysis, supports multilingual application and impressive language coverage. Polyglot depends on Numpy and Libicu-dev.

    The feature includes tokenization,language detection, named entity recognition, part of speech tagging, sentiment analysis, word embedding, etc. Polyglot requests the usage of dedicated command in the command line through the pipeline mechanisms. It is great language coverage and fasts.

  • Textblob :
  • Textblob if for both python 2 and Python 3 library design for processing textual data. The natural language processing task such as part of speech tagging, noun phrase extraction, sentiment analysis, classification, translation, WordNet, integration, parsing, word inflection, add new models or language extensions, and more. It also provides a simple API and great API call for Natural Language processing. It helps in providing access to common text processing operations through a familiar interface. It has the concept of a Textbolb object that can be treated as a Python string that is trained in natural language processing. If anyone wants to put the first step toward NLP with python one should use this library. It helps design prototype. It has some features such as it can handle large text collection, memory use optimization, high processing speed.

  • PyNLPI :
  • PyNLPI is a python library for natural language processing and has a custom made python module NLP task. The outstanding feature of NLPI has an extensive library for working with Format for linguistic Annotation. It consists of different nodules and packages each useful for both standard and advanced natural language processing tasks. We can use NLPI for basic NLP tasks like extraction of n-grams and frequency lists and frequency lists and to build a simple language model, and it also has more complex data types and advanced NLP tasks.

  • Core NLP :
  • Nlp Python

    Core NLP provide set of human language technology tool It provides the linguistic analytic tool for a piece of text tool. Core NLP is written in JAVA, JAVA should be installed in your device .it offers the interface for many programming languages including python. The feature includes task like a parser, sentiment analysis, bootstrap pattern learning, Part-of-speech tagging (POS), named entity recognizer (NER), open-source information extraction tool and coreference resolution system . CoreNLP support four human languages apart from English -Arabic, Chinese, German, French, Spanish. With Core NLP we can extract all text property. Core NLP is great for a beginner, it has an easy interface, versatile, Great for designing the prototype.

  • Genism :
  • Natural Language Processing Python Examples

    Gensim is a natural language processing python library design for topic modeling,document indexing, and similarity retrieval with large corpora. It can handle large text corpora with the help of efficiency data streaming and incremental algorithms. All algorithms in genism are memory independent concerning corpus size hence it can process input larger than RAM. It has extensive documentation and mostly depends on NumPy and SciPy for Scientific computing, thus you have to install these package first before installing Genism. Genism feature includes such as the efficient multicore implementation of popular algorithms, including online Latent Semantic Analysis, Latent Dirichlet Allocation, Random Projection, Hierarchical Dirichlet process. It is fast and has integration possibilities with NLTK.

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