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
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.
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
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.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
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.
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
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
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..
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) - 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.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
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.
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
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
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..
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) - 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.
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
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Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
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DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
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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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Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
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- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
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- central project shell command - contains all commands to manage project
- "IDE" - editor & shell
- edit/run/test cycle
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4. NLP
Natural language processing (NLP)
a field of computer science … concerned with the
interactions
between computers and human (natural)
languages.
!
https://en.wikipedia.org/wiki/Natural_language_processing
5. NLP
“between computers and human (natural)
languages”
1. computer -> human language
2. human language -> computer
6. NLP trend
• Internet is huge and easily accessible resource
of information
• BUT - information is mainly unstructured
• usually simple scraping (scrapy) is sufficient, but
sometimes it is not
• NLP solves or helps in converting free text
(unstructured information) to structural form
13. Word, term, feature
• word <> term
• document or text chunk is an unit / entity / object!
• terms are features of the document!
• each term has properties:
• normalized form -> term.baseform + term.transformation
• position(s) in the document -> term.position(s)
• frequency -> term.frequency
14. Text, document, chunk
• what is document?
• text segmentation
• hard problem
• usually we consider whole document as one
unit (entity)
15. Terms, features
• converting words -> terms
• term frequency is usually the most important feature!
• how to get the list of terms with frequencies:
• preprocessing - e.g. remove all but words, remove stopwords,
tokenization (regexp)
• word normalization
dog ~ dogs zeleno ~ najzelenijih
• .tolower(), regexp, stemming, lemmatization
• much harder for inflectional languages, e.g. Croatian, see text-hr :)
16. Term weight - TF-IDF
• term frequency – inverse document frequency
• variables:
• t - term,
• d - one document
• D - all documents
• TF - is term frequency in a document function - i.e. measure on how
much information the term brings in one document
• IDF - is inverse document frequency of the term function - i.e.
inversed measure on how much information the term brings in all
documents (corpus)
17. Terms position, syntax
• sometimes term position is important
• neighbours, collocation, phrase extraction, NER
• from regexp to parsers
• syntax trees
• complex, cpu intensive
18. Terms position, syntax
In their public lectures they have even claimed that the only
evidence that Khufu built the pyramid is the graffiti found in the five
chambers.
20. Bag of words
• simplified and effective way to process
documents by:
• disregarding grammar (term.baseform?)
• disregarding word order (term.position)
• keeping only multiplicity (term.frequency)
21. Bag of words
• sparse matrix
• numbers can be:
• binary - 0/1
• simple term frequency
• weight - e.g. TF-IDF
22. Bag of words
• very simple -> very fast
• frequently used:
• in index servers
• in database for simple full-text-search
operations
• for processing of large datasets
24. Machine learning
• one of the Machine learning application is NLP
• after text is converted to entities with features,
machine learning techniques can be applied
25. Machine learning
• ML algorithm families categorisation
• supervised - classification (distinct), regression (numerical)
• unsupervised - clustering
• A lot of various methods/algorithm families, statistical,
probabilistic, …
decision trees, neural networks / deep learning,
support vector machines, bayesian networks,
markov models, genetic algorithms