ppt
•Download as PPT, PDF•
1 like•478 views
The document provides an overview of machine learning for natural language processing (NLP) tasks. It discusses framing NLP problems as supervised learning tasks, preprocessing text, feature extraction using the FEX tool, and examples of NLP tasks like part-of-speech tagging and named entity recognition that can be solved using these techniques. It also describes the typical components of a machine learning system for NLP, including preprocessing, feature extraction, classifiers, and evaluation.
Report
Share
Report
Share
![Goals of Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Parts of Speect Tagging
The document discusses parts-of-speech (POS) tagging. It defines POS tagging as labeling each word in a sentence with its appropriate part of speech. It provides an example tagged sentence and discusses the challenges of POS tagging, including ambiguity and open/closed word classes. It also discusses common tag sets and stochastic POS tagging using hidden Markov models.
Sketch engine presentation
Sketch Engine is a web-based tool for analyzing corpora. It allows users to generate word sketches, view concordances, find similar words using the thesaurus, and compare the behavior of words. Key functions include the concordancer, word lists, word sketches, thesaurus, and word sketch difference. Users can analyze pre-loaded corpora or upload their own texts for tokenization, lemmatization, and POS tagging to build custom corpora.
MACHINE-DRIVEN TEXT ANALYSIS
Natural language processing (NLP) involves developing systems that can process and understand human language. This document discusses NLP tools and techniques for representing text numerically so it can be analyzed by machine learning algorithms. It covers topics like tokenization, part-of-speech tagging, named entity recognition, vector space models, term frequency-inverse document frequency (TF-IDF) weighting, and word embeddings which represent words as dense vectors of numbers. Popular Python libraries for NLP and text analysis are also introduced.
NLP_KASHK:Text Normalization
The document discusses text normalization, which involves segmenting and standardizing text for natural language processing. It describes tokenizing text into words and sentences, lemmatizing words into their root forms, and standardizing formats. Tokenization involves separating punctuation, normalizing word formats, and segmenting sentences. Lemmatization determines that words have the same root despite surface differences. Sentence segmentation identifies sentence boundaries, which can be ambiguous without context. Overall, text normalization prepares raw text for further natural language analysis.
File handling & regular expressions in python programming
The document discusses file handling and regular expressions in Python programming. It covers opening, reading, and writing files in both text and binary modes. It also describes parsing text files using built-in functions and regular expressions. Regular expressions topics covered include characters, character classes, quantifiers, grouping, capturing, assertions, and flags. The document provides examples of using the re module to search and manipulate strings using regular expression patterns.
Logic programming (1)
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
Information for learning object exchange
The document discusses Information for Learning Object eXchange (ILOX), which uses the Functional Requirements for Bibliographic Records (FRBR) conceptual model to describe learning objects at different levels of abstraction (Work, Expression, Manifestation, Item). It provides examples of metadata that could be used at each FRBR level and discusses best practices for selecting the appropriate level and metadata when describing learning objects in different contexts like harvesting, searching, and publishing. ILOX is compared to the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) standard, noting they are orthogonal and can be combined.
Prolog (present)
Prolog is a logic programming language based on mathematical logic. It was invented in 1971 and allows programmers to model human logic and decision making. Prolog uses Horn clauses to express statements and performs backward chaining to prove goals by working backwards from what it is trying to prove to the facts. It is commonly used for intelligent data retrieval, expert systems, and other artificial intelligence applications that require symbolic reasoning.
Recommended
Parts of Speect Tagging
The document discusses parts-of-speech (POS) tagging. It defines POS tagging as labeling each word in a sentence with its appropriate part of speech. It provides an example tagged sentence and discusses the challenges of POS tagging, including ambiguity and open/closed word classes. It also discusses common tag sets and stochastic POS tagging using hidden Markov models.
Sketch engine presentation
Sketch Engine is a web-based tool for analyzing corpora. It allows users to generate word sketches, view concordances, find similar words using the thesaurus, and compare the behavior of words. Key functions include the concordancer, word lists, word sketches, thesaurus, and word sketch difference. Users can analyze pre-loaded corpora or upload their own texts for tokenization, lemmatization, and POS tagging to build custom corpora.
MACHINE-DRIVEN TEXT ANALYSIS
Natural language processing (NLP) involves developing systems that can process and understand human language. This document discusses NLP tools and techniques for representing text numerically so it can be analyzed by machine learning algorithms. It covers topics like tokenization, part-of-speech tagging, named entity recognition, vector space models, term frequency-inverse document frequency (TF-IDF) weighting, and word embeddings which represent words as dense vectors of numbers. Popular Python libraries for NLP and text analysis are also introduced.
NLP_KASHK:Text Normalization
The document discusses text normalization, which involves segmenting and standardizing text for natural language processing. It describes tokenizing text into words and sentences, lemmatizing words into their root forms, and standardizing formats. Tokenization involves separating punctuation, normalizing word formats, and segmenting sentences. Lemmatization determines that words have the same root despite surface differences. Sentence segmentation identifies sentence boundaries, which can be ambiguous without context. Overall, text normalization prepares raw text for further natural language analysis.
File handling & regular expressions in python programming
The document discusses file handling and regular expressions in Python programming. It covers opening, reading, and writing files in both text and binary modes. It also describes parsing text files using built-in functions and regular expressions. Regular expressions topics covered include characters, character classes, quantifiers, grouping, capturing, assertions, and flags. The document provides examples of using the re module to search and manipulate strings using regular expression patterns.
Logic programming (1)
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
Information for learning object exchange
The document discusses Information for Learning Object eXchange (ILOX), which uses the Functional Requirements for Bibliographic Records (FRBR) conceptual model to describe learning objects at different levels of abstraction (Work, Expression, Manifestation, Item). It provides examples of metadata that could be used at each FRBR level and discusses best practices for selecting the appropriate level and metadata when describing learning objects in different contexts like harvesting, searching, and publishing. ILOX is compared to the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) standard, noting they are orthogonal and can be combined.
Prolog (present)
Prolog is a logic programming language based on mathematical logic. It was invented in 1971 and allows programmers to model human logic and decision making. Prolog uses Horn clauses to express statements and performs backward chaining to prove goals by working backwards from what it is trying to prove to the facts. It is commonly used for intelligent data retrieval, expert systems, and other artificial intelligence applications that require symbolic reasoning.
Introduction to programming with python
Introduction to programming with python slides from the seminar held in "World University of Bangladesh(WUB)"
1. python programming
Python is a general-purpose programming language that is highly readable. It uses English keywords and has fewer syntactical constructions than other languages. Python supports object-oriented, interactive, and procedural programming. It has various data types like numbers, strings, lists, tuples and dictionaries. Python uses constructs like if/else, for loops, functions and classes to control program flow and structure code.
introduction to python
Python is an open source programming language created by Guido van Rossum in 1991. It is named after the comedy group Monty Python and is based on the ABC language. Python supports both procedural and object-oriented programming and can be used for web development, data analysis, artificial intelligence, and more. It has a simple syntax and large standard library that make it easy to learn and use for various applications.
Python Presentation
Python is an interpreted, object-oriented programming language created by Guido van Rossum in 1990. It has a clear, readable syntax and is designed to be highly extensible. Python code is often much shorter than equivalent code in other languages like C++ or Java due to features like indentation-based blocks and dynamic typing. It is used for web development, scientific computing, and more.
8 issues in pos tagging
This document discusses several issues in part-of-speech (POS) tagging for Tamil language texts. It identifies challenges such as resolving lexical ambiguity, normalization of corpora, and analyzing compound words and multi-word expressions. It also discusses issues like determining the fineness versus coarseness of linguistic analysis, distinguishing syntactic function from lexical category, and whether to use new tags or tags from standard tagsets. Specific examples are provided to illustrate POS ambiguity depending on context, like the word "paṭi" which can be a noun, verb, adjective, adverb, or particle.
11 Unit 1 Chapter 02 Python Fundamentals
This document provides an introduction to Python fundamentals. It discusses Python's character set, tokens or lexical units including keywords, identifiers, literals, operators, and punctuators. It also covers Python programming concepts such as variables and assignments, functions, comments, statements, and programming conventions regarding whitespace, maximum line length, and case sensitivity. The document aims to explain the basic building blocks of the Python language to learn Python programming.
Introduction to prolog
Prolog is a declarative logic programming language where programs consist of facts and rules. Facts are terms that are always true, while rules define relationships between terms using logic notation "if-then". A Prolog program is run by asking queries of the program's database. Variables must start with an uppercase letter and are used to represent unknown values, while atoms are constants that represent known values.
Fundamentals of Python Programming
The document provides an introduction to Python programming including its features, uses, history, and installation process. Some key points covered include:
- Python is an interpreted, object-oriented programming language that is used for web development, scientific computing, and desktop applications.
- It was created by Guido van Rossum in 1991 and named after the Monty Python comedy group.
- To install Python on Windows, users download the latest version from python.org and run the installer, which also installs the IDLE development environment.
- The document then covers basic Python concepts like variables, data types, operators, and input/output functions.
Get started python programming part 1
This document provides an overview of Python programming concepts including the basics of Python, strings, control structures like if/else statements and loops, and input/output functions. It discusses what a program and programming are, gives an introduction to Python including its uses and installation. It then covers key Python concepts like variables, data types, operators, functions, modules and object-oriented programming.
Python Interview questions 2020
This document provides a list of 20 frequently asked Python interview questions and their answers. Some of the key topics covered include what Python is, its advantages like being free and open source, PEP 8 coding style guidelines, memory management in Python using a private heap and garbage collector, namespaces, iterators, generators, slicing, dictionaries, and differences between Python 2.x and 3.x.
Python Tutorial Part 1
This document provides an agenda and overview for a Python tutorial presented over multiple sessions. The first session introduces Python and demonstrates how to use the Python interpreter. The second session covers basic Python data structures like lists, modules, input/output, and exceptions. An optional third session discusses unit testing. The document explains that Python is an easy to learn yet powerful programming language that supports object-oriented programming and high-level data structures in an interpreted, dynamic environment.
Relationship Among Token, Lexeme & Pattern
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
SE Michigan PowerShell Users Group - Regex Part1
The document introduces regular expressions and provides examples of their use. It discusses using simple wildcards with operating system commands and PowerShell commands. It then defines regular expressions and lists examples of using them to match patterns in strings. Websites and books for learning more about regular expressions are also referenced.
R P G Generator
This is my Python Role Player Game Generator toolkit. It can be used to create multiple RPG\'s by simply creating the story and the logic, it will then create random characters and place them in the game.
The Python RPG Generator is a toolset that allows developers to build role-playing games without rewriting core code. It includes programs for dice rolling, name generation, character creation, story writing, and integrating all elements into a full RPG. Character creation considers race, specialty, skills, powers and tools defined in text files. Story files use shortcuts to insert character details dynamically. The main program pulls everything together to generate and present the game based on the story.
Nltk - Boston Text Analytics
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/
Introduction to Python
The document provides an introduction to Python programming. It discusses installing and running Python, basic Python syntax like variables, data types, conditionals, and functions. It emphasizes that Python uses references rather than copying values, so assigning one variable to another causes both to refer to the same object.
Andy On Closures
Closures allow blocks of code to access variables from the enclosing scope even after the scope has closed. This allows closures to maintain state even when passed around or returned from functions. Closures originated in Lisp and are supported in many modern languages like Python, Ruby, and C#, providing benefits like elegant iteration, filtering, and caching of results. While objects can also be used to inject strategy or share state, closures provide a more concise syntax and avoid wrapper classes in many cases.
Traits: A New Language Feature for PHP?
The document discusses traits, a language feature that allows code reuse and composition. It describes traits as sets of methods that can be composed together without order. Several case studies are presented that demonstrate how traits reduced code duplication and improved code organization. The document also discusses different approaches to implementing traits in PHP, including traits and grafts, and debates the advantages and limitations of each. It concludes by discussing how traits allow achieving consistent class hierarchies while avoiding code duplication and enhancing class composition capabilities.
Reversing Google Protobuf protocol
Prasanna K's talk on reversing google's protobuf protocol, delivered at first Null singapore meet up.
Mahout Tutorial FOSSMEET NITC
This document provides an overview and introduction to the Apache Mahout machine learning library. It discusses how Mahout can be used to build scalable machine learning applications using algorithms like collaborative filtering, clustering, classification and frequent pattern mining. It also describes how Mahout can be used for recommendation engines and document classification. The document concludes with references and resources for learning more about Mahout.
Query Linguistic Intent Detection
This document provides an overview of supervised and semi-supervised machine learning techniques for natural language processing (NLP). It begins with an introduction on why machine learning is important for NLP. It then discusses three examples of supervised learning tasks: review classification, relevance ranking, and machine translation. The remainder of the document covers supervised learning methods including generative models, discriminative models such as AdaBoost and support vector machines (SVMs), and applications to NLP problems. It concludes by mentioning opportunities to work with Microsoft on related research.
More Related Content
What's hot
Introduction to programming with python
Introduction to programming with python slides from the seminar held in "World University of Bangladesh(WUB)"
1. python programming
Python is a general-purpose programming language that is highly readable. It uses English keywords and has fewer syntactical constructions than other languages. Python supports object-oriented, interactive, and procedural programming. It has various data types like numbers, strings, lists, tuples and dictionaries. Python uses constructs like if/else, for loops, functions and classes to control program flow and structure code.
introduction to python
Python is an open source programming language created by Guido van Rossum in 1991. It is named after the comedy group Monty Python and is based on the ABC language. Python supports both procedural and object-oriented programming and can be used for web development, data analysis, artificial intelligence, and more. It has a simple syntax and large standard library that make it easy to learn and use for various applications.
Python Presentation
Python is an interpreted, object-oriented programming language created by Guido van Rossum in 1990. It has a clear, readable syntax and is designed to be highly extensible. Python code is often much shorter than equivalent code in other languages like C++ or Java due to features like indentation-based blocks and dynamic typing. It is used for web development, scientific computing, and more.
8 issues in pos tagging
This document discusses several issues in part-of-speech (POS) tagging for Tamil language texts. It identifies challenges such as resolving lexical ambiguity, normalization of corpora, and analyzing compound words and multi-word expressions. It also discusses issues like determining the fineness versus coarseness of linguistic analysis, distinguishing syntactic function from lexical category, and whether to use new tags or tags from standard tagsets. Specific examples are provided to illustrate POS ambiguity depending on context, like the word "paṭi" which can be a noun, verb, adjective, adverb, or particle.
11 Unit 1 Chapter 02 Python Fundamentals
This document provides an introduction to Python fundamentals. It discusses Python's character set, tokens or lexical units including keywords, identifiers, literals, operators, and punctuators. It also covers Python programming concepts such as variables and assignments, functions, comments, statements, and programming conventions regarding whitespace, maximum line length, and case sensitivity. The document aims to explain the basic building blocks of the Python language to learn Python programming.
Introduction to prolog
Prolog is a declarative logic programming language where programs consist of facts and rules. Facts are terms that are always true, while rules define relationships between terms using logic notation "if-then". A Prolog program is run by asking queries of the program's database. Variables must start with an uppercase letter and are used to represent unknown values, while atoms are constants that represent known values.
Fundamentals of Python Programming
The document provides an introduction to Python programming including its features, uses, history, and installation process. Some key points covered include:
- Python is an interpreted, object-oriented programming language that is used for web development, scientific computing, and desktop applications.
- It was created by Guido van Rossum in 1991 and named after the Monty Python comedy group.
- To install Python on Windows, users download the latest version from python.org and run the installer, which also installs the IDLE development environment.
- The document then covers basic Python concepts like variables, data types, operators, and input/output functions.
Get started python programming part 1
This document provides an overview of Python programming concepts including the basics of Python, strings, control structures like if/else statements and loops, and input/output functions. It discusses what a program and programming are, gives an introduction to Python including its uses and installation. It then covers key Python concepts like variables, data types, operators, functions, modules and object-oriented programming.
Python Interview questions 2020
This document provides a list of 20 frequently asked Python interview questions and their answers. Some of the key topics covered include what Python is, its advantages like being free and open source, PEP 8 coding style guidelines, memory management in Python using a private heap and garbage collector, namespaces, iterators, generators, slicing, dictionaries, and differences between Python 2.x and 3.x.
Python Tutorial Part 1
This document provides an agenda and overview for a Python tutorial presented over multiple sessions. The first session introduces Python and demonstrates how to use the Python interpreter. The second session covers basic Python data structures like lists, modules, input/output, and exceptions. An optional third session discusses unit testing. The document explains that Python is an easy to learn yet powerful programming language that supports object-oriented programming and high-level data structures in an interpreted, dynamic environment.
Relationship Among Token, Lexeme & Pattern
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
SE Michigan PowerShell Users Group - Regex Part1
The document introduces regular expressions and provides examples of their use. It discusses using simple wildcards with operating system commands and PowerShell commands. It then defines regular expressions and lists examples of using them to match patterns in strings. Websites and books for learning more about regular expressions are also referenced.
R P G Generator
This is my Python Role Player Game Generator toolkit. It can be used to create multiple RPG\'s by simply creating the story and the logic, it will then create random characters and place them in the game.
The Python RPG Generator is a toolset that allows developers to build role-playing games without rewriting core code. It includes programs for dice rolling, name generation, character creation, story writing, and integrating all elements into a full RPG. Character creation considers race, specialty, skills, powers and tools defined in text files. Story files use shortcuts to insert character details dynamically. The main program pulls everything together to generate and present the game based on the story.
Nltk - Boston Text Analytics
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/
Introduction to Python
The document provides an introduction to Python programming. It discusses installing and running Python, basic Python syntax like variables, data types, conditionals, and functions. It emphasizes that Python uses references rather than copying values, so assigning one variable to another causes both to refer to the same object.
Andy On Closures
Closures allow blocks of code to access variables from the enclosing scope even after the scope has closed. This allows closures to maintain state even when passed around or returned from functions. Closures originated in Lisp and are supported in many modern languages like Python, Ruby, and C#, providing benefits like elegant iteration, filtering, and caching of results. While objects can also be used to inject strategy or share state, closures provide a more concise syntax and avoid wrapper classes in many cases.
Traits: A New Language Feature for PHP?
The document discusses traits, a language feature that allows code reuse and composition. It describes traits as sets of methods that can be composed together without order. Several case studies are presented that demonstrate how traits reduced code duplication and improved code organization. The document also discusses different approaches to implementing traits in PHP, including traits and grafts, and debates the advantages and limitations of each. It concludes by discussing how traits allow achieving consistent class hierarchies while avoiding code duplication and enhancing class composition capabilities.
Reversing Google Protobuf protocol
Prasanna K's talk on reversing google's protobuf protocol, delivered at first Null singapore meet up.
What's hot (20)
Viewers also liked
Mahout Tutorial FOSSMEET NITC
This document provides an overview and introduction to the Apache Mahout machine learning library. It discusses how Mahout can be used to build scalable machine learning applications using algorithms like collaborative filtering, clustering, classification and frequent pattern mining. It also describes how Mahout can be used for recommendation engines and document classification. The document concludes with references and resources for learning more about Mahout.
Query Linguistic Intent Detection
This document provides an overview of supervised and semi-supervised machine learning techniques for natural language processing (NLP). It begins with an introduction on why machine learning is important for NLP. It then discusses three examples of supervised learning tasks: review classification, relevance ranking, and machine translation. The remainder of the document covers supervised learning methods including generative models, discriminative models such as AdaBoost and support vector machines (SVMs), and applications to NLP problems. It concludes by mentioning opportunities to work with Microsoft on related research.
Machine Learning for NLP
This document discusses machine learning algorithms for natural language processing (NLP) classification problems. It introduces decision trees as a machine learning algorithm that recursively partitions training data using hierarchical tree structures. Decision trees are learned from labeled training examples using a top-down induction approach where the training data is recursively split based on feature tests until some stopping criteria is reached. Common algorithms for learning decision trees include ID3, C4.5, and CART.
NLP & Machine Learning - An Introductory Talk
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
Deeplearning NLP
This document provides an introduction to deep learning for natural language processing (NLP) over 50 minutes. It begins with a brief introduction to NLP and deep learning, then discusses traditional NLP techniques like one-hot encoding and clustering-based representations. Next, it covers how deep learning addresses limitations of traditional methods through representation learning, learning from unlabeled data, and modeling language recursively. Several examples of neural networks for NLP tasks are presented like image captioning, sentiment analysis, and character-based language models. The document concludes with discussing word embeddings, document representations, and the future of deep learning for NLP.
Introduction to Natural Language Processing
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
Viewers also liked (6)
Similar to ppt
Nltk
This document provides an introduction to natural language processing (NLP) and the Natural Language Toolkit (NLTK) module for Python. It discusses how NLP aims to develop systems that can understand human language at a deep level, lists common NLP applications, and explains why NLP is difficult due to language ambiguity and complexity. It then describes how corpus-based statistical approaches are used in NLTK to tackle NLP problems by extracting features from text corpora and using statistical models. The document gives an overview of the main NLTK modules and interfaces for common NLP tasks like tagging, parsing, and classification. It provides an example of word tokenization and discusses tokens and types in NLTK.
NLP Deep Learning with Tensorflow
Understand what is natural language process and how can we approach this problem with deep learning especially using google tensorflow
Text classification-php-v4
This document summarizes text classification in PHP. It discusses what text classification is, common natural language processing terminology like tokenization and stemming, Bayes' theorem and how it relates to naive Bayes classification. It provides examples of tokenizing, stemming, stopping words, and building a naive Bayes classifier in PHP using the NlpTools library. Key steps like training and testing a classifier on sample text data are demonstrated.
Day2-Slides.ppt pppppppppppppppppppppppppp
This document provides an overview of natural language processing (NLP) tools and resources that can be used to build a machine learning classifier to identify the fame of people mentioned in news articles. It describes NLP tasks like tokenization, part-of-speech tagging, chunking, named entity recognition, parsing, and coreference resolution. It also introduces libraries like the Curator for accessing NLP tools, Edison for feature extraction, and Learning Based Java for building the classifier. Finally, it demonstrates connecting all the pieces to construct a system that can label famous people as politicians, athletes, or corporate moguls.
Feature Engineering for NLP
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
Open nlp presentationss
This document provides an overview of the OpenNLP natural language processing tool. It discusses the various NLP tasks that OpenNLP can perform, including tokenization, POS tagging, named entity recognition, chunking, parsing, and co-reference resolution. It also describes how models for these tasks are trained in OpenNLP using annotated training data. The document concludes by listing some advantages and limitations of OpenNLP.
Dsm as theory building
1. Domain Specific Modelling can be viewed as a form of theory building, where a domain is understood by developing theories about it consisting of true statements.
2. Traditionally, programming is seen as implementing a theory, but modelling languages treat the domain weakly and lack support for truly representing theories.
3. The paper proposes enhancing modelling languages to more fully support domain theories, including defining syntax, semantics, and mappings between theories and implementations.
Industrial strength - Natural Language Processing
Jeffrey Williams presented on using the spaCy library for natural language processing. He began with an overview of key NLP concepts like tokenization, lemmatization, named entity recognition and part-of-speech tagging. He then demonstrated spaCy's features for these tasks and how to visualize outputs. Williams also showed how to apply spaCy to problems by extending its pipeline and training custom models. Finally, he reviewed alternatives to spaCy like NLTK, CoreNLP and cloud-based services from Microsoft and Google.
Similarity computation exploiting the semantic and syntactic inherent structu...
Presented at Malaga, Spain on 23th Nov, 2017, @ ICSOC 2017 ( The 15th International Conference on Service-Oriented Computing )
Text Mining Infrastructure in R
R is a free software environment for statistical analysis and graphics. This document discusses using R for text mining, including preprocessing text data through transformations like stemming, stopword removal, and part-of-speech tagging. It also demonstrates building term document matrices and classifying text with k-nearest neighbors (KNN) algorithms. Specifically, it shows classifying speeches from Obama and Romney with over 90% accuracy using KNN classification in R.
Machine Learning Applications in NLP.ppt
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
Erlang session1
I gave two Erlang sessions for FCIS in 2010 (a third was planned but unfortunately not given). This is part 1.
Natural Language Processing (NLP)
Slides for my talk at NLP Morning@Lohika.
Part 1: Essential Machine Learning Background for NLP.
Part 2: NLP Challenges and Approaches.
Sk t academy lecture note
The document discusses a lecture on developing an AI chatbot using Python and TensorFlow, covering setting up a Docker environment, explaining example code in Jupyter notebooks, and introducing the two speakers and their backgrounds working on machine learning and chatbots.
Tools for the Toolmakers
The document summarizes the design and implementation of a new programming language called newt. Key points include:
- newt aims to provide tight feedback loops through static typing and fast failure on semantic errors. It has succinct syntax while avoiding surprising behavior.
- The language is implemented in C++ and includes features like primitive types, arrays, records, functions, and automated testing.
- Challenges included generalizing type specifiers and making functions truly first-class. Future work includes collections, polymorphism, and syntax sugar.
Designing A Syntax Based Retrieval System03
The document proposes a syntax-based text retrieval system to support grammatical querying of tagged corpora for language learners and teachers. It describes building an index of part-of-speech tagged n-grams from a corpus, with a filter to select discriminative index terms. A regular expression query is rewritten and its positions in the index are used to find candidate matching text units efficiently. An evaluation compares the proposed index to a complete index in terms of size and query performance.
Poetic APIs
API design is one of the most difficult areas of programming. Besides solving your immediate problem, you must also accomodate unknown future ones—and fit nicely into other people's brains. Let's explore how to do this without a time machine, considering compactness, orthogonality, consistency, safety, coupling, state handling, and the messy interface with human cognition, all illustrated with practical examples—and gruesome mistakes—from several popular Python libraries.
Natural language processing: feature extraction
This document discusses natural language processing (NLP) and feature extraction. It explains that NLP can be used for applications like search, translation, and question answering. The document then discusses extracting features from text like paragraphs, sentences, words, parts of speech, entities, sentiment, topics, and assertions. Specific features discussed in more detail include frequency, relationships between words, language features, supervised machine learning, classifiers, encoding words, word vectors, and parse trees. Tools mentioned for NLP include Google Cloud NLP, Spacy, OpenNLP, and Stanford Core NLP.
C++ Notes by Hisham Ahmed Rizvi for Class 12th Board Exams
The document provides an overview of key concepts in C++ including:
- Data types like int, char, float, and double
- Variables, constants, and escape sequences
- Operators like assignment, arithmetic, relational, and logical operators
- Control structures like if/else, switch, for, while, and do-while loops
- Functions like main(), exit(), get(), put(), and getline()
- Input/output streams like cin, cout, and file streams
The document covers fundamental C++ concepts to help prepare for board exams.
Similar to ppt (20)
Similarity computation exploiting the semantic and syntactic inherent structu...
Similarity computation exploiting the semantic and syntactic inherent structu...
C++ Notes by Hisham Ahmed Rizvi for Class 12th Board Exams
C++ Notes by Hisham Ahmed Rizvi for Class 12th Board Exams
More from butest
EL MODELO DE NEGOCIO DE YOUTUBE
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
LESSONS FROM THE MICHAEL JACKSON TRIAL
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Timeline: The Life of Michael Jackson
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
LESSONS FROM THE MICHAEL JACKSON TRIAL
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Com 380, Summer II
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
PPT
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
MICHAEL JACKSON.doc
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
NEWS ANNOUNCEMENT
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
C-2100 Ultra Zoom.doc
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
MAC Printing on ITS Printers.doc.doc
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
Mac OS X Guide.doc
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
hier
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
WEB DESIGN!
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
More from butest (20)
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
ppt
- 1. Overview of Machine Learning for NLP Tasks: part I (based partly on slides by Kevin Small and Scott Yih)
- 10. Multi-class Classification [3,4] One Versus All (OvA) Constraint Classification
- 22. A Machine Learning System Testing Examples Feature Vectors Training Examples Preprocessing Feature Extraction Machine Learner Classifier(s) Inference Raw Text Formatted Text Function Parameters Labels Labels
- 24. A Machine Learning System Feature Vectors Preprocessing Feature Extraction Raw Text Formatted Text
- 34. Graphical Representation 0 1 2 3 4 5 6 7 Target -2 -1 1 2 0 -3 -4 3 Window [-2,2] Why WRB won VBD 't NN you PRP accept VBP the DT facts NNS ? .
- 39. (Sparse) Collocation 0 1 2 3 4 5 6 7 Target -2 -1 1 2 0 -3 -4 3 -1 inc: coloc(w,t)[-2,2] w[‘t]-t[PRP], w[you]-t[VBP] w[accept]-t[DT], w[the]-t[NNS] -1 inc: scoloc(w,t)[-2,2] w[‘t]-t[PRP], w[‘t]-t[VBP], w[‘t]-t[DT], w[‘t]-t[NNS], w[you]-t[VBP], w[you]-t[DT], w[you]-t[NNS], w[accept]-t[DT], w[accept]-t[NNS], w[the]-t[NNS] Why WRB won VBD 't NN you PRP accept VBP the DT facts NNS ? .
Editor's Notes
- Pos, spelling
- Task is relevant because words can take multiple POS tags, correct one determined by context.
- again, issue of: multiple representations, ambiguity (JFK) ‘ harder’ task, because phrase level
- (in a principled way, given ML theory, that is...)
- Just to r^d -> r, instance space, output space, single (multi) label
- pictures of a 4 class classifier...with the OvA definition
- Learning a Linear Discriminant Function in Online
- (in a principled way, given ML theory, that is...)
- Well-posed learning problems
- Framing the classification task
- POS tagger requires one sentence per line.
- A machine learning system
- Preprocessing text
- A machine learning system
- Feature Generation (Kernels)
- today, we’re focusing on the second form. 3 rd form is deprecated
- Emphasize that it provides you a means to define the types of features you like Feature engineering is in fact an important part of practice We’ll generate two versions of examples using two different script files
- Emphasize that it provides you a means to define the types of features you like Feature engineering is in fact an important part of practice We’ll generate two versions of examples using two different script files
- CS spelling: do we want features that include the target word itself?
- generates active features -> if the pattern isn’t there, no feature generated.
- disjunction: a shorthand unless combined with e.g. existential; w|t is a bit silly
- Note: don’t have ‘inc’ – don’t want target word to be part of the example (because if other word is subsituted in a mistake in test example, your features won’t fire!)