378 Matching Annotations
  1. Apr 2019
  2. onomastics.ru onomastics.ru
    vopr_onom.2016.13.2.025.pdf
    1
    1. ildar 16 Apr 2019
      рецензируемаямонографиявключаетвсебя 47 глав, посвященныхразличнымотраслямономастическойнаукиинаписанных 43 ав-торамииз 13 стран. РуководствоавторскимколлективомвзяланасебяКэролХоу, профессоруниверситетаГлазго, бывшийпрезидентМеждународногосо-ветапоономастическимнаукам
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    onomastics.ru/sites/default/files/doi/10.15826/vopr_onom.2016.13.2.025.pdf
  3. hfrir.jvolsu.com hfrir.jvolsu.com
    C:\Documents and Settings\User\Рабочий стол\Vestnik_Yazikoznanie_5_2015\3_Материалы и сообщения\2_Larina_Milevskaya.pmd
    11
    1. ildar 16 Apr 2019
      прецедентный текст, прецедент-ное высказывание, прецедентное имя, преце-дентную ситуацию
    2. ildar 16 Apr 2019
      По мнению исследователя, к прецедентымотносятся «тексты, (1) значимые для той илииной личности в познавательном и эмоцио-нальном отношениях, (2) имеющие сверхлич-ностный характер, то есть хорошо известныеи окружению данной личности, включая ипредшественников и современников, и, нако-нец, такие, (3) обращение к которым возоб-новляется неоднократно в дискурсе даннойязыковой личности» [1, с. 216].
    3. ildar 16 Apr 2019
      Апеллятивизация, то есть переход именсобственных (онимов) в имена нарицатель-ные (апеллятивы) распространена в русскомязыке достаточно широко.
    4. ildar 16 Apr 2019
      При участии апеллятивизированного они-ма Отелло образовалась синонимическаяпара ревнивец – Отелло (Отелло – геройодноименной трагедии В. Шекспира (1564–1616), из ревности убивший свою жену).
    5. ildar 16 Apr 2019
      Апеллятивизированный оним Печкинпополнил синонимический ряд с доминантойпочтальон (Печкин – почтальон, персонаждетских повестей современного российскогописателя Э. Успенского и снятых по ним муль-типликационных фильмов)
    6. ildar 16 Apr 2019
      Рассматривая причины преимуществен-ного использования апеллятивизированных они-мов Шерлок Холмс (Холмс) и Пинкертон посравнению с апеллятивизированными онимамиМегрэ и Пуаро, мы пришли к выводу, что дан-ный факт не связан со степенью известноститого или иного литературного героя. Так, рас-сказы и повести о Шерлоке Холмсе и снятыепо ним фильмы пользуются большой популяр-ностью, а романы о Пинкертоне не переизда-вались много лет. При этом по частоте упот-реблений единицы Шерлок Холмс (Холмс)иПинкертон различаются незначительно
    7. ildar 16 Apr 2019
      Прецедентным именемназывают «индивидуальное имя, связанноеили 1) с широко известным текстом, относя-щимся, как правило, к числу прецедентных(Анна Каренина, Обломов), или 2) с ситуа-цией, широко известной носителям языка ивыступающей как прецедентная (Иван Су-санин); в состав прецедентных имен входяттакже 3) имена-символы, указывающие на не-которую эталонную совокупность определен-ных качеств (Наполеон, Сальери)» [2]
    8. ildar 16 Apr 2019
      Апеллятивизированный оним Митрофа-нушка (Митрофан) пополнил синонимичес-кий ряд с доминантой невежда (Митрофа-нушка – невежественный и не желавшийучиться герой комедии Д.И. Фонвизина (1745–1792) «Недоросль»)
    9. ildar 16 Apr 2019
      про-номинации– «замены нарицательного име-ни собственным (или наоборот), например:Отелло вместо ревнивец»
    10. ildar 16 Apr 2019
      Таким образом, имена литературных ге-роев, подвергшиеся апеллятивизации, входятв синонимический ряд и образуют синоними-ческие пары с соответствующими апелляти-вами. В подавляющем большинстве случа-ев апеллятивизированная единица выступа-ет по отношению к доминанте как стилисти-ческий синоним.
    11. ildar 16 Apr 2019
      Апеллятивизированный оним Плюшкинпополнил синонимический ряд с доминантойскупец (Плюшкин – отличавшийся необычай-ной скупостью помещик, персонаж поэмыН.В. Гоголя (1809–1852) «Мертвые души»).
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    hfrir.jvolsu.com/index.php/en/component/attachments/download/3096
  4. arxiv.org arxiv.org
    1811.11987
    1
    1. ildar 10 Apr 2019
      cnn deep learning
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    arxiv.org/pdf/1811.11987.pdf
  5. ceur-ws.org ceur-ws.org
    AICS_2016_paper_48.pdf
    1
    1. ildar 08 Apr 2019
      Distant reading o ersnew ways to challenge assumptions about genre, narrative and other aspects ofliterature, by facilitating the analysis of large-scale collections of literary works.Numerous approaches have been proposed and tested for this purpose, includ-ing those based on statistical topic models [10], character pro ling [6], characterfrequency analysis [5, 22], and sentiment analysis [4].
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    ceur-ws.org/Vol-1751/AICS_2016_paper_48.pdf
  6. kilianollivier.fr kilianollivier.fr
    graph-embedding.pdf
    13
    1. ildar 08 Apr 2019
      DeepWalk was introduced by Perozziet al.in 2014 [28]. The authors brought the idea of us-ing recent work in word embedding. They emphasize interesting similarities between NLP andSNA (as seen in II.1). The main idea is to givesentencesof nodes (instead of words) as inputfor word embedding algorithms. They used random walk to produce suchsentencesout of anunweighted graph (the overall architecture of Deepwalk can be seen in figure III.5).
    2. ildar 08 Apr 2019
      Negative sampling (NS) is a simplified version of Noise-contrastive estimation (NCE) [35]. Theyboth are sampling approaches : instead of calculating a cross product for each word in the vo-cabulary, they just use samples of the vocabulary set. Unlike the hierarchical softmax, samplingapproaches do not exhibit a softmax layer. Sampling approaches are only useful during the trainingtime. A full softmax must be computed during the evaluation (that is not a problem since we donot use the model after the training, we just get the feature vectors)
    3. ildar 08 Apr 2019
      According to Mikolovet al.it seems that the non linear hidden layer brings too much com-plexity [26]. In this paper, the author propose two shallow neural networks without hidden layer,Continuous Bag of Words (CBOW) and Skip-Gram model.
    4. ildar 08 Apr 2019
      Asstated before, Social Network Analysis often deals with millions of nodes, therefore the size of theadjacency matrix is the square of number of nodes. Not mentioning obvious performance issues,this embedding would be hardly exploitable because of the curse of dimensionality
    5. ildar 08 Apr 2019
      The simplest graph embedding we can consider is the adjacency matrix of the network
    6. ildar 08 Apr 2019
      Producing vector representations of nodes builds a bridge between social network analysis,data analytics and statistics. The vectors can therefore be used by machine learning algorithmsthat takes vectors as input for prediction, distance computation or clustering.
    7. ildar 08 Apr 2019
      Node role identificationaims to infer from a social graph the role the node is playing into thenetwork.
    8. ildar 08 Apr 2019
      Community detectionaims to identify groups of nodes highly interconnected
    9. ildar 08 Apr 2019
      Link predictionaims to predict the connections that will appear between the actors of a socialnetwork.
    10. ildar 08 Apr 2019
      Node classificationaims to retrieved individual data of an actor (age, gender, interestsetc.)
    11. ildar 08 Apr 2019
      Information diffusion studyingaims to understand how an information is diffused through thenetwork
    12. ildar 08 Apr 2019
      Network modelingaims to simulate the behavior of the network with a simple model.
    13. ildar 08 Apr 2019
      This idea is summed up by Degenne and Forsé [1]: “Instead of thinking realityin terms of relations between actors, lots of those who analyze empirical data limit themselvesinto thinking it in categories (for example: young people, women, executives, developing countries,etc.). These categories are built by aggregating people with similar features and,a priori, relevantfor the current issue.
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    kilianollivier.fr/graph-embedding.pdf
  7. amueller.github.io amueller.github.io
    NMF & Outlier Detection
    2
    1. ildar 08 Apr 2019
      Downsides of NMF Can only be applied to non-negative data Interpretability is hit or miss Non-convex optimization, requires initialization Not orthogonal
    2. ildar 08 Apr 2019
      Why NMF? Meaningful signs Positive weights No “cancellation” like in PCA Can learn over-complete representation
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    amueller.github.io/COMS4995-s18/slides/aml-18-040218-nmf-outlier-detection/
  8. www.cc.gatech.edu www.cc.gatech.edu
    textoutlier17.pdf
    3
    1. ildar 08 Apr 2019
      We extend the fact thatNMF is similar to pLSI and LDA generative models and modelthe outliers using the`1;2-norm. This particular formulation ofNMF is non-standard, and requires careful design of optimizationmethods to solve the problem. We solve the resulting optimiza-tion problem using block coordinate descent technique.
    2. ildar 08 Apr 2019
      One advantage of matrix factorizationmethods is that they decompose the term-document structureof the underlying corpus into a set of semantic term clusters anddocument clusters. The semantic nature of this decompositionprovides the context in which a document may be interpreted foroutlier analysis.
    3. ildar 08 Apr 2019
      there are surprisingly few methods which arespecifically focusedon this domain, even though many genericmethods such as distance-based methods can be easily adapted tothis domain [13,20], and are often used for text outlier analysis
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    cc.gatech.edu/~hpark/papers/textoutlier17.pdf
  9. maroo.cs.umass.edu maroo.cs.umass.edu
    getpdf.php
    5
    1. ildar 08 Apr 2019
      Thenotionofaneventdiffersfromabroadercategoryofeventsbothinspatial/temporallocalizationandinspecificity.Forexample,theeruptionofMountPinatuboonJune15th,1991isconsidertobeanevent,whereasvolcaniceruptioningeneralisconsideredtobeaclassofevents
    2. ildar 08 Apr 2019
      Eventsmightbeunexpected,suchastheerup-tionofavolcano,orexpected,suchasapoliticalelection
    3. ildar 08 Apr 2019
      Duringthefirstportionofthisstudy,thenotionofa“topic”wasmodifiedandsharp-enedtobean“event”,meaningsomeuniquethingthathap-pensatsomepointintime
    4. ildar 08 Apr 2019
      Thetrackingtaskisdefinedtobethetaskofassociatingincomingstorieswitheventsknowntothesystem.Aneventisdefined(“known”)byitsassociationwithstoriesthatdiscusstheevent.Thuseachtargeteventisde-finedbyalistofstoriesthatdiscussit
    5. ildar 08 Apr 2019
      ThisstudycorpusspanstheperiodfromJuly1,1994toJune30,1995andincludesnearly16,000stories,withabouthalftakenfromReutersnewswireandhalffromCNNbroadcastnewstranscripts.
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    maroo.cs.umass.edu/getpdf.php
  10. arxiv.org arxiv.org
    1711.04305.pdf
    9
    1. ildar 08 Apr 2019
      Relational Topic Models (RTM), is another extension, RTM is a hierarchicalmodel of networks and per-node attribute data. First, each document was createdfrom topics in LDA. Then, modelling the connections between documents and con-sidered as binary variables, one for each pair from documents. These are distributedbased on a distribution that depends on topics used to generate each of the constituentdocuments. So in this way, the content of the documents are statistically linked to thelink structure between them and we can say that this model can be used to summarizea network of documents [69]
    2. ildar 08 Apr 2019
      MedLDA, proposed the maximum entropy discrimination latent Dirichlet allo-cation (MedLDA) model, which incorporates the mechanism behind the hierarchi-cal Bayesian models (such as, LDA) with the max margin learning (such as SVMs)according to a unified restricted optimization framework.
    3. ildar 08 Apr 2019
      LLDA is a supervised algorithm that makes topics applying the Labels assigned man-ually. Therefore, LLDA can obtain meaningful topics, with words that map well tothe labels applied. As a disadvantage, Labeled LDA has limitation to support latentsubtopics within a determined label or any global topics. For overcome this problem,proposed partially labeled LDA (PLDA)
    4. ildar 08 Apr 2019
      DTM, Dynamic Topic Model (DTM) is introduced by Blei and Laerty as an ex-tension of LDA that this model can obtain evolution of topics over time in a sequen-tially arranged corpus of documents and exhibits evolution of word-topic distributionwhich causes it easy to vision the topic trend [73]. As an advantage, DTM is veryimpressible for extracting topics from collections that change slowly over a period oftime.
    5. ildar 08 Apr 2019
      Author-Topic model [75], is a popular and simple probabilistic model in topicmodeling for finding relationships among authors, topics, words and documents. Thismodel provides a distribution of dierent topics for each author and also a distributionof words for each topic. For evaluation, the authors used 1700 papers of NIPS con-ference and also 160,000 CiteSeer abstracts of CiteSeer dataset. To estimate the topicand author distributions applied Gibbs sampling. According to their result, showedthis approach can provide a significantly predictive for interests of authors in per-plexity measure.
    6. ildar 08 Apr 2019
      Undeniably, this period(2003 to 2009) is very important because key and baseline approaches were intro-duced, such as: CorrLDA, Author-Topic Model , DTM and , RTM etc
    7. ildar 08 Apr 2019
      In summary, this paper makes four main contributions:–We investigate scholarly articles (from 2003 to 2016) which are related to TopicModeling based on LDA to discover the research development, current trends andintellectual structure of topic modeling based on LDA.–We investigate topic modeling applications in various sciences.–We summarize challenges in topic modeling, such as image processing, Visualiz-ing topic models, Group discovery, User Behavior Modeling, and etc.–We introduce some of the most famous data and tools in topic modeling
    8. ildar 08 Apr 2019
      An analysis of geographic information is another issue that can be referred to [17].They introduced a novel method based on multi-modal Bayesian models to describesocial media by merging text features and spatial knowledge that called GeoFolk.
    9. ildar 08 Apr 2019
      nother group of researchers focused on topic modeling in software engineering,in [8] for the first time, they used LDA, to extract topics in source code and performvisualization of software similarity,
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    arxiv.org/pdf/1711.04305.pdf
  11. Mar 2019
  12. www.phontron.com www.phontron.com
    CS 11-747: Neural Networks for NLP
    1
    1. ildar 26 Mar 2019
      nlp deep learning @course
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    phontron.com/class/nn4nlp2017/schedule.html
  13. www.comp.nus.edu.sg www.comp.nus.edu.sg
    CS6101 - Deep Learning for NLP
    1
    1. ildar 26 Mar 2019
      @course nlp deep learning
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    comp.nus.edu.sg/~kanmy/courses/6101_1810/
  14. www.cs.jhu.edu www.cs.jhu.edu
    601.465/665 - Natural Language Processing - JHU CS
    1
    1. ildar 03 Mar 2019
      @nlp @course
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    cs.jhu.edu/~jason/465/
  15. mt-class.org mt-class.org
    MT class
    1
    1. ildar 02 Mar 2019
      @course @nlp
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    mt-class.org/
  16. Feb 2019
  17. users.umiacs.umd.edu users.umiacs.umd.edu
    CMSC/LING 723 / INST 735
    1
    1. ildar 23 Feb 2019

      Jordan Boyd-Graber video dl, rl

      nlp @course
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    users.umiacs.umd.edu/~jbg/teaching/CMSC_723/
  18. people.ischool.berkeley.edu people.ischool.berkeley.edu
    Info 256. Applied Natural Language Processing
    1
    1. ildar 20 Feb 2019
      nlp @course
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    people.ischool.berkeley.edu/~dbamman/info256.html
  19. localhost:8000 localhost:8000
    Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts
    1
    1. ildar 12 Feb 2019
      Characters form the core of manycomputational analyses, from inferring prototypi-cal character types

      пинг

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    localhost:8000/10.pdf
  20. Jan 2019
  21. anderssoegaard.github.io anderssoegaard.github.io
    Anders Søgaard
    1
    1. ildar 08 Jan 2019
      @scientist nlp
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    anderssoegaard.github.io/
  22. Dec 2018
  23. www.socher.org www.socher.org
    Richard Socher - Home Page
    1
    1. ildar 20 Dec 2018
      nlp @researcher
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    socher.org/
  24. stp.lingfil.uu.se stp.lingfil.uu.se
    Syntactic analysis in language technology, autumn 2014
    1
    1. ildar 20 Dec 2018
      nlp @course
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    stp.lingfil.uu.se/~sara/kurser/5LN455-2014/
  25. maslinsky.spb.ru maslinsky.spb.ru
    Index of /courses/cmta2018/
    1
    1. ildar 20 Dec 2018
      nlp @course
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    maslinsky.spb.ru/courses/cmta2018/
  26. www.machinelearning.ru www.machinelearning.ru
    Математические методы анализа текстов (ВМиК МГУ) / 2018
    1
    1. ildar 15 Dec 2018
      nlp @course
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    machinelearning.ru/wiki/index.php
  27. yaikhom.com yaikhom.com
    Implementing the DBSCAN clustering algorithm
    1
    1. ildar 15 Dec 2018
      clusterisation
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    yaikhom.com/2015/09/04/implementing-the-dbscan-clustering-algorithm.html
  28. programminghistorian.org programminghistorian.org
    Visualizing Data with Bokeh and Pandas
    1
    1. ildar 12 Dec 2018
      visualization
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    programminghistorian.org/en/lessons/visualizing-with-bokeh
  29. github.com github.com
    weitechen/anafora
    1
    1. ildar 08 Dec 2018
      nlp annotation tool
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    github.com/weitechen/anafora
  30. stal.blogspot.com stal.blogspot.com
    Hitchhiker's Guide to Software Architecture and Everything Else - by Michael Stal: Architect always implements
    1
    1. ildar 08 Dec 2018
      software architecture
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    stal.blogspot.com/2008/08/architect-always-implements.html
  31. mrpandey.github.io mrpandey.github.io
    D3 Graph Theory - Interactive Graph Theory Tutorials
    1
    1. ildar 08 Dec 2018
      graph theory visualization
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    mrpandey.github.io/d3graphTheory/index.html
  32. github.com github.com
    MUSoC/Visualization-of-popular-algorithms-in-Python
    1
    1. ildar 08 Dec 2018
      graph theory @code
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    github.com/MUSoC/Visualization-of-popular-algorithms-in-Python
  33. www.cs.utexas.edu www.cs.utexas.edu
    Raymond J. Mooney's Home Page
    1
    1. ildar 05 Dec 2018
      nlp @researcher
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    cs.utexas.edu/users/mooney/
  34. www.cs.umd.edu www.cs.umd.edu
    Computational Linguistics I ·
    1
    1. ildar 05 Dec 2018
      nlp @course
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    cs.umd.edu/class/fall2017/cmsc723/
  35. www.cs.umd.edu www.cs.umd.edu
    · Introduction to Natural Language Processing
    1
    1. ildar 05 Dec 2018
      nlp @course
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    cs.umd.edu/class/fall2018/cmsc470/
  36. www.pitt.edu www.pitt.edu
    LING 1330/2330 Introduction to Computational Linguistics, University of Pittsburgh
    1
    1. ildar 05 Dec 2018
      nlp @course
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    pitt.edu/~naraehan/ling1330/
  37. crowdsourcing-class.org crowdsourcing-class.org
    Crowdsourcing and Human Computation - NETS 213 - University of Pennsylvania
    1
    1. ildar 05 Dec 2018
      crowdsourcing ai @course
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    crowdsourcing-class.org/index.html
  38. kansas.ru kansas.ru
    Введение в обработку естественного языка :: Павел Браславский :: Introduction to NLP :: Pavel Braslavski
    1
    1. ildar 05 Dec 2018
      nlp @course
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    kansas.ru/nlp2016/
  39. artificial-intelligence-class.org artificial-intelligence-class.org
    CIS 521 - Artificial Intelligence - University of Pennsylvania
    1
    1. ildar 05 Dec 2018
      ai @course
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    artificial-intelligence-class.org/index.html
  40. linguistics.illinois.edu linguistics.illinois.edu
    Dr. Corina Roxana Girju | Linguistics at Illinois
    1
    1. ildar 05 Dec 2018
      nlp @researcher
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    linguistics.illinois.edu/directory/profile/girju
  41. www.cs.columbia.edu www.cs.columbia.edu
    COMS W4705: Natural Language Processing (Spring 2018)
    1
    1. ildar 05 Dec 2018
      nlp @course
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    cs.columbia.edu/~mcollins/cs4705-fall2018/
  42. ru-eval.ru ru-eval.ru
    Syntax. RU-EVAL: Evaluation of Russian NLP tools
    1
    1. ildar 05 Dec 2018
      nlp
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    ru-eval.ru/go/syntax.html
  43. bcourses.berkeley.edu bcourses.berkeley.edu
    Talking to Robots
    1
    1. ildar 05 Dec 2018
      nlp @course
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    bcourses.berkeley.edu/courses/1464824/
  44. www.cs.upc.edu www.cs.upc.edu
    ~gatius
    1
    1. ildar 04 Dec 2018
      nlp @researcher
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    cs.upc.edu/~gatius/
  45. users.umiacs.umd.edu users.umiacs.umd.edu
    CMSC 726
    1
    1. ildar 04 Dec 2018
      ml @course
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    users.umiacs.umd.edu/~jbg/teaching/CMSC_726/
  46. users.umiacs.umd.edu users.umiacs.umd.edu
    Jordan Boyd-Graber: Home
    1
    1. ildar 04 Dec 2018
      nlp @researcher
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    users.umiacs.umd.edu/~jbg/
  47. www.cs.umd.edu www.cs.umd.edu
    Computational Linguistics I ·
    1
    1. ildar 04 Dec 2018
      nlp @course
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    cs.umd.edu/class/fall2015/cmsc723/
  48. www.cs.utexas.edu www.cs.utexas.edu
    CS 388: Natural Language Processing
    1
    1. ildar 04 Dec 2018
      nlp @course
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    cs.utexas.edu/~mooney/cs388/
  49. winstonhsu.info winstonhsu.info
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  50. www.cs.tau.ac.il www.cs.tau.ac.il
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  51. www.cs.columbia.edu www.cs.columbia.edu
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  52. cs.brown.edu cs.brown.edu
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  54. socialmedia-class.org socialmedia-class.org
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  55. courses.washington.edu courses.washington.edu
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  57. people.cs.umass.edu people.cs.umass.edu
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  58. web.cs.ucla.edu web.cs.ucla.edu
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  59. computational-linguistics-class.org computational-linguistics-class.org
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  60. cocoxu.github.io cocoxu.github.io
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  62. www.commonlounge.com www.commonlounge.com
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  64. www.cs.cmu.edu www.cs.cmu.edu
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  67. berkeley-deep-learning.github.io berkeley-deep-learning.github.io
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  68. wosp.core.ac.uk wosp.core.ac.uk
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  70. github.com github.com
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  71. l2r.cs.uiuc.edu l2r.cs.uiuc.edu
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  72. l2r.cs.uiuc.edu l2r.cs.uiuc.edu
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  73. ai.google ai.google
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  74. www.technologyreview.com www.technologyreview.com
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  75. rtw.ml.cmu.edu rtw.ml.cmu.edu
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  76. bcourses.berkeley.edu bcourses.berkeley.edu
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  77. bcourses.berkeley.edu bcourses.berkeley.edu
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  80. people.csail.mit.edu people.csail.mit.edu
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  81. www.uio.no www.uio.no
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  82. www.uio.no www.uio.no
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  84. www.quora.com www.quora.com
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  85. arxiv.org arxiv.org
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  86. www.krugosvet.ru www.krugosvet.ru
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      Возникновение когнитивной лингвистики – это один из эпизодов общего методологического сдвига, начавшегося в лингвистике с конца в 1950-х годов и сводящегося к снятию запрета на введение в рассмотрение «далеких от поверхности», недоступных непосредственному наблюдению теоретических (модельных) конструктов

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  87. github.com github.com
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  88. source.opennews.org source.opennews.org
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  89. www.semanticscholar.org www.semanticscholar.org
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  90. en.wikipedia.org en.wikipedia.org
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      A semantic treebank is a collection of natural language sentences annotated with a meaning representation. These resources use a formal representation of each sentence's semantic structure.
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  96. github.com github.com
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  97. shop.oreilly.com shop.oreilly.com
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  100. github.com github.com
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  101. github.com github.com
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  102. en.wikipedia.org en.wikipedia.org
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      A syntactically annotated corpus (treebank) is a part of Russian National Corpus.[2] It contains 40,000 sentences (600,000 words) which are fully syntactically and morphologically annotated. The primary annotation was made by ETAP-3 and then manually verified by competent linguists.
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  103. Nov 2018
  104. click-or-die.ru click-or-die.ru
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  107. wikimapia.org wikimapia.org
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  108. www.nubo.ru www.nubo.ru
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  109. ru.wikipedia.org ru.wikipedia.org
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  110. dic.academic.ru dic.academic.ru
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  111. vk.com vk.com
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  112. drinkinfo.ru drinkinfo.ru
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  113. www.theguardian.com www.theguardian.com
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  114. www.nature.com www.nature.com
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  115. en.wikipedia.org en.wikipedia.org
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  116. github.com github.com
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  117. github.com github.com
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  118. www.coursera.org www.coursera.org
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  119. superuser.com superuser.com
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  120. github.com github.com
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  121. artemis-ml.readthedocs.io artemis-ml.readthedocs.io
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  122. github.com github.com
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  123. plantcv.readthedocs.io plantcv.readthedocs.io
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  124. github.com github.com
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  125. medium.freecodecamp.org medium.freecodecamp.org
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  126. github.com github.com
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  127. github.com github.com
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  128. github.com github.com
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  129. alligator.io alligator.io
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  130. www.youtube.com www.youtube.com
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  131. dev.to dev.to
    VueJS - Props, eventBus, Vuex... Battle Royale TO THE DEATH
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      Home template with Bootstrap for layout purposes. Wrestler information in the array wrestlerList.

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  132. Oct 2018
  133. ccl.pku.edu.cn ccl.pku.edu.cn
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    ccl.pku.edu.cn/doubtfire/NLP/Lexical_Analysis/Word_Lemmatization/Introduction/Computational Morphology.htm
  134. habr.com habr.com
    Способы представления словарей для автоматической обработки текстов
    1
    1. ildar 31 Oct 2018
      morphology rule-based
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    habr.com/post/190694/
  135. en.wikipedia.org en.wikipedia.org
    Text segmentation - Wikipedia
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    1. ildar 30 Oct 2018
      In English and many other languages using some form of the Latin alphabet, the space is a good approximation of a word divider (word delimiter), although this concept has limits because of the variability with which languages emically regard collocations and compounds.

      пробел -- хороший аппроксиматор разделения слов. но есть коллокации и сложносоставные слова.

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    en.wikipedia.org/wiki/Text_segmentation
  136. ru.wikipedia.org ru.wikipedia.org
    Проект:Внесение символов алфавитов народов России в Юникод — Википедия
    1
    1. ildar 30 Oct 2018
      unicode
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    ru.wikipedia.org/wiki/Проект:Внесение_символов_алфавитов_народов_России_в_Юникод
  137. linguistics.stackexchange.com linguistics.stackexchange.com
    Is there really a difference between agglutinative and non-agglutinative languages when spoken?
    1
    1. ildar 30 Oct 2018
      Since the question is about determining the morphological profile of a language, the issue of determining word boundaries is quite central.

      Так как морфологический профиль, то проблема разделения слов (word boundaries) является центральной

      word segmentation morphology
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    linguistics.stackexchange.com/questions/1069/is-there-really-a-difference-between-agglutinative-and-non-agglutinative-languag
  138. en.wikipedia.org en.wikipedia.org
    Agglutination - Wikipedia
    1
    1. ildar 30 Oct 2018
      Agglutination is a linguistic process pertaining to derivational morphology in which complex words are formed by stringing together morphemes without changing them in spelling or phonetics.
      morphology
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    en.wikipedia.org/wiki/Agglutination
  139. omeka.org omeka.org
    Omeka Classic - Zotero Import
    1
    1. ildar 30 Oct 2018
      omeka zotero
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    omeka.org/classic/plugins/ZoteroImport/
  140. academic.oup.com academic.oup.com
    Omeka in the classroom: The challenges of teaching material culture in a digital world
    1
    1. ildar 30 Oct 2018
      omeka
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    academic.oup.com/dsh/article-abstract/28/2/279/1037192
  141. askubuntu.com askubuntu.com
    Getting internet browsing history from shell
    1
    1. ildar 30 Oct 2018
      browser history
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    askubuntu.com/questions/631631/getting-internet-browsing-history-from-shell
  142. github.com github.com
    kshaffer/pypothesis
    1
    1. ildar 30 Oct 2018
      Python scripts for interacting with the hypothes.is API
      annotation
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    github.com/kshaffer/pypothesis
  143. github.com github.com
    ApolloAuto/apollo
    1
    1. ildar 30 Oct 2018
      self-driving car
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    github.com/ApolloAuto/apollo
  144. github.com github.com
    Intellicode is unable to download its model behind proxy · Issue #4 · MicrosoftDocs/intellicode
    1
    1. ildar 29 Oct 2018

      intellicode bug workaround

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    github.com/MicrosoftDocs/intellicode/issues/4
  145. github.com github.com
    Hypothesis
    1
    1. ildar 29 Oct 2018
      annotation
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    github.com/hypothesis
  146. ru.wikipedia.org ru.wikipedia.org
    Ханча — Википедия
    1
    1. ildar 29 Oct 2018
      Так как ханча никогда не упрощали централизованно, её знаки в большинстве случаев идентичны традиционным китайским и японским иероглифам. Очень малое число знаков ханча имеют скорописные формы или уникальны для корейского языка.

      Иероглифы упрощают. Видимо, для ускорения письма. А для чего еще?

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    ru.wikipedia.org/wiki/Ханча
  147. www.kamihq.com www.kamihq.com
    Kami | Online PDF and Document Viewer and Annotation App
    1
    1. ildar 26 Oct 2018
      annotation
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    kamihq.com/