Machine Learning Applications in NLP.ppt
•Download as PPT, PDF•
4 likes•4,262 views
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.
Report
Share
Report
Share
![Machine Learning Real World Applications Rob Jasper Intelligent Results http://fac-staff.seattleu.edu/jasperr [email_address]](https://image.slidesharecdn.com/machine-learning-applications-in-nlpppt4816/85/Machine-Learning-Applications-in-NLP-ppt-1-320.jpg)
![Goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Introduction to Machine learning ppt
Introduction to Machine Learning : Machine Learning (ML) is a type of Intelligence (AI) that allows Software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning Algorithms use historical data as input to predict new output values.
Machine learning ppt
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Machine learning session4(linear regression)
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Machine learning
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
natural language processing help at myassignmenthelp.net
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.
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Types of machine learning
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models to make predictions, unsupervised learning finds patterns in unlabeled data through clustering, and reinforcement learning allows agents to learn behaviors through rewards and punishments from its environment.
Deep Learning - Overview of my work II
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
Recommended
Introduction to Machine learning ppt
Introduction to Machine Learning : Machine Learning (ML) is a type of Intelligence (AI) that allows Software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning Algorithms use historical data as input to predict new output values.
Machine learning ppt
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Machine learning session4(linear regression)
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Machine learning
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
natural language processing help at myassignmenthelp.net
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.
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Types of machine learning
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models to make predictions, unsupervised learning finds patterns in unlabeled data through clustering, and reinforcement learning allows agents to learn behaviors through rewards and punishments from its environment.
Deep Learning - Overview of my work II
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
Introduction to Machine Learning
Introduction to Machine Learning provides an introduction to machine learning, and the algorithms and languages that are commonly used.
Supervised and unsupervised learning
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
Natural Language Processing
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
Introduction to Machine Learning
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Introduction to ML (Machine Learning)
This ppt is for beginners in ML to get them familiar with common terms and keywords they will come across while studying this field.
Machine Learning
This is the presentation of Machine learning.Showing the advantage of machine learning and brief introduction of types of machine learning.
Machine Learning and Real-World Applications
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Presentation on unsupervised learning
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
Machine learning
The document discusses machine learning, including its concepts, applications, and different types. It defines machine learning as programming computers to optimize a performance criterion using example data or past experience. It describes supervised learning methods like classification and regression which use historical data to predict future outcomes. Unsupervised learning methods like clustering are used to find patterns in unlabeled data. Reinforcement learning trains agents using rewards and punishments. Examples of machine learning applications discussed include predictive analytics, computer vision, natural language processing and more.
Pattern recognition and Machine Learning.
Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.
Natural language processing (nlp)
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
NLTK - Natural Language Processing in Python
For full details, including the address, and to RSVP see: http://www.meetup.com/bostonpython/calendar/15547287/ NLTK is the Natural Language Toolkit, an extensive Python library for processing natural language. Shankar Ambady will give us a tour of just a few of its extensive capabilities, including sentence parsing, synonym finding, spam detection, and more. Linguistic expertise is not required, though if you know the difference between a hyponym and a hypernym, you might be able to help the rest of us! Socializing at 6:30, Shankar's presentation at 7:00. See you at the NERD.
NLTK
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
NLP PPT.pptx
The document provides an overview of natural language processing (NLP) including definitions, applications, modeling techniques, and tools used. It defines NLP as making computers understand human language and discusses applications like email filters, assistants, translation, and data analysis. Techniques covered include data preprocessing, tokenization, stop words removal, stemming, lemmatization, bag of words, TF-IDF, word embeddings, and sentiment analysis. Python is highlighted as a commonly used programming language and libraries like NLTK are mentioned. Demos are provided of tokenization, stemming, lemmatization, and sentiment analysis.
supervised learning
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Natural language processing
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Artificial Intelligence, Machine Learning and Deep Learning
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Machine learning
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
Natural Language Processing seminar review
This document summarizes a seminar review on natural language processing. It defines NLP as using AI to communicate with intelligent systems in a human language like English. It outlines the steps of defining representations, parsing information, and constructing data structures. It also lists some of the basic components, applications, implementations, algorithms, and companies involved in NLP.
Machine Learning presentation.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
Eric Smidth
This document outlines the course "Data Mining & Probabilistic Reasoning" taught at HPI Potsdam in winter 2013/14. It discusses topics like data mining, probabilistic reasoning, machine learning algorithms, and applications. The course covers basics of probability, evaluation measures, classification, regression, clustering, graphical models and more. Example applications discussed include part-of-speech tagging, email classification, recommendation systems, and community detection in social networks.
MLlecture1.ppt
This document provides an overview of the CS760 Machine Learning course taught by David Page at the University of Wisconsin. The course will cover a broad survey of machine learning algorithms and applications over 30 class meetings. Topics will include both theoretical and practical aspects of supervised learning algorithms like naive Bayes, decision trees, neural networks, and support vector machines. Students will complete programming homework assignments applying various machine learning algorithms and a midterm exam. The primary goals of the course are to understand what learning systems should do and how existing systems work.
More Related Content
What's hot
Introduction to Machine Learning
Introduction to Machine Learning provides an introduction to machine learning, and the algorithms and languages that are commonly used.
Supervised and unsupervised learning
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
Natural Language Processing
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
Introduction to Machine Learning
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Introduction to ML (Machine Learning)
This ppt is for beginners in ML to get them familiar with common terms and keywords they will come across while studying this field.
Machine Learning
This is the presentation of Machine learning.Showing the advantage of machine learning and brief introduction of types of machine learning.
Machine Learning and Real-World Applications
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Presentation on unsupervised learning
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
Machine learning
The document discusses machine learning, including its concepts, applications, and different types. It defines machine learning as programming computers to optimize a performance criterion using example data or past experience. It describes supervised learning methods like classification and regression which use historical data to predict future outcomes. Unsupervised learning methods like clustering are used to find patterns in unlabeled data. Reinforcement learning trains agents using rewards and punishments. Examples of machine learning applications discussed include predictive analytics, computer vision, natural language processing and more.
Pattern recognition and Machine Learning.
Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.
Natural language processing (nlp)
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
NLTK - Natural Language Processing in Python
For full details, including the address, and to RSVP see: http://www.meetup.com/bostonpython/calendar/15547287/ NLTK is the Natural Language Toolkit, an extensive Python library for processing natural language. Shankar Ambady will give us a tour of just a few of its extensive capabilities, including sentence parsing, synonym finding, spam detection, and more. Linguistic expertise is not required, though if you know the difference between a hyponym and a hypernym, you might be able to help the rest of us! Socializing at 6:30, Shankar's presentation at 7:00. See you at the NERD.
NLTK
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
NLP PPT.pptx
The document provides an overview of natural language processing (NLP) including definitions, applications, modeling techniques, and tools used. It defines NLP as making computers understand human language and discusses applications like email filters, assistants, translation, and data analysis. Techniques covered include data preprocessing, tokenization, stop words removal, stemming, lemmatization, bag of words, TF-IDF, word embeddings, and sentiment analysis. Python is highlighted as a commonly used programming language and libraries like NLTK are mentioned. Demos are provided of tokenization, stemming, lemmatization, and sentiment analysis.
supervised learning
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Natural language processing
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Artificial Intelligence, Machine Learning and Deep Learning
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Machine learning
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
Natural Language Processing seminar review
This document summarizes a seminar review on natural language processing. It defines NLP as using AI to communicate with intelligent systems in a human language like English. It outlines the steps of defining representations, parsing information, and constructing data structures. It also lists some of the basic components, applications, implementations, algorithms, and companies involved in NLP.
Machine Learning presentation.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
What's hot (20)
Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence, Machine Learning and Deep Learning
Similar to Machine Learning Applications in NLP.ppt
Eric Smidth
This document outlines the course "Data Mining & Probabilistic Reasoning" taught at HPI Potsdam in winter 2013/14. It discusses topics like data mining, probabilistic reasoning, machine learning algorithms, and applications. The course covers basics of probability, evaluation measures, classification, regression, clustering, graphical models and more. Example applications discussed include part-of-speech tagging, email classification, recommendation systems, and community detection in social networks.
MLlecture1.ppt
This document provides an overview of the CS760 Machine Learning course taught by David Page at the University of Wisconsin. The course will cover a broad survey of machine learning algorithms and applications over 30 class meetings. Topics will include both theoretical and practical aspects of supervised learning algorithms like naive Bayes, decision trees, neural networks, and support vector machines. Students will complete programming homework assignments applying various machine learning algorithms and a midterm exam. The primary goals of the course are to understand what learning systems should do and how existing systems work.
MLlecture1.ppt
This document outlines the syllabus for a machine learning course. It introduces the instructor, teaching assistant, required textbook, and meeting schedule. It describes the course style as primarily algorithmic and experimental, covering many ML subfields. The goals are to understand what a learning system should do and how existing systems work. Background knowledge in languages, AI topics, and math is assumed, but no prior ML experience is needed. Requirements include biweekly programming homework, a midterm exam, and a final project. Grading will be based on homework, exam, project, and discussion participation. Policies on late homework and academic misconduct are also provided.
Max Entropy
This document discusses maximum entropy models and their application to natural language processing tasks. It introduces maximum entropy modeling, describing how the approach works by choosing a probability distribution with maximum entropy subject to constraints from observed data. Training involves using algorithms like generalized iterative scaling to find optimal feature weights that satisfy the constraints. Maximum entropy models have been applied successfully to tasks like part-of-speech tagging, machine translation, and language modeling.
课堂讲义(最后更新:2009-9-25)
This document provides an overview of machine learning concepts including:
- Defining machine learning as the study of how to build systems that improve with experience.
- Designing a learning system for the task of playing checkers by choosing the training experience, representation, and learning algorithm.
- Common machine learning applications like speech recognition, computer vision, and robot control.
Lec1 intoduction.pptx
This document provides an introduction to an artificial intelligence course on machine learning. It discusses different machine learning tasks like classification, regression, transcription, and machine translation. It also covers the concepts of experience (datasets), performance evaluation, supervised vs unsupervised learning, and examples of tasks like face recognition, search queries prediction, and medical imaging analysis that are well-suited for machine learning. Key algorithms discussed include neural networks, decision trees, naive Bayes, and support vector machines.
Machine Learning and Inductive Inference
This document provides an introduction to machine learning and inductive inference. It discusses what machine learning is, common learning tasks like concept learning and function learning, different data representations, and example applications such as knowledge discovery and building adaptive systems. The course will cover generalizing from specific examples to broader concepts through inductive inference and different learning approaches.
Computational Biology, Part 4 Protein Coding Regions
The document discusses different machine learning approaches for supervised classification and sequence analysis. It describes several classification algorithms like k-nearest neighbors, decision trees, linear discriminants, and support vector machines. It also discusses evaluating classifiers using cross-validation and confusion matrices. For sequence analysis, it covers using position-specific scoring matrices, hidden Markov models, cobbling, and family pairwise search to identify new members of protein families. It compares the performance of these different machine learning methods on sequence analysis tasks.
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.
ppt
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.
ppt
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.
ppt
The document discusses machine learning and various related concepts. It provides an overview of machine learning, including well-posed learning problems, designing learning systems, supervised learning, and different machine learning approaches. It also discusses specific machine learning algorithms like naive Bayes classification and decision tree learning.
Machine Learning for NLP
The document discusses machine learning algorithms for natural language processing (NLP). It introduces decision trees, AdaBoost, and support vector machines (SVMs) as three machine learning algorithms that can be applied to NLP classification problems like part-of-speech tagging and word sense disambiguation to resolve linguistic ambiguity. Decision trees are constructed using a top-down induction approach, while AdaBoost and SVMs aim to minimize training error.
Text Mining Analytics 101
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
lecture_mooney.ppt
This document provides an overview of machine learning and various machine learning techniques. It discusses what machine learning is, different types of learning tasks like classification and regression, how performance is measured, and different types of training experiences like direct supervision and reinforcement learning. It then covers specific machine learning algorithms like classification using Rocchio's algorithm, nearest neighbor learning, Bayesian learning approaches, and text categorization using naive Bayes.
Genetic Algorithms.ppt
The document discusses machine learning and genetic algorithms. It provides definitions of machine learning as the study of processes that lead to self-improvement of machine performance through experience. It also discusses different types of learning including supervised learning, unsupervised learning, and reinforcement learning. The document then explains genetic algorithms as evolutionary algorithms that use operations like mutation and crossover to evolve solutions to problems over multiple generations.
mlas06_nigam_tie_01.ppt
The document discusses machine learning techniques for information extraction from text, including sliding window classifiers, boundary detection models, and hidden Markov models. It describes how these techniques identify fields like named entities through features of words and formatting, and understand relationships between fields. These machine learning methods can extract structured data from unstructured text by recognizing fields, associating records, and normalizing values.
Slides
This document summarizes key concepts in information retrieval systems and algorithms for large data sets. It discusses the differences between information retrieval and data retrieval systems. It also describes several classic models for relevance ranking in IR, including the Boolean model and vector space model. The document outlines topics like text processing, indexing, searching, and evaluation in information retrieval systems.
Introduction to Machine Learning.
The document discusses machine learning and provides an overview of the field. It defines machine learning, explains why it is useful, and gives a brief tour of different machine learning techniques including decision trees, neural networks, Bayesian networks, and more. It also discusses some issues in machine learning like how learning problem characteristics and algorithms influence accuracy.
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.
Similar to Machine Learning Applications in NLP.ppt (20)
Computational Biology, Part 4 Protein Coding Regions
Computational Biology, Part 4 Protein Coding Regions
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 ...
Machine Learning Applications in NLP.ppt
- 1. Machine Learning Real World Applications Rob Jasper Intelligent Results http://fac-staff.seattleu.edu/jasperr [email_address]
- 8. Concepts and training examples Instance Space X + + + + c - - - - Training examples
- 10. Error of hypothesis Instance Space X + + + + c - h - - - Where c and h disagree
- 11. An Operational Model of Machine Learning Learner Training Data Model Execution Engine Model Tagged Data Production Data
- 20. Vector Comparison Phish Trey 6 14 d1 d2
- 22. Rocchio Classifier Music m m m m m m M Threshold Characteristic Vector Sports s s s s s s s S Business b b b b B b
- 23. Rocchio Formula
- 24. Rocchio Example Phish Sales + + + + - - - - Centroid+ Centroid- Rocchio
- 25. Support Vector Machines + + + + + + + + - - - - - - - - - -