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Résumé parsing : Resume parsers have achieved up to 87% accuracy, which refers to the accuracy of data entry and categorizing the data correctly. Human accuracy is typically not greater than 96%, so the resume parsers have achieved "near human accuracy." One executive recruiting company tested three resume parsers and ... |
Résumé parsing : A notable resume study was conducted by Marianne Bertrand and Sendhil Mullainathan in 2003. They wanted to observe the effects of White-sounding names versus Black-sounding names on resumes in the hiring process. They sent identical resumes—varying from low- to high-quality—of the same qualifications a... |
Résumé parsing : The parsing software has to rely on complex rules and statistical algorithms to correctly capture the desired information in the resumes. There are many variations of writing style, word choice, syntax, etc. and the same word can have multiple meanings. The date alone can be written hundreds of differe... |
Résumé parsing : Resume parsers have become so omnipresent that it is now recommended that candidates focus on writing to the parsing system rather than to the recruiter. The following techniques have been proposed to increase the probability of success: Use keywords from the job description in relevant places on your ... |
Résumé parsing : With recent advancements in machine learning, the text mining and analysis processes, which ensure up to 95% accuracy in data processing, many AI technologies have sprung up to help the job seekers in the creation of application documents. These services focus on creating ATS-friendly resumes, execute ... |
Résumé parsing : Resume parsers are already standard in most mid- to large-sized companies and this trend will continue as the parsers become even more affordable. A qualified candidate's resume can be ignored if it is not formatted the proper way or doesn't contain specific keywords or phrases. As Machine Learning and... |
Semantic parsing : Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, q... |
Semantic parsing : Early research of semantic parsing included the generation of grammar manually as well as utilizing applied programming logic. In the 2000s, most of the work in this area involved the creation/learning and use of different grammars and lexicons on controlled tasks, particularly general grammars such ... |
Semantic parsing : Datasets used for training statistical semantic parsing models are divided into two main classes based on application: those used for question answering via knowledge base queries, and those used for code generation. |
Semantic parsing : Within the field of natural language processing (NLP), semantic parsing deals with transforming human language into a format that is easier for machines to understand and comprehend. This method is useful in a number of contexts: Voice Assistants and Chatbots: Semantic parsing enhances the quality of... |
Semantic parsing : The performance of Semantic parsers is also measured using standard evaluation metrics as like syntactic parsing. This can be evaluated for the ratio of exact matches (percentage of sentences that were perfectly parsed), and precision, recall, and F1-score calculated based on the correct constituency... |
Semantic parsing : Automatic programming Class (philosophy) Formal semantics (linguistics) Information extraction Information retrieval Minimal recursion semantics Process philosophy Question answering Semantic analysis (linguistics) Semantic role labeling Statistical semantics Syntax Type–token distinction == Referenc... |
Semantic role labeling : In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result. It serves to find the... |
Semantic role labeling : In 1968, the first idea for semantic role labeling was proposed by Charles J. Fillmore. His proposal led to the FrameNet project which produced the first major computational lexicon that systematically described many predicates and their corresponding roles. Daniel Gildea (Currently at Universi... |
Semantic role labeling : Semantic role labeling is mostly used for machines to understand the roles of words within sentences. This benefits applications similar to Natural Language Processing programs that need to understand not just the words of languages, but how they can be used in varying sentences. A better under... |
Semantic role labeling : Named entity recognition Lexical semantics Semantic parsing Syntax tree Annotation |
Semantic role labeling : CoNLL-2005 Shared Task: Semantic Role Labeling Illinois Semantic Role Labeler state of the art semantic role labeling system Demo Preposition SRL: Identifies semantic relations expressed by prepositions Shalmaneser is another state of the art system for assigning semantic predicates and roles. |
Sentence boundary disambiguation : Sentence boundary disambiguation (SBD), also known as sentence breaking, sentence boundary detection, and sentence segmentation, is the problem in natural language processing of deciding where sentences begin and end. Natural language processing tools often require their input to be d... |
Sentence boundary disambiguation : The standard 'vanilla' approach to locate the end of a sentence: (a) If it is a period, it ends a sentence. (b) If the preceding token is in the hand-compiled list of abbreviations, then it does not end a sentence. (c) If the next token is capitalized, then it ends a sentence. This st... |
Sentence boundary disambiguation : Examples of use of Perl compatible regular expressions ("PCRE") ((?<=[a-z0-9][.?!])|(?<=[a-z0-9][.?!]\"))(\s|\r\n)(?=\"?[A-Z]) $sentences = preg_split("/(?<!\..)([\?\!\.]+)\s(?!.\.)/", $text, -1, PREG_SPLIT_DELIM_CAPTURE); (for PHP) Online use, libraries, and APIs sent_detector – Java... |
Sentence boundary disambiguation : Multiword expression Punctuation Sentence extraction Sentence spacing Speech segmentation Syllabification Text segmentation Translation memory Word divider |
Sentence boundary disambiguation : pySBD - python Sentence Boundary Disambiguation |
Sentiment analysis : Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely appl... |
Sentiment analysis : Coronet has the best lines of all day cruisers. Bertram has a deep V hull and runs easily through seas. Pastel-colored 1980s day cruisers from Florida are ugly. I dislike old cabin cruisers. |
Sentiment analysis : I do not dislike cabin cruisers. (Negation handling) Disliking watercraft is not really my thing. (Negation, inverted word order) Sometimes I really hate RIBs. (Adverbial modifies the sentiment) I'd really truly love going out in this weather! (Possibly sarcastic) Chris Craft is better looking than... |
Sentiment analysis : A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classificati... |
Sentiment analysis : Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, an... |
Sentiment analysis : The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on precision and recall over the two target categories of negative and positive texts. However, according to research human raters typically only... |
Sentiment analysis : The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their pr... |
Sentiment analysis : For a recommender system, sentiment analysis has been proven to be a valuable technique. A recommender system aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, collaborative filtering works on the rating matrix, and c... |
Sentiment analysis : Affective computing Consumer sentiment Emotion recognition Friendly artificial intelligence Interpersonal accuracy Multimodal sentiment analysis Stylometry == References == |
Shallow parsing : Shallow parsing (also chunking or light parsing) is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.). While the ... |
Shallow parsing : Apache OpenNLP OpenNLP includes a chunker. GATE General Architecture for Text Engineering GATE includes a chunker. NLTK chunking Illinois Shallow Parser Shallow Parser Demo |
Shallow parsing : Parser Semantic role labeling Named-entity recognition |
Terminology extraction : Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing numb... |
Terminology extraction : The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained. Bilingual terminology can be extracted also from comparable corpora (corpora containing texts within the same text type, doma... |
Terminology extraction : Computational linguistics Glossary Natural language processing Domain ontology Subject indexing Taxonomy (general) Terminology Text mining Text simplification == References == |
Text segmentation : Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing... |
Text segmentation : Automatic segmentation is the problem in natural language processing of implementing a computer process to segment text. When punctuation and similar clues are not consistently available, the segmentation task often requires fairly non-trivial techniques, such as statistical decision-making, large d... |
Text segmentation : Hyphenation Natural language processing Speech segmentation Lexical analysis Word count Line breaking Image segmentation == References == |
Truecasing : Truecasing, also called capitalization recovery, capitalization correction, or case restoration, is the problem in natural language processing (NLP) of determining the proper capitalization of words where such information is unavailable. This commonly comes up due to the standard practice (in English and m... |
Truecasing : Neural networks that operate at the word level or the character level have been trained to recover capitalization with greater than 90% accuracy. Sentence segmentation can be used to determine where sentences begin, to implement the rule that the first word of every sentence must be capitalized. Part-of-sp... |
Truecasing : Truecasing aids in other NLP tasks, such as named entity recognition (NER), automatic content extraction (ACE), and machine translation. Proper capitalization allows easier detection of proper nouns, which are the starting points of NER and ACE. Some translation systems use statistical machine learning tec... |
Truecasing : Sentence case Title case == References == |
Stochastic grammar : A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality: Stochastic context-free grammar Statistical parsing Data-oriented parsing Hidden Markov model (or stochastic regular grammar) Estimation theory The grammar is realized as a language mode... |
Stochastic grammar : A probabilistic method for rhyme detection is implemented by Hirjee & Brown in their study in 2013 to find internal and imperfect rhyme pairs in rap lyrics. The concept is adapted from a sequence alignment technique using BLOSUM (BLOcks SUbstitution Matrix). They were able to detect rhymes undetect... |
Stochastic grammar : Colorless green ideas sleep furiously Computational linguistics L-system#Stochastic grammars Stochastic context-free grammar Statistical language acquisition |
Stochastic grammar : Christopher D. Manning, Hinrich Schütze: Foundations of Statistical Natural Language Processing, MIT Press (1999), ISBN 978-0-262-13360-9. Stefan Wermter, Ellen Riloff, Gabriele Scheler (eds.): Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Springer ... |
Additive smoothing : In statistics, additive smoothing, also called Laplace smoothing or Lidstone smoothing, is a technique used to smooth count data, eliminating issues caused by certain values having 0 occurrences. Given a set of observation counts x = ⟨ x 1 , x 2 , … , x d ⟩ =\langle x_,x_,\ldots ,x_\rangle from a... |
Additive smoothing : Laplace came up with this smoothing technique when he tried to estimate the chance that the sun will rise tomorrow. His rationale was that even given a large sample of days with the rising sun, we still can not be completely sure that the sun will still rise tomorrow (known as the sunrise problem). |
Additive smoothing : A pseudocount is an amount (not generally an integer, despite its name) added to the number of observed cases in order to change the expected probability in a model of those data, when not known to be zero. It is so named because, roughly speaking, a pseudo-count of value α weighs into the posteri... |
Additive smoothing : Bayesian average Prediction by partial matching Categorical distribution |
Additive smoothing : SF Chen, J Goodman (1996). "An empirical study of smoothing techniques for language modeling". Proceedings of the 34th annual meeting on Association for Computational Linguistics. Pseudocounts Bayesian interpretation of pseudocount regularizers A video explaining the use of Additive smoothing in a ... |
Brown clustering : Brown clustering is a hard hierarchical agglomerative clustering problem based on distributional information proposed by Peter Brown, William A. Brown, Vincent Della Pietra, Peter V. de Souza, Jennifer Lai, and Robert Mercer. The method, which is based on bigram language models, is typically applied ... |
Brown clustering : In natural language processing, Brown clustering or IBM clustering is a form of hierarchical clustering of words based on the contexts in which they occur, proposed by Peter Brown, William A. Brown, Vincent Della Pietra, Peter de Souza, Jennifer Lai, and Robert Mercer of IBM in the context of languag... |
Brown clustering : Brown groups items (i.e., types) into classes, using a binary merging criterion based on the log-probability of a text under a class-based language model, i.e. a probability model that takes the clustering into account. Thus, average mutual information (AMI) is the optimization function, and merges a... |
Brown clustering : Brown clustering has also been explored using trigrams. Brown clustering as proposed generates a fixed number of output classes. It is important to choose the correct number of classes, which is task-dependent. The cluster memberships of words resulting from Brown clustering can be used as features i... |
Brown clustering : How to tune Brown clustering |
Collostructional analysis : Collostructional analysis is a family of methods developed by (in alphabetical order) Stefan Th. Gries (University of California, Santa Barbara) and Anatol Stefanowitsch (Free University of Berlin). Collostructional analysis aims at measuring the degree of attraction or repulsion that words ... |
Collostructional analysis : Collostructional analysis so far comprises three different methods: collexeme analysis, to measure the degree of attraction/repulsion of a lemma to a slot in one particular construction; distinctive collexeme analysis, to measure the preference of a lemma to one particular construction over ... |
Collostructional analysis : Collostructional analysis requires frequencies of words and constructions and is similar to a wide variety of collocation statistics. It differs from raw frequency counts by providing not only observed co-occurrence frequencies of words and constructions, but also (i) a comparison of the obs... |
Collostructional analysis : Collostructional analysis differs from most collocation statistics such that (i) it measures not the association of words to words, but of words to syntactic patterns or constructions; thus, it takes syntactic structure more seriously than most collocation-based analyses; (ii) it has so far ... |
Dissociated press : Dissociated press is a parody generator (a computer program that generates nonsensical text). The generated text is based on another text using the Markov chain technique. The name is a play on "Associated Press" and the psychological term dissociation (although word salad is more typical of conditi... |
Dissociated press : The algorithm starts by printing a number of consecutive words (or letters) from the source text. Then it searches the source text for an occurrence of the few last words or letters printed out so far. If multiple occurrences are found, it picks a random one, and proceeds with printing the text foll... |
Dissociated press : Here is a short example of word-based Dissociated Press applied to the Jargon File: wart: n. A small, crocky feature that sticks out of an array (C has no checks for this). This is relatively benign and easy to spot if the phrase is bent so as to be not worth paying attention to the medium in questi... |
Dissociated press : The dissociated press algorithm is described in HAKMEM (1972) Item #176. The name "dissociated press" is first known to have been associated with the Emacs implementation. Brian Hayes discussed a Travesty algorithm in Scientific American in November 1983. The article provided a garbled William Faulk... |
Dissociated press : Cut-up technique Markov chain Mark V. Shaney, a similar program used as a chatbot on Usenet Racter Word salad Parody generator, generic term for a computer program that generates nonsensical text SCIgen, a computer program that generates nonsensical computer science research papers |
Dissociated press : Emacs documentation on Dissociated Press Dissociated Press in the Jargon File Dissociated Press on celebrity Twitter feeds A parody text generator (a Pascal implementaiton) This article is based in part on the Jargon File, which is in the public domain. |
Dynamic topic model : Within statistics, Dynamic topic models' are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that c... |
Dynamic topic model : Similarly to LDA and pLSA, in a dynamic topic model, each document is viewed as a mixture of unobserved topics. Furthermore, each topic defines a multinomial distribution over a set of terms. Thus, for each word of each document, a topic is drawn from the mixture and a term is subsequently drawn f... |
Dynamic topic model : Define α t as the per-document topic distribution at time t. β t , k as the word distribution of topic k at time t. η t , d as the topic distribution for document d in time t, z t , d , n as the topic for the nth word in document d in time t, and w t , d , n as the specific word. In this mode... |
Dynamic topic model : In the dynamic topic model, only W t , d , n is observable. Learning the other parameters constitutes an inference problem. Blei and Lafferty argue that applying Gibbs sampling to do inference in this model is more difficult than in static models, due to the nonconjugacy of the Gaussian and multi... |
Dynamic topic model : In the original paper, a dynamic topic model is applied to the corpus of Science articles published between 1881 and 1999 aiming to show that this method can be used to analyze the trends of word usage inside topics. The authors also show that the model trained with past documents is able to fit d... |
F-score : In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predi... |
F-score : The name F-measure is believed to be named after a different F function in Van Rijsbergen's book, when introduced to the Fourth Message Understanding Conference (MUC-4, 1992). |
F-score : The traditional F-measure or balanced F-score (F1 score) is the harmonic mean of precision and recall: F 1 = 2 r e c a l l − 1 + p r e c i s i o n − 1 = 2 p r e c i s i o n ⋅ r e c a l l p r e c i s i o n + r e c a l l = 2 T P 2 T P + F P + F N = ^+\mathrm ^=2 \cdot \mathrm +\mathrm = +\mathrm +\mathrm ... |
F-score : This is related to the field of binary classification where recall is often termed "sensitivity". |
F-score : Precision-recall curve, and thus the F β score, explicitly depends on the ratio r of positive to negative test cases. This means that comparison of the F-score across different problems with differing class ratios is problematic. One way to address this issue (see e.g., Siblini et al., 2020 ) is to use a st... |
F-score : The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance. It is particularly relevant in applications which are primarily concerned with the positive class and where the positive class is rare relative to the negative c... |
F-score : The F1 score is the Dice coefficient of the set of retrieved items and the set of relevant items. The F1-score of a classifier which always predicts the positive class converges to 1 as the probability of the positive class increases. The F1-score of a classifier which always predicts the positive class is eq... |
F-score : David Hand and others criticize the widespread use of the F1 score since it gives equal importance to precision and recall. In practice, different types of mis-classifications incur different costs. In other words, the relative importance of precision and recall is an aspect of the problem. According to David... |
F-score : While the F-measure is the harmonic mean of recall and precision, the Fowlkes–Mallows index is their geometric mean. |
F-score : The F-score is also used for evaluating classification problems with more than two classes (Multiclass classification). A common method is to average the F-score over each class, aiming at a balanced measurement of performance. |
F-score : BLEU Confusion matrix Hypothesis tests for accuracy METEOR NIST (metric) Receiver operating characteristic ROUGE (metric) Uncertainty coefficient, aka Proficiency Word error rate LEPOR == References == |
Factored language model : The factored language model (FLM) is an extension of a conventional language model introduced by Jeff Bilmes and Katrin Kirchoff in 2003. In an FLM, each word is viewed as a vector of k factors: w i = . =\^,...,f_^\. An FLM provides the probabilistic model P ( f | f 1 , . . . , f N ) ,...,f_)... |
Factored language model : J Bilmes and K Kirchhoff (2003). "Factored Language Models and Generalized Parallel Backoff" (PDF). Human Language Technology Conference. Archived from the original (PDF) on 17 July 2012. |
Glottochronology : Glottochronology (from Attic Greek γλῶττα tongue, language and χρόνος time) is the part of lexicostatistics which involves comparative linguistics and deals with the chronological relationship between languages.: 131 The idea was developed by Morris Swadesh in the 1950s in his article on Salish inter... |
Glottochronology : The original method of glottochronology presumed that the core vocabulary of a language is replaced at a constant (or constant average) rate across all languages and cultures and so can be used to measure the passage of time. The process makes use of a list of lexical terms and morphemes which are si... |
Glottochronology : The concept of language change is old, and its history is reviewed in Hymes (1973) and Wells (1973). In some sense, glottochronology is a reconstruction of history and can often be closely related to archaeology. Many linguistic studies find the success of glottochronology to be found alongside archa... |
Glottochronology : Somewhere in between the original concept of Swadesh and the rejection of glottochronology in its entirety lies the idea that glottochronology as a formal method of linguistic analysis becomes valid with the help of several important modifications. Thus, inhomogeneities in the replacement rate were d... |
Glottochronology : Basic English Cognate Dolgopolsky list Historical linguistics Indo-European studies Leipzig–Jakarta list Lexicostatistics Mass lexical comparison Proto-language Quantitative comparative linguistics Swadesh list |
Glottochronology : Bergsland, Knut; & Vogt, Hans. (1962). On the validity of glottochronology. Current Anthropology, 3, 115–153. Brainerd, Barron (1970). A Stochastic Process related to Language Change. Journal of Applied Probability 7, 69–78. Callaghan, Catherine A. (1991). Utian and the Swadesh list. In J. E. Redden ... |
Glottochronology : Swadesh list in Wiktionary. Discussion with some statistics A simplified explanation of the difference between glottochronology and lexicostatistics. Queryable experiment: quantification of the genetic proximity between 110 languages with trees and discussion |
Interactive machine translation : Interactive machine translation (IMT), is a specific sub-field of computer-aided translation. Under this translation paradigm, the computer software that assists the human translator attempts to predict the text the user is going to input by taking into account all the information it h... |
Interactive machine translation : Historically, interactive machine translation is born as an evolution of the computer-aided translation paradigm, where the human translator and the machine translation system were intended to work as a tandem. This first work was extended within the TransType research project, funded ... |
Interactive machine translation : The interactive machine translation process starts with the system suggesting a translation hypothesis to the user. Then, the user may accept the complete sentence as correct, or may modify it if he considers there is some error. Typically, when modifying a given word, it is assumed th... |
Interactive machine translation : Evaluation is a difficult issue in interactive machine translation. Ideally, evaluation should take place in experiments involving human users. However, given the high monetary cost this would imply, this is seldom the case. Moreover, even when considering human translators in order to... |
Interactive machine translation : Although interactive machine translation is a sub-field of computer-aided translation, the main attractive of the former with respect to the latter is the interactivity. In classical computer-aided translation, the translation system may suggest one translation hypothesis in the best c... |
Interactive machine translation : Machine translation Statistical machine translation Computer-aided translation Computational linguistics Postediting Translation |
Interactive machine translation : Lilt's Interactive Machine Translation demo Interactive Machine Translation demo TransType project web page TransType2 project web page MIPRCV project web page Forecat Forecat-OmegaT |
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