Natural Language Processing and
related topics
What is Natural Language Processing or NLP? – answer NLP is the field of study that
focuses on the interactions between human language and computers. It sits at the
intersection of computer science, artificial intelligence, and computational linguistics
(Wikipedia).
NLP goes by many names — text analytics, data mining, computational linguistics —
but the basic principle remains the same. NLP refers to computer systems that derive
meaning from human language in a smart and useful way.
What are the applications of NLP? – answer NLP is a way for computers to analyze,
understand, and derive meaning from human language in a smart and useful way.
Developers can organize and structure knowledge to perform tasks such as automatic
text summarization, translation, named entity recognition, relationship extraction,
sentiment analysis, speech recognition, topic extraction, topic segmentation, parts-of-
speech tagging, relationship extraction, stemming, and more. NLP is commonly used for
text mining, machine translation, and automated question answering.
NLP algorithms are typically based on machine learning algorithms. Instead of hand-
coding large sets of rules, NLP can rely on machine learning to automatically learn
these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a
collection of sentences), and making a statical inference. In general, the more data
analyzed, the more accurate the model will be.
How does NLP work? - answerApart 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. 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.
(John Rehling, an NLP expert at Meltwater Group, said in How Natural Language
Processing Helps Uncover Social Media Sentiment.)
Why is NLP relevant? - answerNLP is characterized as a hard problem in computer
science. Human language is rarely precise, or plainly spoken. To understand human
language is to understand not only the words, but the concepts and how they're linked
together to create meaning. Despite language being one of the easiest things for
humans to learn, the ambiguity of language is what makes natural language processing
a difficult problem for computers to master.
, What are some open-source NLP libraries? - answer- Apache OpenNLP: a machine
learning toolkit that provides tokenizers, sentence segmentation, part-of-speech
tagging, named entity extraction, chunking, parsing, coreference resolution, and more.
- Natural Language Toolkit (NLTK): a Python library that provides modules for
processing text, classifying, tokenizing, stemming, tagging, parsing, and more.
- Standford NLP: a suite of NLP tools that provide part-of-speech tagging, the named
entity recognizer, coreference resolutionsystem, sentiment analysis, and more.
- MALLET: a Java package that provides Latent Dirichlet Allocation, document
classification, clustering, topic modeling, information extraction, and more.
These libraries provide the algorithmic building blocks of NLP in real-world applications.
Algorithmia provides a free API endpoint for many of these algorithms, without ever
having to setup or provision servers and infrastructure.
What are some NLP examples? - answer- Use Summarizer to automatically summarize
a block of text, exacting topic sentences, and ignoring the rest.
- Generate keyword topic tags from a document using LDA (Latent Dirichlet Allocation),
which determines the most relevant words from a document. This algorithm is at the
heart of the Auto-Tag and Auto-Tag URL microservices.
- Sentiment Analysis, based on StanfordNLP, can be used to identify the feeling,
opinion, or belief of a statement, from very negative, to neutral, to very positive. Often,
developers with use an algorithm to identify the sentiment of a term in a sentence, or
use sentiment analysis to analyze social media.
What's NLP's role in social media? - answerNLP can analyze language patterns to
understand text. 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.
Much can be gleaned from sentiment analysis. Companies can target unhappy
customers or, more importantly, find their competitors' unhappy customers, and
generate leads. I like to call these discoveries "actionable insights" — findings that can
be directly implemented into PR, marketing, adverting and sales efforts.
What are the limits of NLP in social media? - answerNLP technology lacks human-level
intelligence, at least for the foreseeable future. On a text-by-text basis, the system's
conclusions may be wrong as no automated sentiment analysis that currently exists can
handle certain level of nuance (sarcasm, for example).
Furthermore, certain expressions ("ima") or abbreviations ("#ff") fool the program,
especially when people have 140 characters or less to express their opinions, or when
they use slang, profanity, misspellings and neologisms.
Finally, much of social media interaction is personal, expressed between two people or
among a group, commonly in 1st or 2nd person contrasting with news or brand posts,
related topics
What is Natural Language Processing or NLP? – answer NLP is the field of study that
focuses on the interactions between human language and computers. It sits at the
intersection of computer science, artificial intelligence, and computational linguistics
(Wikipedia).
NLP goes by many names — text analytics, data mining, computational linguistics —
but the basic principle remains the same. NLP refers to computer systems that derive
meaning from human language in a smart and useful way.
What are the applications of NLP? – answer NLP is a way for computers to analyze,
understand, and derive meaning from human language in a smart and useful way.
Developers can organize and structure knowledge to perform tasks such as automatic
text summarization, translation, named entity recognition, relationship extraction,
sentiment analysis, speech recognition, topic extraction, topic segmentation, parts-of-
speech tagging, relationship extraction, stemming, and more. NLP is commonly used for
text mining, machine translation, and automated question answering.
NLP algorithms are typically based on machine learning algorithms. Instead of hand-
coding large sets of rules, NLP can rely on machine learning to automatically learn
these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a
collection of sentences), and making a statical inference. In general, the more data
analyzed, the more accurate the model will be.
How does NLP work? - answerApart 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. 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.
(John Rehling, an NLP expert at Meltwater Group, said in How Natural Language
Processing Helps Uncover Social Media Sentiment.)
Why is NLP relevant? - answerNLP is characterized as a hard problem in computer
science. Human language is rarely precise, or plainly spoken. To understand human
language is to understand not only the words, but the concepts and how they're linked
together to create meaning. Despite language being one of the easiest things for
humans to learn, the ambiguity of language is what makes natural language processing
a difficult problem for computers to master.
, What are some open-source NLP libraries? - answer- Apache OpenNLP: a machine
learning toolkit that provides tokenizers, sentence segmentation, part-of-speech
tagging, named entity extraction, chunking, parsing, coreference resolution, and more.
- Natural Language Toolkit (NLTK): a Python library that provides modules for
processing text, classifying, tokenizing, stemming, tagging, parsing, and more.
- Standford NLP: a suite of NLP tools that provide part-of-speech tagging, the named
entity recognizer, coreference resolutionsystem, sentiment analysis, and more.
- MALLET: a Java package that provides Latent Dirichlet Allocation, document
classification, clustering, topic modeling, information extraction, and more.
These libraries provide the algorithmic building blocks of NLP in real-world applications.
Algorithmia provides a free API endpoint for many of these algorithms, without ever
having to setup or provision servers and infrastructure.
What are some NLP examples? - answer- Use Summarizer to automatically summarize
a block of text, exacting topic sentences, and ignoring the rest.
- Generate keyword topic tags from a document using LDA (Latent Dirichlet Allocation),
which determines the most relevant words from a document. This algorithm is at the
heart of the Auto-Tag and Auto-Tag URL microservices.
- Sentiment Analysis, based on StanfordNLP, can be used to identify the feeling,
opinion, or belief of a statement, from very negative, to neutral, to very positive. Often,
developers with use an algorithm to identify the sentiment of a term in a sentence, or
use sentiment analysis to analyze social media.
What's NLP's role in social media? - answerNLP can analyze language patterns to
understand text. 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.
Much can be gleaned from sentiment analysis. Companies can target unhappy
customers or, more importantly, find their competitors' unhappy customers, and
generate leads. I like to call these discoveries "actionable insights" — findings that can
be directly implemented into PR, marketing, adverting and sales efforts.
What are the limits of NLP in social media? - answerNLP technology lacks human-level
intelligence, at least for the foreseeable future. On a text-by-text basis, the system's
conclusions may be wrong as no automated sentiment analysis that currently exists can
handle certain level of nuance (sarcasm, for example).
Furthermore, certain expressions ("ima") or abbreviations ("#ff") fool the program,
especially when people have 140 characters or less to express their opinions, or when
they use slang, profanity, misspellings and neologisms.
Finally, much of social media interaction is personal, expressed between two people or
among a group, commonly in 1st or 2nd person contrasting with news or brand posts,
