These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Natural Language Processing (NLP) - IntroductionAritra Mukherjee
This presentation provides a beginner-friendly introduction towards Natural Language Processing in a way that arouses interest in the field. I have made the effort to include as many easy to understand examples as possible.
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Natural Language Processing (NLP) - IntroductionAritra Mukherjee
This presentation provides a beginner-friendly introduction towards Natural Language Processing in a way that arouses interest in the field. I have made the effort to include as many easy to understand examples as possible.
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
Natural Language Processing: State of The Art, Current Trends and Challengesantonellarose
Diksha Khurana1
, Aditya Koli1
, Kiran Khatter1,2 and Sukhdev Singh1,2
1Department of Computer Science and Engineering
Manav Rachna International University, Faridabad-121004, India
2Accendere Knowledge Management Services Pvt. Ltd., India
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
A SURVEY OF GRAMMAR CHECKERS FOR NATURAL LANGUAGEScsandit
ABSTRACT
Natural Language processing is an interdisciplinary branch of linguistic and computer science studied under the Artificial Intelligence (AI) that gave birth to an allied area called
‘Computational Linguistic’ which focuses on processing of natural languages on computational devices. A natural language consists of a large number of sentences which are linguistic units involving one or more words linked together in accordance with a set of predefined rules called grammar. Grammar checking is the task of validating sentences syntactically and is a prominent tool within language engineering. Our review draws on the recent development of various grammar checkers to look at past, present and the future in a new light. Our review covers grammar checkers of many languages with the aim of seeking their approaches, methodologies for developing new tool and system as a whole. The survey concludes with the discussion of various features included in existing grammar checkers of foreign languages as well as a few Indian Languages.
Domain Specific Terminology Extraction (ICICT 2006)IT Industry
Imran Sarwar Bajwa, M. Imran Siddique, M. Abbas Choudhary, [2006], "Automatic Domain Specific Terminology Extraction using a Decision Support System", in IEEE 4th International Conference on Information and Communication Technology (ICICT 2006), Cairo, Egypt. pp:651-659
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
Natural language processing PPT presentationSai Mohith
A ppt presentation for technicial seminar on the topic Natural Language processing
References used:
Slideshare.net
wikipedia.org NLP
Stanford NLP website
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning.
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
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3. Problem of Natural Language
“Human language is highly ambiguous … It is also ever changing
and evolving. People are great at producing language and
understanding language, and are capable of expressing,
perceiving, and interpreting very elaborate and nuanced
meanings. At the same time, while we humans are great users of
language, we are also very poor at formally understanding and
describing the rules that govern language.”
- Page 1, Neural Network Methods in Natural Language Processing, 2017.
Source : https://machinelearningmastery.com/natural-language-processing/
3Venkatesh Murugadas
4. “It is hard from the standpoint of the child, who must spend many
years acquiring a language … it is hard for the adult language
learner, it is hard for the scientist who attempts to model the
relevant phenomena, and it is hard for the engineer who attempts
to build systems that deal with natural language input or output.
These tasks are so hard that Turing could rightly make fluent
conversation in natural language the centrepiece of his test for
intelligence.”
- Page 248, Mathematical Linguistics, 2010.
Source : https://machinelearningmastery.com/natural-language-processing/
4Venkatesh Murugadas
5. Computer Linguistics
Linguistics is the scientific study of language, including its grammar,
semantics, and phonetics.
Computational linguistics is the modern study of linguistics using the
tools of computer science. Yesterday’s linguistics may be today’s
computational linguist as the use of computational tools and thinking
has overtaken most fields of study.
Source : https://machinelearningmastery.com/natural-language-
processing/
5Venkatesh Murugadas
6. Statistical NLP
Statistical NLP aims to do statistical inference for the field
of natural language. Statistical inference in general
consists of taking some data (generated in accordance
with some unknown probability distribution) and then
making some inference about this distribution.
— Page 191, Foundations of Statistical Natural Language Processing, 1999.
Source : https://machinelearningmastery.com/natural-language-
processing/
6Venkatesh Murugadas
7. Natural language processing (NLP) is a collective term
referring to automatic computational processing of
human languages. This includes both algorithms that
take human-produced text as input, and algorithms
that produce natural looking text as outputs.
— Page xvii, Neural Network Methods in Natural Language Processing, 2017.
Source : https://machinelearningmastery.com/natural-language-processing/
Natural language processing is a subfield of computer science, information engineering, and
artificial intelligence concerned with the interactions between computers and human languages, in
particular how to program computers to process and analyze large amounts of natural language
!7Venkatesh Murugadas
8. We will take Natural Language Processing — or NLP for short
–in a wide sense to cover any kind of computer manipulation
of natural language. At one extreme, it could be as simple as
counting word frequencies to compare different writing
styles. At the other extreme, NLP involves “understanding”
complete human utterances, at least to the extent of being
able to give useful responses to them.
— Page ix, Natural Language Processing with Python, 2009.
Source : https://machinelearningmastery.com/natural-language-
processing/
8Venkatesh Murugadas
9. Areas of NLP
• Natural Language Understanding
• Natural Language Search
• Natural Language Generation
• Natural Language Interface
Venkatesh Murugadas
10. Applications of NLP
1. Text classification and Categorisation
2. Named Entity Recognition
3. Conversational AI
4. Paraphrase detection
5. Language generation and Multi-document Summarisation
6. Machine Translation
7. Speech recognition
8. Spell Checking
10Venkatesh Murugadas
11. Corpus
• “A corpus is a large body of natural language text used for
accumulating statistics on natural language text. The plural
is corpora. Corpora often include extra information such as
a tag for each word indicating its part-of-speech, and
perhaps the parse tree for each sentence.”
Source : https://www.quora.com/In-NLP-what-is-the-difference-between-a-Lexicon-and-a-Corpus
11Venkatesh Murugadas
12. NLP Pipeline
• Word Tokenisation
• Sentence Segmentation
• Parts of Speech Tagging
• Dependency Parsing
• Named Entity Recognition
• Relation Extraction
12Venkatesh Murugadas
13. Word Tokenisation
Token :
“A token is an instance of a sequence of characters in some particular document that are grouped
together as a useful semantic unit for processing.” Eg. To sleep perhaps to dream.
Type:
“A type is the class of all tokens containing the same character sequence.”
Term :
“A term is a (perhaps normalized) type that is included in the dictionary.”
Text Normalisation :
“Token normalization is the process of creating tokens, so that matches occur despite superficial
differences in the character sequences of the tokens”
Source: nlp.anirbansaha.com
Tokenization is an identification of
basic units to be processed.
Tokenizer must often be customised
to the data in question.
13Venkatesh Murugadas
14. How is tokenisation done?
• NLTK ( Natural Language Tool Kit) and SpaCy language models use Regular
Expressions (Regex) to create tokens from the running sequence of texts.
• NLTK - Penn Treebank Tokenizer , Word Punct Tokenizer , Tweet Tokenizer ,
MWETokenizer (Multi word Expression Tokenizer)
• This is language dependent.
• Languages in which white spaces are not present, such as Chinese, Japanese and
Korean they use the technique called Word Segmentation.
!14Venkatesh Murugadas
15. • Problems
• Hyphenated words - co-operative, self-esteem
• URL’s - “https://www.google.com/"
• Phone numbers - (541) 754-3010
• Compound nouns (Names , Places) - New York
15Venkatesh Murugadas
16. Sentence Segmentation
• It is splitting the running text by detecting the sentence boundary.
• Sentence Boundary Detection.
• NLTK uses the class Punkt Sentence Tokenizer. This is the most widely used
sentence tokenizer.
16Venkatesh Murugadas
17. Punkt Architecture
Source: Unsupervised Multilingual Sentence Boundary Detection ( Tibor Kiss, Jan Strunk)
Type based Classification (Initials,
Ordinal numbers, Texts)
1. Strong Collocation
2. Internal Periods
3. Penalty
Token based Classification
1. Orthographic Heuristics - Word
shape
2. The Collocation Heuristics
3. Frequent sentence Starter
Heuristic
17Venkatesh Murugadas
19. Parts of Speech Tagging
• Part-of-Speech tagging in itself may not be the solution to any particular NLP
problem. It is however something that is done as a pre-requisite to simplify a lot of
different problems
• 8 Parts of Speech in English
• There are open classes and closed classes.
• Open class - Noun, Verb, Adverb, Adjective
• There are languages in which there is no classification of Parts of Speech, such as
Riau Indonesian. Korean language do not have Adjectives.
NOUN.
PRONOUN.
VERB.
ADJECTIVE.
ADVERB.
PREPOSITION.
CONJUNCTION
INTERJECTION.
19Venkatesh Murugadas
20. • There are 8 to 45 POS tags present.
• The mainly used tagged corpora :
• Brown corpus - with a million word
• Wall Street Journal corpus - with a
million word
• Switchboard : telephone speech
corpus - with 2 million words
Source: Speech and Language Processing, Daniel Jurafsky and James H. Martin
20Venkatesh Murugadas
21. Source: Speech and Language Processing, Daniel Jurafsky and James H. Martin
Parts of Speech Tagging algorithm
Generative Hidden Markov Model
21Venkatesh Murugadas
22. Parts of Speech Tagging algorithm
Discriminative Maximum Entropy Markov Model
Source: Speech and Language Processing, Daniel Jurafsky and James H. Martin
Discriminative model to incorporate a
lot of features based on which the
classification will be better.
There is a feature template.
22Venkatesh Murugadas
23. • The modern POS tagging algorithms
use Bidirectional methods.
• The Stanford core NLP uses a log-
linear Parts of Speech Tagger.
• Based on the paper : https://
nlp.stanford.edu/~manning/papers/
tagging.pdf
23Venkatesh Murugadas
24. Dependency Parser
• Dependency syntax postulates that syntactic structure consists
of lexical items linked by binary asymmetric relations (“arrows”)
called dependencies.
• The arrows are commonly typed with name of grammatical
relations.
• So dependencies form a tree (connected, acyclic, single-head)
24Venkatesh Murugadas
25. • Shallow parsing (also chunking, "light
parsing") is an analysis of a sentence which
first identifies constituent parts of
sentences (nouns, verbs, adjectives, etc.)
and then links them to higher order units
that have discrete grammatical meanings
(noun groups or phrases, verb groups, etc.).
25Venkatesh Murugadas
26. • The Stanford NLP core is
based on the paper : https://
nlp.stanford.edu/~sebschu/
pubs/schuster-manning-
lrec2016.pdf
26Venkatesh Murugadas
28. Named Entity Recognition
Named-entity recognition (NER) (also known as entity identification, entity
chunking and entity extraction) is a subtask of information extraction that
seeks to locate and classify named entity mentions in unstructured text into
pre-defined categories such as the person names, organisations, locations,
medical codes, time expressions, quantities, monetary values, percentages, etc.
Source: https://en.wikipedia.org/wiki/Named-entity_recognition
28Venkatesh Murugadas
29. Noun Phrase Chunking
• This is a basic technique used for entity detection.
• Each of these larger boxes is called a chunk.
• This is done with the help of Regular Expression. (RegEx)
Source: https://www.nltk.org/book/ch07.html
29Venkatesh Murugadas
30. Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which
first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and
then links them to higher order units that have discrete grammatical meanings
(noun groups or phrases, verb groups, etc.).
Image Source: https://www.nltk.org/book/ch07.html
Source: https://en.wikipedia.org/wiki/Shallow_parsing
30Venkatesh Murugadas
31. • Noun chunking using
Regular Expression
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32. • SpaCy based Named Entity Recognition
• It is trained on the dataset OntoNotes5.
• There are 7 pre-defined categories of
Entities.
32
Applications of NER
1. NLU
2. NLS
Venkatesh Murugadas