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Frederick Jelinek : Frederick Jelinek (18 November 1932 – 14 September 2010) was a Czech-American researcher in information theory, automatic speech recognition, and natural language processing. He is well known for his oft-quoted statement, "Every time I fire a linguist, the performance of the speech recognizer goes u... |
Frederick Jelinek : Jelinek was born on November 18, 1932, as Bedřich Jelínek in Kladno to Vilém and Trude Jelínek. His father was Jewish; his mother was born in Switzerland to Czech Catholic parents and had converted to Judaism. Jelínek senior, a dentist, had planned early to escape Nazi occupation and flee to England... |
Frederick Jelinek : Information theory was a fashionable scientific approach in the mid '50s. However, pioneer Claude Shannon wrote in 1956 that this trendiness was dangerous. He said, "Our fellow scientists in many different fields, attracted by the fanfare and by the new avenues opened to scientific analysis, are usi... |
Frederick Jelinek : Institutional page at Johns Hopkins university |
Katz's back-off model : Katz back-off is a generative n-gram language model that estimates the conditional probability of a word given its history in the n-gram. It accomplishes this estimation by backing off through progressively shorter history models under certain conditions. By doing so, the model with the most rel... |
Katz's back-off model : The equation for Katz's back-off model is: P b o ( w i ∣ w i − n + 1 ⋯ w i − 1 ) = &P_(w_\mid w_\cdots w_)\\[4pt]=&d_\cdots w_\cdots w_w_)\cdots w_)&C(w_\cdots w_)>k\\[10pt]\alpha _\cdots w_P_(w_\mid w_\cdots w_)&\end\end where C(x) = number of times x appears in training wi = ith word in the gi... |
Katz's back-off model : This model generally works well in practice, but fails in some circumstances. For example, suppose that the bigram "a b" and the unigram "c" are very common, but the trigram "a b c" is never seen. Since "a b" and "c" are very common, it may be significant (that is, not due to chance) that "a b c... |
Language model : A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induc... |
Language model : Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars, which became fundamental to the field of programming languages. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discre... |
Language model : In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting ... |
Language model : Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typ... |
Language model : == Further reading == |
Latent Dirichlet allocation : In natural language processing, latent Dirichlet allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) for modeling automatically extracted topics in textual corpora. The LDA is an example of a Bayesian topic model. In this, observations (e.g., words) are ... |
Latent Dirichlet allocation : In the context of population genetics, LDA was proposed by J. K. Pritchard, M. Stephens and P. Donnelly in 2000. LDA was applied in machine learning by David Blei, Andrew Ng and Michael I. Jordan in 2003. |
Latent Dirichlet allocation : With plate notation, which is often used to represent probabilistic graphical models (PGMs), the dependencies among the many variables can be captured concisely. The boxes are "plates" representing replicates, which are repeated entities. The outer plate represents documents, while the inn... |
Latent Dirichlet allocation : Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of statistical inference. |
Latent Dirichlet allocation : Variational Bayesian methods Pachinko allocation tf-idf Infer.NET |
Latent Dirichlet allocation : jLDADMM A Java package for topic modeling on normal or short texts. jLDADMM includes implementations of the LDA topic model and the one-topic-per-document Dirichlet Multinomial Mixture model. jLDADMM also provides an implementation for document clustering evaluation to compare topic models... |
Markov information source : In mathematics, a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain. |
Markov information source : An information source is a sequence of random variables ranging over a finite alphabet Γ , having a stationary distribution. A Markov information source is then a (stationary) Markov chain M , together with a function f : S → Γ that maps states S in the Markov chain to letters in the alp... |
Markov information source : Markov sources are commonly used in communication theory, as a model of a transmitter. Markov sources also occur in natural language processing, where they are used to represent hidden meaning in a text. Given the output of a Markov source, whose underlying Markov chain is unknown, the task ... |
Markov information source : Robert B. Ash, Information Theory, (1965) Dover Publications. ISBN 0-486-66521-6 |
Markovian discrimination : Markovian discrimination is a class of spam filtering methods used in CRM114 and other spam filters to filter based on statistical patterns of transition probabilities between words or other lexical tokens in spam messages that would not be captured using simple bag-of-words naive Bayes spam ... |
Markovian discrimination : A bag-of-words model contains only a dictionary of legal words and their relative probabilities in spam and genuine messages. A Markovian model additionally includes the relative transition probabilities between words in spam and in genuine messages, where the relative transition probability ... |
Markovian discrimination : There are two primary classes of Markov models, visible Markov models and hidden Markov models, which differ in whether the Markov chain generating token sequences is assumed to have its states fully determined by each generated token (the visible Markov models) or might also have additional ... |
Markovian discrimination : Maximum-entropy Markov model == References == |
Maximum-entropy Markov model : In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum ... |
Maximum-entropy Markov model : Suppose we have a sequence of observations O 1 , … , O n ,\dots ,O_ that we seek to tag with the labels S 1 , … , S n ,\dots ,S_ that maximize the conditional probability P ( S 1 , … , S n ∣ O 1 , … , O n ) ,\dots ,S_\mid O_,\dots ,O_) . In a MEMM, this probability is factored into Markov... |
Maximum-entropy Markov model : An advantage of MEMMs rather than HMMs for sequence tagging is that they offer increased freedom in choosing features to represent observations. In sequence tagging situations, it is useful to use domain knowledge to design special-purpose features. In the original paper introducing MEMMs... |
Moses (machine translation) : Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. Moses then allows new source-language text to be decoded using these models to produce a... |
Moses (machine translation) : Apertium OpenLogos Comparison of machine translation applications Machine translation |
Moses (machine translation) : Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, Evan Herbst. (2007) "Moses: Open Source Toolkit for Statistical Machine Translation"... |
Moses (machine translation) : Official website IRST LM Toolkit on SourceForge RandLM on SourceForge |
Noisy channel model : The noisy channel model is a framework used in spell checkers, question answering, speech recognition, and machine translation. In this model, the goal is to find the intended word given a word where the letters have been scrambled in some manner. |
Noisy channel model : See Chapter B of. Given an alphabet Σ , let Σ ∗ be the set of all finite strings over Σ . Let the dictionary D of valid words be some subset of Σ ∗ , i.e., D ⊆ Σ ∗ . The noisy channel is the matrix Γ w s = Pr ( s | w ) =\Pr(s|w) , where w ∈ D is the intended word and s ∈ Σ ∗ is the scrambl... |
Noisy channel model : One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode. See chapter 1, and chapter 25... |
Noisy channel model : Speech recognition can be thought of as translating from a sound-language to a text-language. Consequently, we have T ^ = argmax T ∈ Text P ( S ∣ T ) ⏞ speech model P ( T ) ⏞ language model = \overbrace ^\overbrace ^ where P ( S | T ) is the probability that a speech sound S is produced if the ... |
Noisy channel model : Coding theory == References == |
P4-metric : P4 metric (also known as FS or Symmetric F ) enables performance evaluation of the binary classifier. It is calculated from precision, recall, specificity and NPV (negative predictive value). P4 is designed in similar way to F1 metric, however addressing the criticisms leveled against F1. It may be perceive... |
P4-metric : The key concept of P4 is to leverage the four key conditional probabilities: P ( + ∣ C + ) ) - the probability that the sample is positive, provided the classifier result was positive. P ( C + ∣ + ) \mid +) - the probability that the classifier result will be positive, provided the sample is positive. P ( C... |
P4-metric : P4 is defined as a harmonic mean of four key conditional probabilities: P 4 = 4 1 P ( + ∣ C + ) + 1 P ( C + ∣ + ) + 1 P ( C − ∣ − ) + 1 P ( − ∣ C − ) = 4 1 p r e c i s i o n + 1 r e c a l l + 1 s p e c i f i c i t y + 1 N P V _=)+\mid +)+\mid -)+)=+++ In terms of TP,TN,FP,FN it can be calculated as follows... |
P4-metric : Evaluating the performance of binary classifier is a multidisciplinary concept. It spans from the evaluation of medical tests, psychiatric tests to machine learning classifiers from a variety of fields. Thus, many metrics in use exist under several names. Some of them being defined independently. |
P4-metric : Symmetry - contrasting to the F1 metric, P4 is symmetrical. It means - it does not change its value when dataset labeling is changed - positives named negatives and negatives named positives. Range: P 4 ∈ [ 0 , 1 ] _\in [0,1] Achieving P 4 ≈ 1 _\approx 1 requires all the key four conditional probabilities... |
P4-metric : Dependency table for selected metrics ("true" means depends, "false" - does not depend): Metrics that do not depend on a given probability are prone to misrepresentation when it approaches 0. |
P4-metric : F-score Informedness Markedness Matthews correlation coefficient Precision and Recall Sensitivity and Specificity NPV Confusion matrix == References == |
Pachinko allocation : In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocat... |
Pachinko allocation : Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006. The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007. In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM based on a variant of the hierar... |
Pachinko allocation : PAM connects words in V and topics in T with an arbitrary directed acyclic graph (DAG), where topic nodes occupy the interior levels and the leaves are words. The probability of generating a whole corpus is the product of the probabilities for every document: P ( D | α ) = ∏ d P ( d | α ) |\alpha... |
Pachinko allocation : Probabilistic latent semantic indexing (PLSI), an early topic model from Thomas Hofmann in 1999. Latent Dirichlet allocation, a generalization of PLSI developed by David Blei, Andrew Ng, and Michael Jordan in 2002, allowing documents to have a mixture of topics. MALLET, an open-source Java library... |
Pachinko allocation : Mixtures of Hierarchical Topics with Pachinko Allocation, a video recording of David Mimno presenting HPAM in 2007. |
Probabilistic context-free grammar : In theoretical linguistics and computational linguistics, probabilistic context free grammars (PCFGs) extend context-free grammars, similar to how hidden Markov models extend regular grammars. Each production is assigned a probability. The probability of a derivation (parse) is the ... |
Probabilistic context-free grammar : Derivation: The process of recursive generation of strings from a grammar. Parsing: Finding a valid derivation using an automaton. Parse Tree: The alignment of the grammar to a sequence. An example of a parser for PCFG grammars is the pushdown automaton. The algorithm parses grammar... |
Probabilistic context-free grammar : Similar to a CFG, a probabilistic context-free grammar G can be defined by a quintuple: G = ( M , T , R , S , P ) where M is the set of non-terminal symbols T is the set of terminal symbols R is the set of production rules S is the start symbol P is the set of probabilities on prod... |
Probabilistic context-free grammar : PCFGs models extend context-free grammars the same way as hidden Markov models extend regular grammars. The Inside-Outside algorithm is an analogue of the Forward-Backward algorithm. It computes the total probability of all derivations that are consistent with a given sequence, base... |
Probabilistic context-free grammar : Context-free grammars are represented as a set of rules inspired from attempts to model natural languages. The rules are absolute and have a typical syntax representation known as Backus–Naur form. The production rules consist of terminal and non-terminal S symbols and a blank ϵ ... |
Probabilistic context-free grammar : A weighted context-free grammar (WCFG) is a more general category of context-free grammar, where each production has a numeric weight associated with it. The weight of a specific parse tree in a WCFG is the product (or sum ) of all rule weights in the tree. Each rule weight is inclu... |
Probabilistic context-free grammar : Statistical parsing Stochastic grammar L-system |
Probabilistic context-free grammar : Rfam Database Infernal The Stanford Parser: A statistical parser pyStatParser |
Probabilistic latent semantic analysis : Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional rep... |
Probabilistic latent semantic analysis : Considering observations in the form of co-occurrences ( w , d ) of words and documents, PLSA models the probability of each co-occurrence as a mixture of conditionally independent multinomial distributions: P ( w , d ) = ∑ c P ( c ) P ( d | c ) P ( w | c ) = P ( d ) ∑ c P ( c ... |
Probabilistic latent semantic analysis : PLSA may be used in a discriminative setting, via Fisher kernels. PLSA has applications in information retrieval and filtering, natural language processing, machine learning from text, bioinformatics, and related areas. It is reported that the aspect model used in the probabilis... |
Probabilistic latent semantic analysis : Hierarchical extensions: Asymmetric: MASHA ("Multinomial ASymmetric Hierarchical Analysis") Symmetric: HPLSA ("Hierarchical Probabilistic Latent Semantic Analysis") Generative models: The following models have been developed to address an often-criticized shortcoming of PLSA, na... |
Probabilistic latent semantic analysis : This is an example of a latent class model (see references therein), and it is related to non-negative matrix factorization. The present terminology was coined in 1999 by Thomas Hofmann. |
Probabilistic latent semantic analysis : Compound term processing Pachinko allocation Vector space model |
Probabilistic latent semantic analysis : Probabilistic Latent Semantic Analysis Complete PLSA DEMO in C# |
Sinkov statistic : Sinkov statistics, also known as log-weight statistics, is a specialized field of statistics that was developed by Abraham Sinkov, while working for the small Signal Intelligence Service organization, the primary mission of which was to compile codes and ciphers for use by the U.S. Army. The mathemat... |
Statistical machine translation : Statistical machine translation (SMT) is a machine translation approach where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to mac... |
Statistical machine translation : The idea behind statistical machine translation comes from information theory. A document is translated according to the probability distribution p ( e | f ) that a string e in the target language (for example, English) is the translation of a string f in the source language (for ex... |
Statistical machine translation : The most frequently cited benefits of statistical machine translation (SMT) over rule-based approach are: More efficient use of human and data resources There are many parallel corpora in machine-readable format and even more monolingual data. Generally, SMT systems are not tailored to... |
Statistical machine translation : Corpus creation can be costly. Specific errors are hard to predict and fix. Results may have superficial fluency that masks translation problems. Statistical machine translation usually works less well for language pairs with significantly different word order. The benefits obtained fo... |
Statistical machine translation : In word-based translation, the fundamental unit of translation is a word in some natural language. Typically, the number of words in translated sentences are different, because of compound words, morphology and idioms. The ratio of the lengths of sequences of translated words is called... |
Statistical machine translation : In phrase-based translation, the aim is to reduce the restrictions of word-based translation by translating whole sequences of words, where the lengths may differ. The sequences of words are called blocks or phrases. These are typically not linguistic phrases, but phrasemes that were f... |
Statistical machine translation : Syntax-based translation is based on the idea of translating syntactic units, rather than single words or strings of words (as in phrase-based MT), i.e. (partial) parse trees of sentences/utterances. Until the 1990s, with advent of strong stochastic parsers, the statistical counterpart... |
Statistical machine translation : Hierarchical phrase-based translation combines the phrase-based and syntax-based approaches to translation. It uses synchronous context-free grammar rules, but the grammars can be constructed by an extension of methods for phrase-based translation without reference to linguistically mo... |
Statistical machine translation : A language model is an essential component of any statistical machine translation system, which aids in making the translation as fluent as possible. It is a function that takes a translated sentence and returns the probability of it being said by a native speaker. A good language mode... |
Statistical machine translation : Google Translate (started transition to neural machine translation in 2016) Microsoft Translator (started transition to neural machine translation in 2016) Yandex.Translate (switched to hybrid approach incorporating neural machine translation in 2017) |
Statistical machine translation : Problems with statistical machine translation include: |
Statistical machine translation : Annotated list of statistical natural language processing resources — Includes links to freely available statistical machine translation software |
Statistical semantics : In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. |
Statistical semantics : The term statistical semantics was first used by Warren Weaver in his well-known paper on machine translation. He argued that word sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption ... |
Statistical semantics : Research in statistical semantics has resulted in a wide variety of algorithms that use the distributional hypothesis to discover many aspects of semantics, by applying statistical techniques to large corpora: Measuring the similarity in word meanings Measuring the similarity in word relations M... |
Statistical semantics : Statistical semantics focuses on the meanings of common words and the relations between common words, unlike text mining, which tends to focus on whole documents, document collections, or named entities (names of people, places, and organizations). Statistical semantics is a subfield of computat... |
Synchronous context-free grammar : Synchronous context-free grammars (SynCFG or SCFG; not to be confused with stochastic CFGs) are a type of formal grammar designed for use in transfer-based machine translation. Rules in these grammars apply to two languages at the same time, capturing grammatical structures that are e... |
Synchronous context-free grammar : Rules in a SynCFG are superficially similar to CFG rules, except that they specify the structure of two phrases at the same time; one in the source language (the language being translated) and one in the target language. Numeric indices indicate correspondences between non-terminals i... |
Synchronous context-free grammar : cdec, MT decoding package that supports SynCFGs Joshua, a machine translation decoding system written in Java == References == |
Tf–idf : In information retrieval, tf–idf (also TF*IDF, TFIDF, TF–IDF, or Tf–idf), short for term frequency–inverse document frequency, is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. Like the bag-of-words model, it m... |
Tf–idf : Karen Spärck Jones (1972) conceived a statistical interpretation of term-specificity called Inverse Document Frequency (idf), which became a cornerstone of term weighting: The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs.For example, the df (docum... |
Tf–idf : The tf–idf is the product of two statistics, term frequency and inverse document frequency. There are various ways for determining the exact values of both statistics. A formula that aims to define the importance of a keyword or phrase within a document or a web page. |
Tf–idf : Idf was introduced as "term specificity" by Karen Spärck Jones in a 1972 paper. Although it has worked well as a heuristic, its theoretical foundations have been troublesome for at least three decades afterward, with many researchers trying to find information theoretic justifications for it. Spärck Jones's ow... |
Tf–idf : Both term frequency and inverse document frequency can be formulated in terms of information theory; it helps to understand why their product has a meaning in terms of joint informational content of a document. A characteristic assumption about the distribution p ( d , t ) is that: p ( d | t ) = 1 | | | This... |
Tf–idf : Suppose that we have term count tables of a corpus consisting of only two documents, as listed on the right. The calculation of tf–idf for the term "this" is performed as follows: In its raw frequency form, tf is just the frequency of the "this" for each document. In each document, the word "this" appears once... |
Tf–idf : The idea behind tf–idf also applies to entities other than terms. In 1998, the concept of idf was applied to citations. The authors argued that "if a very uncommon citation is shared by two documents, this should be weighted more highly than a citation made by a large number of documents". In addition, tf–idf ... |
Tf–idf : A number of term-weighting schemes have derived from tf–idf. One of them is TF–PDF (term frequency * proportional document frequency). TF–PDF was introduced in 2001 in the context of identifying emerging topics in the media. The PDF component measures the difference of how often a term occurs in different doma... |
Tf–idf : Salton, G; McGill, M. J. (1986). Introduction to modern information retrieval. McGraw-Hill. ISBN 978-0-07-054484-0. Salton, G.; Fox, E. A.; Wu, H. (1983). "Extended Boolean information retrieval". Communications of the ACM. 26 (11): 1022–1036. doi:10.1145/182.358466. hdl:1813/6351. S2CID 207180535. Salton, G.;... |
Tf–idf : Gensim is a Python library for vector space modeling and includes tf–idf weighting. Anatomy of a search engine tf–idf and related definitions as used in Lucene TfidfTransformer in scikit-learn Text to Matrix Generator (TMG) MATLAB toolbox that can be used for various tasks in text mining (TM) specifically i) i... |
Topic model : In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given tha... |
Topic model : An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization ... |
Topic model : Approaches for temporal information include Block and Newman's determination of the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Griffiths & Steyvers used topic modeling on abstracts from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001 whe... |
Topic model : In practice, researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A survey by D. Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms w... |
Topic model : Explicit semantic analysis Latent semantic analysis Latent Dirichlet allocation Hierarchical Dirichlet process Non-negative matrix factorization Statistical classification Unsupervised learning Mallet (software project) Gensim Sentence embedding |
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