AI News, Getting started in Natural Language Processing (NLP)
Getting started in Natural Language Processing (NLP)
Let’s say you heard about Natural Language Processing (NLP), some sort of technology that can magically understand natural language.
Maybe you were using Siri or Google Assistant, reading a sentiment analysis of tweets or using machine translation, and you wondered how it is possible to achieve something so complex.
It is an engineering discipline that combines the power of artificial intelligence, computational linguistics and computer science, to “understand” natural language.
They can be used to solve NLP problems, as much as they can be used to solve a large number of problems not related to natural language processing.
to put it simply, methods where the tools are usually handcrafted by experts) were the standard and the statistical methods were viewed with disdain because of the failure, in the 90s, of the neural networks (NN) applied to natural language processing.
The book begins with theoretical foundations and linguistic concepts, and then addresses most of the NLP problems: word sense disambiguation, POS tagging, probabilistic parsing, machine translation, clustering, topic modeling, text categorization, etc.
For each of these points they present, analyze and compare a huge number of papers that were part of the state-of-the-art at that moment and that are still of interest.
You will find here topics such as compositional semantics, question-answer systems, information extraction, dialogue agents and above all, speech recognition.
The first chapter, for example, introduces basic concepts and explains how they are easily handled by NLTK: corpus, KWIC, similar words, frequencies, tokenization, collocations, n-grams, etc.
It explains, in a didactic way, new approaches relying on research papers or technical reports that might be hard to follow for non-initiated readers.
NLTK is a Python library that allows many classic tasks of NLP and that makes available a large amount of necessary resources, such as corpus, grammars, ontologies, etc.
Reading the documentation is worth it to understand the key concepts used in NLP applications: token, document, corpus, pipeline, tagger, parser, etc.
the decision is based on 200 words), you will find many articles that are actually works of undergraduate students or work in progress of MSc and PhD students.
Perhaps the most remarkable exception is LREC: Language Resources and Evaluation, where although the quality of the material is very disparate, it is one of the best conferences to be aware of the resources and tools available for NLP.
I write “more important” in quotes because it is relative (the quality of workshops can vary a lot), but you should know that when researchers are evaluated, this is standard criteria.
It is a vast and young field, and over the past few years, Deep Learning architectures and algorithms have made impressive advances, yielding state-of-the-art results for some common NLP tasks.
And please feel free to share with us books, websites, works and tools that you consider important and that I do not mention here.
Natural language processing
Natural language processing (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 process and analyze large amounts of natural language data.
However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced.
Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted 'blocks worlds' with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966.
However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data.
Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.
These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government.
However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the World Wide Web), which can often make up for the inferior results if the algorithm used has a low enough time complexity to be practical, which some such as Chinese Whispers do.
The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large corpora of typical real-world examples (a corpus (plural, 'corpora') is a set of documents, possibly with human or computer annotations).
12 of the best free Natural Language Processing and Machine Learning educational resources
Advances in of Natural Language Processing and Machine Learning are broadening the scope of what technology can do in people’s everyday lives, and because of this, there is an unprecedented number of people developing a curiosity in the fields.
We’ve split these resources into two categories: The resources on this post are 12 of the best, not the 12 best, and as such should be taken as suggestions on where to start learning without spending a cent, nothing more!
Natural Language Processing
For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way.
When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches.
- On Friday, January 18, 2019
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