Natural Language Processing Summary:
Natural Language Process, or NLP for short, is a field of study focused on the interactions between human language and computers. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia).
“Natural Language Processing is a field that covers computer understanding and manipulation of human language, and it’s ripe with possibilities for newsgathering,” Anthony Pesce said in Natural Language Processing in the kitchen. “You usually hear about it in the context of analyzing large pools of legislation or other document sets, attempting to discover patterns or root out corruption.”
NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
“Apart from common word processor operations that treat text like a mere sequence of symbols, NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence and, ultimately, sentences convey ideas,” John Rehling, an NLP expert at Meltwater Group, said in How Natural Language Processing Helps Uncover Social Media Sentiment. “By analyzing language for its meaning, NLP systems have long filled useful roles, such as correcting grammar, converting speech to text and automatically translating between languages.”
What Can I Use Natural Language Processing For?
- Summarize blocks of text using Summarizer to extract the most important and central ideas while ignoring irrelevant information.
- Automatically generate keyword tags from content using AutoTag, which leverages LDA, a technique that discovers topics contained within a body of text.
- Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity Recognition.
- Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutral to very positive.
- Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer.
These are just some of the natural language processing algorithms web developers can use.
What Are Some Real World Examples of Natural Language Processing?
Social media analysis is a great example of NLP use. Brands track conversations online to understand what customers are saying, and glean insight into user behavior.
“One of the most compelling ways NLP offers valuable intelligence is by tracking sentiment — the tone of a written message (tweet, Facebook update, etc.) — and tag that text as positive, negative or neutral,” Rehling said.
Build your own social media monitoring tool
- Start by using the algorithm Retrieve Tweets With Keyword to capture all mentions of your brand name on Twitter. In our case, we search for mentions of Algorithmia.
- Then, pipe the results into the Sentiment Analysis algorithm, which will assign a sentiment rating from 0-4 for each string (Tweet).
Similarly, Facebook uses NLP to track trending topics and popular hashtags.
“Hashtags and topics are two different ways of grouping and participating in conversations,” Chris Struhar, a software engineer on News Feed, said in How Facebook Built Trending Topics With Natural Language Processing. “So don’t think Facebook won’t recognize a string as a topic without a hashtag in front of it. Rather, it’s all about NLP: natural language processing. Ain’t nothing natural about a hashtag, so Facebook instead parses strings and figures out which strings are referring to nodes — objects in the network. We look at the text, and we try to understand what that was about.”
It’s not just social media that can use NLP to it’s benefit. Publishers are hoping to use NLP to improve the quality of their online communities by leveraging technology to “auto-filter the offensive comments on news sites to save moderators from what can be an ‘exhausting process’,” Francis Tseng said in Prototype winner using ‘natural language processing’ to solve journalism’s commenting problem.
Use NLP to build your own RSS reader
You can build a machine learning RSS reader in less than 30-minutes using the follow algorithms:
- ScrapeRSS to grab the title and content from an RSS feed.
- Html2Text to keep the important text, but strip all the HTML from the document.
- AutoTag uses Latent Dirichlet Allocation to identify relevant keywords from the text.
- Sentiment Analysis is then used to identify if the article is positive, negative, or neutral.
- Summarizer is finally used to identify the key sentences.
Recommended NLP Books for Beginners
- Speech and Language Processing: “The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations.”
- An Introduction to Information Retrieval: “Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts.”
- Foundations of Statistical Natural Language Processing: “This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.”
- Handbook of Natural Language Processing: “The Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.”
- Statistical Language Learning (Language, Speech, and Communication): “Eugene Charniak breaks new ground in artificial intelligenceresearch by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background.”
- Natural Language Understanding: “This long-awaited revision offers a comprehensive introduction to natural language understanding with developments and research in the field today. Building on the effective framework of the first edition, the new edition gives the same balanced coverage of syntax, semantics, and discourse, and offers a uniform framework based on feature-based context-free grammars and chart parsers used for syntactic and semantic processing.”
- Natural Language Processing Tutorial: “We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. You can get the source of the post from github.”
- Basic Natural Language Processing: “In this tutorial competition, we dig a little “deeper” into sentiment analysis. People express their emotions in language that is often obscured by sarcasm, ambiguity, and plays on words, all of which could be very misleading for both humans and computers.“
- An NLP tutorial with Roger Ebert: “Natural Language Processing is the process of extracting information from text and speech. In this post, we walk through different approaches for automatically extracting information from text—keyword-based, statistical, machine learning—to explain why many organizations are now moving towards the more sophisticated machine-learning approaches to managing text data.”
If you’re interested in learning more, this free introductory course from Stanford University will help you will learn the fundamentals of natural language processing, and how you can use it to solve practical problems.
Once you’ve gotten the fundamentals down, apply what you’ve learned using Python and NLTK, the most popular framework for Python NLP.
- Natural language processing (Wikipedia): “Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. In 1950, Alan Turing published an article titled ‘Computing Machinery and Intelligence’ which proposed what is now called the Turing test as a criterion of intelligence. Starting in the late 1980s, however, there was a revolution in NLP with the introduction of machine learning algorithms for language processing.”
- Outline of natural language processing (Wikipedia): “The following outline is provided as an overview of and topical guide to natural language processing: Natural language processing – computer activity in which computers are entailed to analyze, understand, alter, or generate natural language.”
- Apache OpenNLP: “The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text.”
- Natural Language Toolkit: “NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Natural Language Processing with Python provides a practical introduction to programming for language processing.”