N-gram models are statistical language models that can be used for word prediction. They calculate the probability of a word based on the previous N-1 words. Higher N-gram models require more data but capture context better. N-gram probabilities are estimated by counting word sequences in a large training corpus and calculating conditional probabilities. These probabilities provide information about syntactic patterns and semantic relationships in language.
The document discusses various techniques for smoothing N-gram language models, including Laplace smoothing, Good-Turing discounting, and backoff models. Laplace smoothing involves adding one to all counts to address unseen events. Good-Turing smoothing estimates probabilities for low counts based on the frequency of higher counts. Backoff models first use the highest available order N-gram model and only fallback to a lower order if the current count is zero.
Parts-of-speech can be divided into closed classes and open classes. Closed classes have a fixed set of members like prepositions, while open classes like nouns and verbs are continually changing with new words being created. Parts-of-speech tagging is the process of assigning a part-of-speech tag to each word using statistical models trained on tagged corpora. Hidden Markov Models are commonly used, where the goal is to find the most probable tag sequence given an input word sequence.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
This document provides an overview of key concepts in natural language processing including probability, n-gram models, maximum likelihood estimation, word segmentation, smoothing techniques like Laplace smoothing, discount smoothing, Good-Turing smoothing, backoff, and interpolation. It uses mathematical formulas and examples to explain concepts like calculating conditional probabilities, joint probabilities, and smoothing methods to address data sparsity issues in language models.
The document discusses N-gram language models. It explains that an N-gram model predicts the next likely word based on the previous N-1 words. It provides examples of unigrams, bigrams, and trigrams. The document also describes how N-gram models are used to calculate the probability of a sequence of words by breaking it down using the chain rule of probability and conditional probabilities of word pairs. N-gram probabilities are estimated from a large training corpus.
The document discusses English morphology, including singular and plural forms, morphological parsing and stemming, and the different types of morphology in English such as inflectional, derivational, compounding, and cliticization morphology. It provides examples to illustrate singular and plural forms, morphological rules for changing word endings, and how prefixes, suffixes, and other morphemes can be added to word stems to change word classes or derive new words.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
The document discusses first-order logic (FOL) and its advantages over propositional logic for representing knowledge. It introduces the basic elements of FOL syntax, such as constants, predicates, functions, variables, and connectives. It provides examples of FOL expressions and discusses how objects and relations between objects can be represented. It also covers quantification in FOL using universal and existential quantifiers.
The document discusses various techniques for smoothing N-gram language models, including Laplace smoothing, Good-Turing discounting, and backoff models. Laplace smoothing involves adding one to all counts to address unseen events. Good-Turing smoothing estimates probabilities for low counts based on the frequency of higher counts. Backoff models first use the highest available order N-gram model and only fallback to a lower order if the current count is zero.
Parts-of-speech can be divided into closed classes and open classes. Closed classes have a fixed set of members like prepositions, while open classes like nouns and verbs are continually changing with new words being created. Parts-of-speech tagging is the process of assigning a part-of-speech tag to each word using statistical models trained on tagged corpora. Hidden Markov Models are commonly used, where the goal is to find the most probable tag sequence given an input word sequence.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
This document provides an overview of key concepts in natural language processing including probability, n-gram models, maximum likelihood estimation, word segmentation, smoothing techniques like Laplace smoothing, discount smoothing, Good-Turing smoothing, backoff, and interpolation. It uses mathematical formulas and examples to explain concepts like calculating conditional probabilities, joint probabilities, and smoothing methods to address data sparsity issues in language models.
The document discusses N-gram language models. It explains that an N-gram model predicts the next likely word based on the previous N-1 words. It provides examples of unigrams, bigrams, and trigrams. The document also describes how N-gram models are used to calculate the probability of a sequence of words by breaking it down using the chain rule of probability and conditional probabilities of word pairs. N-gram probabilities are estimated from a large training corpus.
The document discusses English morphology, including singular and plural forms, morphological parsing and stemming, and the different types of morphology in English such as inflectional, derivational, compounding, and cliticization morphology. It provides examples to illustrate singular and plural forms, morphological rules for changing word endings, and how prefixes, suffixes, and other morphemes can be added to word stems to change word classes or derive new words.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
The document discusses first-order logic (FOL) and its advantages over propositional logic for representing knowledge. It introduces the basic elements of FOL syntax, such as constants, predicates, functions, variables, and connectives. It provides examples of FOL expressions and discusses how objects and relations between objects can be represented. It also covers quantification in FOL using universal and existential quantifiers.
The document discusses N-gram language models, which assign probabilities to sequences of words. An N-gram is a sequence of N words, such as a bigram (two words) or trigram (three words). The N-gram model approximates the probability of a word given its history as the probability given the previous N-1 words. This is called the Markov assumption. Maximum likelihood estimation is used to estimate N-gram probabilities from word counts in a corpus.
Machine Learning: Generative and Discriminative Modelsbutest
The document discusses machine learning models, specifically generative and discriminative models. It provides examples of generative models like Naive Bayes classifiers and hidden Markov models. Discriminative models discussed include logistic regression and conditional random fields. The document contrasts how generative models estimate class-conditional probabilities while discriminative models directly estimate posterior probabilities. It also compares how hidden Markov models model sequential data generatively while conditional random fields model sequential data discriminatively.
This document discusses natural language processing and language models. It begins by explaining that natural language processing aims to give computers the ability to process human language in order to perform tasks like dialogue systems, machine translation, and question answering. It then discusses how language models assign probabilities to strings of text to determine if they are valid sentences. Specifically, it covers n-gram models which use the previous n words to predict the next, and how smoothing techniques are used to handle uncommon words. The document provides an overview of key concepts in natural language processing and language modeling.
The document provides an overview of natural language processing (NLP). It defines NLP as the automatic processing of human language and discusses how NLP relates to fields like linguistics, cognitive science, and computer science. The document also describes common NLP tasks like information extraction, machine translation, and summarization. It discusses challenges in NLP like ambiguity and examines techniques used in NLP like rule-based systems, probabilistic models, and the use of linguistic knowledge.
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.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
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.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
Finite state automata (deterministic and nondeterministic finite automata) provide decisions regarding the acceptance and rejection of a string while transducers provide some output for a given input. Thus, the two machines are quite useful in language processing tasks.
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.
This document provides an overview of Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It defines key terminology for LDA including documents, words, topics, and distributions. The document then explains LDA's graphical model and generative process, which represents documents as mixtures over latent topics and generates words probabilistically from topics. Variational inference is introduced as an approach for approximating the intractable posterior distribution over topics and learning model parameters.
The document discusses the analysis of algorithms. It begins by defining an algorithm and describing different types. It then covers analyzing algorithms in terms of correctness, time efficiency, space efficiency, and optimality through theoretical and empirical analysis. The document discusses analyzing time efficiency by determining the number of repetitions of basic operations as a function of input size. It provides examples of input size, basic operations, and formulas for counting operations. It also covers analyzing best, worst, and average cases and establishes asymptotic efficiency classes. The document then analyzes several examples of non-recursive and recursive algorithms.
This document discusses natural language processing (NLP) from a developer's perspective. It provides an overview of common NLP tasks like spam detection, machine translation, question answering, and summarization. It then discusses some of the challenges in NLP like ambiguity and new forms of written language. The document goes on to explain probabilistic models and language models that are used to complete sentences and rearrange phrases based on probabilities. It also covers text processing techniques like tokenization, regular expressions, and more. Finally, it discusses spelling correction techniques using noisy channel models and confusion matrices.
The document discusses different types of residential access networks that connect homes to the internet, including dial-up, DSL, cable modems, and fiber to the home. It provides details on the infrastructure and bandwidth capabilities of each type of access network.
This document discusses using n-grams as features for sentiment classification. It explores using high order n-grams to capture positive and negative expressions that are difficult to model with patterns. It describes combining unigrams and bigrams to improve performance over using bigrams alone. The document also discusses reducing n-gram data to reduce computational complexity and picking top features based on statistical measures. It presents experiments comparing language models, passive-aggressive, and Winnow classifiers on sentiment classification tasks.
The document discusses N-gram language models, which assign probabilities to sequences of words. An N-gram is a sequence of N words, such as a bigram (two words) or trigram (three words). The N-gram model approximates the probability of a word given its history as the probability given the previous N-1 words. This is called the Markov assumption. Maximum likelihood estimation is used to estimate N-gram probabilities from word counts in a corpus.
Machine Learning: Generative and Discriminative Modelsbutest
The document discusses machine learning models, specifically generative and discriminative models. It provides examples of generative models like Naive Bayes classifiers and hidden Markov models. Discriminative models discussed include logistic regression and conditional random fields. The document contrasts how generative models estimate class-conditional probabilities while discriminative models directly estimate posterior probabilities. It also compares how hidden Markov models model sequential data generatively while conditional random fields model sequential data discriminatively.
This document discusses natural language processing and language models. It begins by explaining that natural language processing aims to give computers the ability to process human language in order to perform tasks like dialogue systems, machine translation, and question answering. It then discusses how language models assign probabilities to strings of text to determine if they are valid sentences. Specifically, it covers n-gram models which use the previous n words to predict the next, and how smoothing techniques are used to handle uncommon words. The document provides an overview of key concepts in natural language processing and language modeling.
The document provides an overview of natural language processing (NLP). It defines NLP as the automatic processing of human language and discusses how NLP relates to fields like linguistics, cognitive science, and computer science. The document also describes common NLP tasks like information extraction, machine translation, and summarization. It discusses challenges in NLP like ambiguity and examines techniques used in NLP like rule-based systems, probabilistic models, and the use of linguistic knowledge.
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.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
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.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
Finite state automata (deterministic and nondeterministic finite automata) provide decisions regarding the acceptance and rejection of a string while transducers provide some output for a given input. Thus, the two machines are quite useful in language processing tasks.
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.
This document provides an overview of Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It defines key terminology for LDA including documents, words, topics, and distributions. The document then explains LDA's graphical model and generative process, which represents documents as mixtures over latent topics and generates words probabilistically from topics. Variational inference is introduced as an approach for approximating the intractable posterior distribution over topics and learning model parameters.
The document discusses the analysis of algorithms. It begins by defining an algorithm and describing different types. It then covers analyzing algorithms in terms of correctness, time efficiency, space efficiency, and optimality through theoretical and empirical analysis. The document discusses analyzing time efficiency by determining the number of repetitions of basic operations as a function of input size. It provides examples of input size, basic operations, and formulas for counting operations. It also covers analyzing best, worst, and average cases and establishes asymptotic efficiency classes. The document then analyzes several examples of non-recursive and recursive algorithms.
This document discusses natural language processing (NLP) from a developer's perspective. It provides an overview of common NLP tasks like spam detection, machine translation, question answering, and summarization. It then discusses some of the challenges in NLP like ambiguity and new forms of written language. The document goes on to explain probabilistic models and language models that are used to complete sentences and rearrange phrases based on probabilities. It also covers text processing techniques like tokenization, regular expressions, and more. Finally, it discusses spelling correction techniques using noisy channel models and confusion matrices.
The document discusses different types of residential access networks that connect homes to the internet, including dial-up, DSL, cable modems, and fiber to the home. It provides details on the infrastructure and bandwidth capabilities of each type of access network.
This document discusses using n-grams as features for sentiment classification. It explores using high order n-grams to capture positive and negative expressions that are difficult to model with patterns. It describes combining unigrams and bigrams to improve performance over using bigrams alone. The document also discusses reducing n-gram data to reduce computational complexity and picking top features based on statistical measures. It presents experiments comparing language models, passive-aggressive, and Winnow classifiers on sentiment classification tasks.
Ricerca sul sentiment lasciato nel Web dai privati che hanno scritto riguardo ad EXPO 2015 in italiano, inglese, francese, tedesco e spagnolo, prima dell'inaugurazione, durante l’Esposizione Universale e dopo la sua chiusura.
This document discusses natural language processing (NLP) from a developer's perspective. It provides an overview of common NLP tasks like spam detection, machine translation, question answering, and summarization. It then discusses challenges in NLP like ambiguity and new forms of written language. The document goes on to explain how probabilistic models are used in NLP to infer language properties and complete tasks like sentence completion and phrase rearrangement using concepts like language models. It also covers text processing techniques like tokenization and regular expressions. Finally, it discusses spelling correction in detail using techniques like noisy channel modeling and confusion matrices.
This document is the slides for a lecture on part-of-speech tagging, keyword and phrase extraction, and text similarity for natural language processing. It introduces part-of-speech tagging and different taggers such as rule-based and ngram-based approaches. It also discusses methods for keyword and phrase extraction including supervised classifiers and unsupervised techniques like TF-IDF. Finally, it covers measuring text similarity using vector space models and cosine similarity.
Il testo è un estratto di una relazione più vasta. Questa parte ha come oggetto l’applicazione della Social Network Analysis quale strumento di marketing, per uno sviluppo di una metodologia e di un database in grado di rilevare i soggetti più importanti per il business all’interno di social network quali facebook, twitter o myspace. Tale rilevazione può permettere di sfruttare il fenomeno del WOM applicandolo alla rete di conoscenze che un individuo possiede all’interno dei SN stessi.
Current state of the art pos tagging for indian languages – a studyiaemedu
The document summarizes research on part-of-speech tagging for several Indian languages. It discusses rule-based, stochastic, and neural network approaches to POS tagging and surveys work done on Hindi, Bengali, Tamil, Telugu, Gujarati, Malayalam, Manipuri, and Assamese. The accuracy of POS taggers for Indian languages ranges from 71-94% depending on the language and approach used, compared to 93-98% for Western languages, due to limited annotated data and morphological complexity in Indian languages.
Natural Language processing Parts of speech tagging, its classes, and how to ...Rajnish Raj
Part of speech (POS) tagging is the process of assigning a part of speech tag like noun, verb, adjective to each word in a sentence. It involves determining the most likely tag sequence given the probabilities of tags occurring before or after other tags, and words occurring with certain tags. POS tagging is the first step in many NLP applications and helps determine the grammatical role of words. It involves calculating bigram and lexical probabilities from annotated corpora to find the tag sequence with the highest joint probability.
This document provides an overview of machine learning techniques for text mining and information extraction, including supervised, unsupervised, and weakly supervised learning algorithms. It discusses support vector machines, naive Bayes models, maximum entropy models, and feature selection methods. Key machine learning approaches covered are support vector machines, naive Bayes classifiers, maximum entropy models, and the use of kernels and feature extraction for text classification tasks.
This is an introduction to an algorithm and methodology to extract semantics from one or several documents using Natural Language Processing and Machine learning techniques. The presentation describes the different components of the semantic analyzer using Wikipedia and Dbpedia as data sets.
Studio sul posizionamento digitale di quattro brand del Gruppo Max Mara (Max Mara, Max&co., Marella e Pennyblack) e due competitor (Liu Jo e Pinko).
L'analisi si basa sulle conversazioni sui social network rilevate nel periodo 1-14 Ottobre 2014.
This document provides an overview of natural language processing (NLP) including the linguistic basis of NLP, common NLP problems and approaches, sources of NLP data, and steps to develop an NLP system. It discusses tokenization, part-of-speech tagging, parsing, machine learning approaches like naive Bayes classification and dependency parsing, measuring word similarity, and distributional semantics. The document also provides advice on going from research to production systems and notes areas not covered like machine translation and deep learning methods.
Nuovi scenari sociali, reti digitali e accumulo di big data. Un approccio sis...Valerio Eletti
Viviamo in ambienti sempre più complessi. Uno sguardo all'attuale intreccio di fenomeni globali allarmanti. Focus su uno di questi: lo sviluppo delle reti sociali digitali e il conseguente accumulo di big data. Come affrontare questo scenario? Nuove protesi cognitive e un nuovo paradigma cognitivo complesso.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
Representation Learning of Vectors of Words and PhrasesFelipe Moraes
Talk about representation learning using word vectors such as Word2Vec, Paragraph Vector. Also introduced to neural network language models. Expose some applications using NNLM such as sentiment analysis and information retrieval.
Jarrar: Probabilistic Language Modeling - Introduction to N-gramsMustafa Jarrar
Lecture video by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
Discusses the concept of Language Models in Natural Language Processing. The n-gram models, markov chains are discussed. Smoothing techniques such as add-1 smoothing, interpolation and discounting methods are addressed.
Simulation de phénomènes naturels : Explorer comment la modélisation et la simulation sont utilisées pour simuler des phénomènes naturels tels que les catastrophes naturelles (ouragans, tremblements de terre, inondations), les phénomènes météorologiques, etc., afin de mieux comprendre leur comportement et d'améliorer les mesures de prévention et de préparation.
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
This document provides an introduction to natural language processing (NLP). It discusses key topics in NLP including languages and intelligence, the goals of NLP, applications of NLP, and general themes in NLP like ambiguity in language and statistical vs rule-based methods. The document also previews specific NLP techniques that will be covered like part-of-speech tagging, parsing, grammar induction, and finite state analysis. Empirical approaches to NLP are discussed including analyzing word frequencies in corpora and addressing data sparseness issues.
Sequencing run grief counseling: counting kmers at MG-RASTwltrimbl
Talk by Will Trimble of Argonne National Laboratory on April 29, 2014, at UIC's department of Ecology & Evolution on visualizing and interpreting the redundancy spectrum of long kmers in high-throughput sequence data.
This document discusses building a next word prediction model using deep learning methods like LSTMs. It will take text as input, preprocess and tokenize the data, then build a deep learning model to predict the next word based on the previous words. Simple word prediction models use n-grams to calculate conditional word probabilities based on occurrence counts from text corpora. Bigram and trigram models are discussed as ways to predict the next word based on the previous one or two words in a sequence.
This document provides an overview of knowledge representation in artificial intelligence. It discusses how knowledge representation is used to formalize problems so they can be solved through search techniques. Good knowledge representations reduce problems to search problems, while bad representations prevent problem solving. Logical expressions are presented as one way to represent states, actions, and allow an agent to deduce hidden properties and appropriate actions. The document then discusses the Wumpus World environment as an example problem domain. It describes the percepts, properties, and actions in the Wumpus World. Finally, it discusses using first-order logic to represent facts and inferences about the Wumpus World.
This document provides a brief introduction to logic by outlining its historical development and key concepts in propositional logic. It discusses how logic evolved from philosophical logic using natural languages to symbolic and mathematical logic using formal languages. It then defines propositional logic syntax using formulas and semantics using truth assignments. It explains how to determine if a truth assignment satisfies a formula and different perspectives on semantics. Finally, it introduces proofs in propositional logic using natural deduction rules.
This document provides an overview of logical agents and knowledge representation. It discusses knowledge-based agents and the Wumpus world example. It introduces propositional logic and various inference techniques like forward chaining, backward chaining, and resolution that can be used for automated reasoning. It also discusses concepts like entailment, models, validity, and satisfiability. Finally, it discusses how these logical concepts can be applied to build an agent that reasons about the Wumpus world using propositional logic.
This document discusses propositional logic and covers topics like propositions, common logical operators like negation and conjunction, proving the equivalence of logical formulas, constructing logical formulas based on truth tables, and simplifying logical formulas using laws like De Morgan's laws and distribution laws. Examples are provided for each topic to illustrate key concepts in propositional logic.
Prolog is a logic programming language well-suited for symbolic problems involving objects and relations. The document introduces Prolog concepts like defining relations using facts and rules, recursive rules, how Prolog answers questions, and the declarative and procedural meanings of programs. Key points covered include using constants and variables, different clause types, matching terms, declarative versus procedural execution, and representing lists.
In this chapter, the Logic, contrapositive, converse, inference, discrete mathematics, discrete structure, argument, conjunction, disjunction, negation are well explained.
Prosody is an essential component of human speech. Prosody, broadly, describes all of the production qualities of speech that are not involved in conveying lexical information. Where the words are “what is said”, prosody is “how it is said”. Prosody of speech, plays an important role not only in communicating the syntax, semantics and pragmatics of spoken language, but also in conveying information about the speaker and their internal state (e.g. emotion or fatigue).
Understanding prosody is critical to understanding speech communication. Spoken language processing (SLP) technology that approaches human levels of competence will necessarily include automatic analysis of prosody. Despite the importance of prosody in spoken communication, researchers are often unable to reliably incorporate prosodic information into applications. One explanation is a lack of compact, consistent, and universal representations of prosodic information. This talk will describe the state of the art in prosodic analysis and its use in spoken language processing with a focus on the development of new representations of prosody.
This document provides an introduction to probability and statistics concepts over 2 weeks. It covers basic probability topics like sample spaces, events, probability definitions and axioms. Conditional probability and the multiplication rule for conditional probability are explained. Bayes' theorem relating prior, likelihood and posterior probabilities is introduced. Examples on probability calculations for coin tosses, dice rolls and medical testing are provided. Key terms around experimental units, populations, descriptive and inferential statistics are also defined.
The document discusses genome rearrangements and greedy algorithms for sorting gene orders. It begins with an example of comparing gene orders between cabbage and turnip mitochondria, finding 99% gene similarity but different orders. This motivated studying genome rearrangements in evolution. Several rearrangement operations are defined, including reversals and translocations. Greedy algorithms are proposed for sorting gene orders using reversals by repeatedly maximizing the sorted prefix. Applications to cancer rearrangements and comparing mouse and human genomes are mentioned. [/SUMMARY]
This document provides an introduction and overview of natural language processing (NLP). It discusses how NLP aims to allow computers to communicate with humans using everyday language. It also discusses related areas like artificial intelligence, linguistics, and cognitive science. The document outlines some key aspects of communication like intention, generation, perception, analysis, and incorporation. It discusses the roles of syntax, semantics, and pragmatics. It also covers challenges in NLP like ambiguity and how ambiguity is pervasive and can lead to many possible interpretations. The document contrasts natural languages with computer languages and provides examples of common NLP tasks.
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Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
This document discusses n-gram models for natural language processing tasks. It begins by introducing n-gram models and how they are used to estimate conditional probabilities of words. Specifically, it explains how n-gram models make the assumption that the probability of a word depends only on the previous n words. The document then provides examples of unigram, bigram, and trigram probability computations. It also discusses techniques like smoothing and backoff that are used to address data sparsity issues with n-gram models. Finally, it briefly mentions some shortcomings of n-gram models.
Presentazione di Pierpaolo Basile, durante il suo talk dal titolo "Geometria e Semantica del Linguaggio.
L'incontro si è tenuto il giorno 17 Dicembre 2014 all'interno del progetto SSC (Scientific Storming Café).
L'abstract del talk è "Rappresentare concetti in uno spazio geometrico è una tecnica ampiamente utilizzata nell'informatica per modellare la semantica del linguaggio naturale. Ad esempio i motori di ricerca che interroghiamo ogni giorno utilizzano la geometria per rappresentare parole e documenti. Obiettivo del talk è introdurre i concetti di base dei modelli di semantica distribuzionale e presentare alcuni operatori geometrici per la composizione dei termini per rappresentare concetti più complessi come frasi o interi documenti"
no loss, elastic, no
Telnet: no loss, elastic, yes (interactive)
FTP: no loss, elastic, no
SMTP: no loss, elastic, no
HTTP: no loss, elastic, no
Streaming audio/video: loss-tolerant, 5kbps-1Mbps, yes (100’s msec)
Interactive games: loss-tolerant, elastic, yes (100’s msec)
So in summary, different apps have different requirements for data loss, throughput, and time sensitivity.
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Conditional independence assumptions allow simpler probabilistic models to be constructed that can still accurately model real-world phenomena. Bayesian networks provide a systematic way to construct probabilistic models using conditional independence assumptions. Representing knowledge with probabilities provides a coherent framework for representing uncertainty, which is important for building intelligent systems that can reason effectively with incomplete information.
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The document discusses query compilation and optimization. It covers parsing a SQL query into a parse tree, converting the parse tree into a logical query plan using relational algebra, and estimating the costs of different logical and physical query plans to select the most efficient plan. The key steps are parsing, rewriting the logical query plan using algebraic laws to improve it, estimating sizes of intermediate results, and selecting a physical query plan based on cost estimates.
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1. Supervised learning aims to learn a function that maps inputs to target variables by minimizing error on training data. Decision tree learning is an example approach.
2. Decision trees partition data into purer subsets using information gain, which measures the reduction in entropy when an attribute is used.
3. The greedy decision tree algorithm recursively selects the attribute with highest information gain to split on, growing subtrees until leaves contain only one class.
1) Markov models and hidden Markov models describe systems that transition between states based on probabilities, where the next state depends only on the current state.
2) Markov models assume each state corresponds to a directly observable event, while hidden Markov models allow states to be hidden and observations to depend probabilistically on the current state.
3) Transition and initial state probabilities can be described using a transition matrix in Markov models to calculate the probability of state sequences.
Here is a proof of this statement using resolution refutation:
1. ∀x∀y(F(x) ∧ F(y) ∧ L(x,y)) → S(x,y) (Premise: Any fish larger can swim faster)
2. ∃x∀y(F(y) → L(x,y)) (Premise: There exists a largest fish)
3. ∃x∀y(F(y) → S(x,y)) (Goal: There exists a fastest fish)
4. F(a) ∧ F(b) ∧ L(a,b) → S(a
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2. Last lecture
• Uncertainty
– How to represent uncertainty
• Probability and its axioms
– As degree of agent’s belief
• Joint distribution
– P(A,B,C,D … Z)
• Bayes rule
– P(X,Y) = P(X|Y) P(Y) = P(Y/X) P(X)
• Independence
– 2 random variables A and B are independent iff
P(a, b) = P(a) P(b) for all values a, b
• Conditional independence
– 2 random variables A and B are conditionally independent given C iff
P(a, b | c) = P(a | c) P(b | c) for all values a, b, c
3. Today’s Lecture
• Use probability axioms to model word prediction
• N-grams
• Smoothing
• Reading:
– Sections 4.1, 4.2, 4.3, 4.5.1 in Speech and Language
Processing [Jurafsky & Martin]
4. Next Word Prediction
• From a NY Times story...
– Stocks ...
– Stocks plunged this ….
– Stocks plunged this morning, despite a cut in interest
rates
– Stocks plunged this morning, despite a cut in interest
rates by the Federal Reserve, as Wall ...
– Stocks plunged this morning, despite a cut in interest
rates by the Federal Reserve, as Wall Street began
5. – Stocks plunged this morning, despite a cut in
interest rates by the Federal Reserve, as Wall
Street began trading for the first time since last …
– Stocks plunged this morning, despite a cut in
interest rates by the Federal Reserve, as Wall
Street began trading for the first time since last
Tuesday's terrorist attacks.
6. Human Word Prediction
• Clearly, at least some of us have the ability to
predict future words in an utterance.
• How?
– Domain knowledge: red blood vs. red hat
– Syntactic knowledge: the…<adj|noun>
– Lexical knowledge: baked <potato vs. chicken>
7. Claim
• A useful part of the knowledge needed to allow
Word Prediction can be captured using simple
statistical techniques
• In particular, we'll be interested in the notion of the
probability of a sequence (of letters, words,…)
8. Useful Applications
• Why do we want to predict a word, given some
preceding words?
– Rank the likelihood of sequences containing various
alternative hypotheses, e.g. for ASR
Theatre owners say popcorn/unicorn sales have doubled...
– Assess the likelihood/goodness of a sentence, e.g. for
text generation or machine translation
9. Applications of word prediction
• Spelling checkers
– They are leaving in about fifteen minuets to go to her house
– The study was conducted mainly be John Black.
– I need to notified the bank of this problem.
• Mobile phone texting
– He is trying to fine out.
– Hopefully, all with continue smoothly in my absence.
• Speech recognition
– Theatre owners say popcorn/unicorn sales have doubled...
• Handwriting recognition
• Disabled users
• Machine Translation
September 2003 9
10. N-Gram Models of Language
• Use the previous N-1 words in a sequence to
predict the next word
• Language Model (LM)
– unigrams, bigrams, trigrams,…
• How do we train these models?
– Very large corpora
11. Corpora
• Corpora are online collections of text and speech
– Brown Corpus
– Wall Street Journal
– AP newswire
– Hansards
– Timit
– DARPA/NIST text/speech corpora (Call Home, Call
Friend, ATIS, Switchboard, Broadcast News, Broadcast
Conversation, TDT, Communicator)
– TRAINS, Boston Radio News Corpus
12. Terminology
• Sentence: unit of written language
• Utterance: unit of spoken language
• Word Form: the inflected form as it actually
appears in the corpus
• Lemma: an abstract form, shared by word forms
having the same stem, part of speech, word
sense – stands for the class of words with same
stem
– chairs [chair] , reflected [reflect], ate [eat],
• Types: number of distinct words in a corpus
(vocabulary size)
• Tokens: total number of words
13. Simple N-Grams
• Assume a language has T word types in its lexicon,
how likely is word x to follow word y?
– Simplest model of word probability: 1/T
– Alternative 1: estimate likelihood of x occurring in new text
based on its general frequency of occurrence estimated
from a corpus (unigram probability)
popcorn is more likely to occur than unicorn
– Alternative 2: condition the likelihood of x occurring in the
context of previous words (bigrams, trigrams,…)
mythical unicorn is more likely than mythical popcorn
14. Computing the Probability of a
Word Sequence
• By the Chain Rule we can decompose a joint probability, e.g.
P(w1,w2,w3) as follows
P(w1,w2, ...,wn) = P(wn|wn-1,wn-2,...,w1) P(wn-1|wn-2, ...,w1) … P(w2|w1) P(w1)
P(wn-1, ...,w1)
n
P(wn) P(wk | w1 1)
k
1 k 1
• Compute the product of component conditional probabilities?
– P(the mythical unicorn) = P(the) * P(mythical|the) * P(unicorn|the
mythical)
• But…the longer the sequence, the less likely we are to find it
in a training corpus
P(Most biologists and folklore specialists believe that in fact the mythical
unicorn horns derived from the narwhal)
• What can we do?
15. Bigram Model
• Approximate by
– E.g., P(unicorn|the mythical) by P(unicorn|mythical)
P(wn |w1n 1) P(wn | wn 1)
• Markov assumption: the probability of a word
depends only on the probability of a limited history
• Generalization: the probability of a word
depends only on the probability of the n
previous words
– trigrams, 4-grams, 5-grams,…
– the higher n is, the more data needed to train
– backoff models…
16. Using N-Grams
• For N-gram models
– P(wn | w1n 1) P(wn |wn 1 1)
n
N
• E.g. for bigrams, (w8| w8 1)
P P(w8| w88 2 1)
1
1
– By the Chain Rule we can decompose a joint
probability, e.g. P(w1,w2,w3) as follows
P(w1,w2, ...,wn) = P(wn|wn-1,wn-2,...,w1) P(wn-1|wn-2, ...,w1) …
P(w2|w1) P(w1)
n
P(wn) P(wk | wk 1 )
1 k 1
k N 1
17. • For bigrams then, the probability of a sequence is
just the product of the conditional probabilities of its
bigrams, e.g.
P(the,mythical,unicorn) = P(unicorn|mythical) P(mythical|the)
P(the|<start>)
18. Training and Testing
• N-Gram probabilities come from a training corpus
– overly narrow corpus: probabilities don't generalize
– overly general corpus: probabilities don't reflect task or
domain
• A separate test corpus is used to evaluate the
model
– held out test set; development (dev) test set
– Simple baseline
– results tested for statistical significance – how do they
differ from a baseline? Other results?
19. A Simple Bigram Example
• Estimate the likelihood of the sentence I want to eat
Chinese food.
– P(I want to eat Chinese food) = P(I | <start>) P(want | I)
P(to | want) P(eat | to) P(Chinese | eat) P(food | Chinese)
P(<end>|food)
• What do we need to calculate these likelihoods?
– Bigram probabilities for each word pair sequence in the
sentence
– Calculated from a large corpus
20. Early Bigram Probabilities
from BERP
Eat on .16 Eat Thai .03
Eat some .06 Eat breakfast .03
Eat lunch .06 Eat in .02
Eat dinner .05 Eat Chinese .02
Eat at .04 Eat Mexican .02
Eat a .04 Eat tomorrow .01
Eat Indian .04 Eat dessert .007
Eat today .03 Eat British .001
21. <start> I .25 Want some .04
<start> I’d .06 Want Thai .01
<start> Tell .04 To eat .26
<start> I’m .02 To have .14
I want .32 To spend .09
I would .29 To be .02
I don’t .08 British food .60
I have .04 British restaurant .15
Want to .65 British cuisine .01
Want a .05 British lunch .01
22. • P(I want to eat British food) = P(I|<start>)
P(want|I) P(to|want) P(eat|to) P(British|eat)
P(food|British) = .25*.32*.65*.26*.001*.60 =
.000080
– Suppose P(<end>|food) = .2?
– How would we calculate I want to eat Chinese
food ?
• Probabilities roughly capture ``syntactic'' facts
and ``world knowledge''
– eat is often followed by an NP
– British food is not too popular
• N-gram models can be trained by counting
and normalization
23. Early BERP Bigram Counts
I Want To Eat Chinese Food lunch
I 8 1087 0 13 0 0 0
Want 3 0 786 0 6 8 6
To 3 0 10 860 3 0 12
Eat 0 0 2 0 19 2 52
Chinese 2 0 0 0 0 120 1
Food 19 0 17 0 0 0 0
Lunch 4 0 0 0 0 1 0
24. Early BERP Bigram
Probabilities
• Normalization: divide each row's counts by
appropriate unigram counts for wn-1
I Want To Eat Chinese Food Lunch
3437 1215 3256 938 213 1506 459
• Computing the bigram probability of I I
– C(I,I)/C( I in call contexts )
– p (I|I) = 8 / 3437 = .0023
• Maximum Likelihood Estimation (MLE):
relative frequency freqwww2)
freq( 1,
( 1)
25. What do we learn about the
language?
• What's being captured with ...
– P(want | I) = .32
– P(to | want) = .65
– P(eat | to) = .26
– P(food | Chinese) = .56
– P(lunch | eat) = .055
• What about...
– P(I | I) = .0023
– P(I | want) = .0025
– P(I | food) = .013
26. – P(I | I) = .0023 I I I I want
– P(I | want) = .0025 I want I want
– P(I | food) = .013 the kind of food I want is ...
27. Approximating Shakespeare
• Generating sentences with random
unigrams...
– Every enter now severally so, let
– Hill he late speaks; or! a more to leg less first you
enter
• With bigrams...
– What means, sir. I confess she? then all sorts, he
is trim, captain.
– Why dost stand forth thy canopy, forsooth; he is
this palpable hit the King Henry.
• Trigrams
– Sweet prince, Falstaff shall die.
– This shall forbid it should be branded, if renown
made it empty.
28. • Quadrigrams
– What! I will go seek the traitor Gloucester.
– Will you not tell me who I am?
– What's coming out here looks like Shakespeare
because it is Shakespeare
• Note: As we increase the value of N, the
accuracy of an n-gram model increases,
since choice of next word becomes
increasingly constrained
29. N-Gram Training Sensitivity
• If we repeated the Shakespeare experiment but
trained our n-grams on a Wall Street Journal
corpus, what would we get?
• Note: This question has major implications for
corpus selection or design
30. WSJ is not Shakespeare: Sentences Generated
from WSJ
31. Some Useful Observations
• There are 884,647 tokens, with 29,066 word form
types, in an approximately one million word
Shakespeare corpus
– Shakespeare produced 300,000 bigram types out of 844
million possible bigrams: so, 99.96% of the possible
bigrams were never seen (have zero entries in the table)
• A small number of events occur with high frequency
• A large number of events occur with low frequency
• You can quickly collect statistics on the high
frequency events
• You might have to wait an arbitrarily long time to get
valid statistics on low frequency events
• Some zeroes in the table are really zeros But others
are simply low frequency events you haven't seen
yet. How to address?
32. Smoothing
• Words follow a Zipfian distribution
– Small number of words occur very frequently
– A large number are seen only once
– Zipf’s law: a word’s frequency is approximately inversely
proportional to its rank in the word distribution list
• Zero probabilities on one bigram cause a zero
probability on the entire sentence
• So….how do we estimate the likelihood of unseen
n-grams?
32
34. Laplace Smoothing
• For unigrams:
–Add 1 to every word (type) count to get an
adjusted count c*
–Normalize by N (#tokens) + V (#types)
–Original unigram probability
P(w ) c i
i
N
–New unigram probability
P (w )
LP i
c 1
i
N V
35. Unigram Smoothing
Example
P (w ) c 1
• Tiny Corpus, V=4; N=20
i
LP i
N V
Word True Ct Unigram New Ct Adjusted
Prob Prob
eat 10 .5 11 .46
British 4 .2 5 .21
food 6 .3 7 .29
happily 0 .0 1 .04
20 1.0 ~20 1.0
36. • So, we lower some (larger) observed
counts in order to include
unobserved vocabulary
• For bigrams:
–Original P(w | w ) c(w | w )
n n 1
n n 1
c(w ) n 1
–New P(w | w ) c(w | w ) 1
n n 1
n n 1
c(w ) V n 1
–But this change counts drastically:
• Too much weight given to unseen ngrams
• In practice, unsmoothed bigrams often work
better!
37. Backoff Methods (e.g. Katz ’87)
• For e.g. a trigram model
– Compute unigram, bigram and trigram probabilities
– Usage:
• Where trigram unavailable back off to bigram if available, o.w. back
off to the current word’s unigram probability
• E.g An omnivorous unicorn
• NB: check errata pages for changes to figures
38. Smoothing Summed Up
• Add-one smoothing (easy, but inaccurate)
– Add 1 to every word count (Note: this is type)
– Increment normalization factor by Vocabulary size: N (tokens)
+ V (types)
• Backoff models
– When a count for an n-gram is 0, back off to the count for the
(n-1)-gram
– These can be weighted – trigrams count more
39. Summary
• N-gram probabilities can be used to estimate the
likelihood
– Of a word occurring in a context (N-1)
– Of a sentence occurring at all
• Smoothing techniques and backoff models deal with
problems of unseen words in corpus