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Towards a part-of-speech tagger for Sranan Tongo ...
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Nicolás, C.V.; Viktor, Z.. - : Фонд содействия развитию интернет-медиа, ИТ-образования, человеческого потенциала "Лига интернет-медиа", 2022
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Human Gait Phase Recognition in Embedded Sensor System
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Liu, Zhenbang. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Automatische Gebärdenspracherkennung: Von Videokorpora zu Glossensätzen ... : Automatic sign language recognition : from video corpora to gloss sentences ...
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Comparison of Machine Learning Models: Gesture Recognition Using a Multimodal Wrist Orthosis for Tetraplegics
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In: The Journal of Purdue Undergraduate Research (2020)
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Subunits Inference and Lexicon Development Based on Pairwise Comparison of Utterances and Signs
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In: Information ; Volume 10 ; Issue 10 (2019)
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Acoustic event, spoken keyword and emotional outburst detection
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Prototyputveckling för skalbar motor med förståelse för naturligt språk ; Prototype development for a scalable engine with natural language understanding
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Automatic assessment of singing voice pronunciation: a case study with Jingju music
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In: TDX (Tesis Doctorals en Xarxa) (2018)
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Detecting sections and entities in court decisions using HMM and CRF graphical models
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In: Conférence Extraction et Gestion des Connaissances ; https://hal.archives-ouvertes.fr/hal-02101479 ; Conférence Extraction et Gestion des Connaissances, Université Grenoble alpes (UGA), Jan 2017, Grenoble, France ; http://egc2017.imag.fr/ (2017)
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Score-Informed Syllable Segmentation For Jingju A Cappella Singing Voice With Mel-Frequency Intensity Profiles ...
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Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
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In: Publications (2017)
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Stem-based PoS tagging for agglutinative languages ; Sondan Eklemeli Dillerde Gövde Tabanlı Sözcük Türü ˙I¸saretleme
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О ВОЗМОЖНОСТИ МАТЕМАТИЧЕСКОГО МОДЕЛИРОВАНИЯ ЭВОЛЮЦИИ ПОЛИСЕМИИ ЗНАКОВ ЕСТЕСТВЕННОГО ЯЗЫКА С ПОМОЩЬЮ НЕСТАЦИОНАРНЫХ ПРОЦЕССОВ РОЖДЕНИЯ И ГИБЕЛИ
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ПОДДУБНЫЙ ВАСИЛИЙ ВАСИЛЬЕВИЧ. - : Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования «Национальный исследовательский Томский государственный университет», 2016
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Abstract:
Рассматривается возможность математического моделирования эволюции полисемии ансамбля знаков естественного языка с помощью нестационарных процессов рождения и гибели. Показано, что адекватной математической моделью развития полисемии ансамбля знаков может служить скрытая нестационарная модель процессов рождения и гибели значений языковых знаков. Получено условное распределение состояний такого процесса при экспоненциальных спадах интенсивностей процессов рождения и гибели. Предложен критерий идентификации скрытой модели, дана его реализация на примере словаря языка А. С. Пушкина. ; We consider the possibility of mathematical modeling of the evolution of polysemy of ensemble of signs of natural language by means of non-stationary processes of birth and death. We showed that an adequate mathematical model of polysemy of ensemble of signs might be built on the base of hidden non-stationary model of the birth and death processes of the meanings of linguistic signs. We assume exponential decay of the intensities of the processes of birth and death: x(() = X0 exp((t -10 ), ) = Ц0 exp(_ (t t0 Vx2 ), where t is the current time; t0 is the time moment when the sign appears in the ensemble; X0, ц0 are the initial values of intensities of the processes of birth and death; т = G / X0, т2 = G / ц0 are time decay constants of intensities, and G is the average number of meanings, which the sign may birth and lose during his life: G = jX(t)dt = X0tj, G = |ц(()dt = ц0т2. We received the conditional (with fixed parameters t0, X0, Ц0, G) probability distribution of states n of this process: · 1 ^ f и(Л \k c (t)n-k Г (a (t) +1) f b (t) f X ^ k=0 k !(n k )!Г(п0а (t)-k +1)) 1 -b{t ц(<) Pn (t|S) = exp -(1 b (t)) (1 b (t)) where b(() = expfjц(()dt^, c(()=X(t)-;()b(t). In the hidden model of the statistical ensemble of processes of birth and death the parameters t0, X0, ц0, G of each individual process (of each linguistic sign) randomly vary in relation of each to other, subject to certain distribution laws. Under the assumption of a Pois-son distribution of the flow of signs, the distribution density of the parameter t0 can be considered as uniform on a large enough time interval, while the distributions of parameters X0, ц0, G are unknown. Unconditional probability distribution Pn(t) of the state n of an ensemble of the processes of birth-death (of the polysemy of an ensemble of signs) at moment t is the mathematical expectation of the conditional distribution Pn(t|0) over the distribution of parameters t0, X0, ц0, G. We have solved the task of estimation of the parameter distributions (for identifying of hidden model) according to the empirical polysemy distribution Pne obtained from a representative dictionary, with the subsequent calculation of the optimal theoretical distribution Pn(t). As an identification criterion (criterion of proximity of distribution), we select a logarithmic RMS criterion of type: 1(()=5) 2 J log Pn (t) log Pm ( ) log Pns (t) j=± z n0 n=1 ^ min convenient for large (several orders of magnitude) changes in distributions for different n. The criterion was implemented on example of using of the dictionary of Pushkin's language. We obtain a good agreement of distributions Pn(t) and Pne that confirms the possibility of using of hidden mathematical model of non-stationary process of birth-death for the simulation of polysemy evolution of the ensemble of signs of natural language.
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Keyword:
НЕОДНОРОДНЫЙ ПРОЦЕСС РОЖДЕНИЯ И ГИБЕЛИ,СКРЫТАЯ МАРКОВСКАЯ МОДЕЛЬ,ИДЕНТИФИКАЦИЯ МОДЕЛИ,ЯЗЫКОВОЙ ЗНАК,ПОЛИСЕМИЯ,HETEROGENEOUS PROCESS OF BIRTH AND DEATH,HIDDEN MARKOV MODEL,MODEL IDENTIFICATION,LANGUAGE SIGN,POLYSEMY
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URL: http://cyberleninka.ru/article/n/o-vozmozhnosti-matematicheskogo-modelirovaniya-evolyutsii-polisemii-znakov-estestvennogo-yazyka-s-pomoschyu-nestatsionarnyh
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