Visose pasaulio kalbose yra terminai spalvoms reikšti, tačiau priklausomai nuo ekstralingvistinių veiksnių, skirtingos tautos turi nevienodą jų skaičių. Išanalizavę dešimtis kalbų lingvistai B. Berlinas ir P. Kėjus nustatė, jog ankstyvame kalbų raidos etape tebuvo du žodžiai spalvoms apibrėžti: vienas - tamsioms, kitas – šviesioms. Šviesai atstovauja balta spalva, tamsai – juoda. Į prototipines baltumo ir juodumo konceptų reikšmes (baltas, -a – sniego spalvos, visai šviesus, juodas, -a – visiškai tamsus, kaip anglis) remiasi nemažai metaforinių reikšmių. Šio magistro darbo tikslas – išskirti skirtingas ir bendras baltumo ir juodumo metaforines reikšmes, būdingas lietuvių ir anglų kalboms. Tyrimo medžiagą sudaro lietuvių kalbos tekstyno grožinės literatūros blokas ir anglų kalbos tekstyno medžiaga. Išanalizavus tekstyno medžiagą nustatyta, jog prototipinės juodo reikšmės: tamsus, neperregimas, tankus, gilus; nešvarus, suteptas; rasė, gymis; rūšies pavadinimas; kava, arbata; metaforinės reikšmės: nešvarus, nedoras; prastas apie maistą (tik lietuvių kalboje); blogis: a) viduje išgyvenamas (emocijos); b) bauginantys, tragiški, nelaimingi įvykiai, su jais sietinos vietos; sunkus, kasdienis; neteisėta veikla, tokios veiklos rezultatai; simbolinės reikšmės: artėjančios nelaimės simbolis; nelaimės pranašai (blogio simboliai); gedulo simbolis. Tekstyno medžiagos analizė parodė, jog prototipinės balto reikšmės: šviesa; perregimas, bespalvis; švara; tuštuma; rasė, gymis; rūšies... [toliau žr. visą tekstą] / Every language has colour terms, but different amount of them depending on extra linguistic features. Linguist B. Berlin and P. Kay analysed dozens of languages and determined that there were only two colour terms (dark and bright) in the first stage of language evolution. Bright is represented by white and dark is represented by black. Many metaphoric meanings are based on prototypical meanings of concepts of whiteness (white – of colour of snow, very bright) and blackness (black – very dark, like coal). The goal of this master thesis is to determine common and different metaphorical meanings of whiteness and blackness characteristic for Lithuanian and English languages. The research material is collected from corpuses of Lithuanian and English languages. The data of the analysis of both concepts showed, blackness and whiteness in most cases are used to identify a feature of colour, i.e. prototypical colour meaning. But it also revealed a number of cases when the colour term does not denote a colour feature, but it is used metaphorically, with objects lacking the colour feature. The analysis has also revealed the importance of the symbolic meaning in the research of the colour concepts. The concept of Blackness in both languages is perceived similarly. Prototypical meanings are the following: dark, lacking light, impenetrable, soiled; used to characterize dark skin pigmentation; certain object in black colour (blackcurrant, black bread and etc.); (coffee or tea) without... [to full text]

<p>In the current information age, massive amounts of data are gathered, at a rate prohibiting their effective structuring, analysis, and conversion into useful knowledge. This information overload is manifested both in large numbers of data objects recorded in data sets, and large numbers of attributes, also known as high dimensionality. This dis-sertation deals with problems originating from high dimensionality of data representation, referred to as the &ldquo;curse of dimensionality,&rdquo; in the context of machine learning, data mining, and information retrieval. The described research follows two angles: studying the behavior of (dis)similarity metrics with increasing dimensionality, and exploring feature-selection methods, primarily with regard to document representation schemes for text classification. The main results of the dissertation, relevant to the first research angle, include theoretical insights into the concentration behavior of cosine similarity, and a detailed analysis of the phenomenon of hubness, which refers to the tendency of some points in a data set to become hubs by being in-cluded in unexpectedly many <em>k</em>-nearest neighbor lists of other points. The mechanisms behind the phenomenon are studied in detail, both from a theoretical and empirical perspective, linking hubness with the (intrinsic) dimensionality of data, describing its interaction with the cluster structure of data and the information provided by class la-bels, and demonstrating the interplay of the phenomenon and well known algorithms for classification, semi-supervised learning, clustering, and outlier detection, with special consideration being given to time-series classification and information retrieval. Results pertaining to the second research angle include quantification of the interaction between various transformations of high-dimensional document representations, and feature selection, in the context of text classification.</p> / <p>U tekućem &bdquo;informatičkom dobu&ldquo;, masivne količine podataka se<br />sakupljaju brzinom koja ne dozvoljava njihovo efektivno strukturiranje,<br />analizu, i pretvaranje u korisno znanje. Ovo zasićenje informacijama<br />se manifestuje kako kroz veliki broj objekata uključenih<br />u skupove podataka, tako i kroz veliki broj atributa, takođe poznat<br />kao velika dimenzionalnost. Disertacija se bavi problemima koji<br />proizilaze iz velike dimenzionalnosti reprezentacije podataka, često<br />nazivanim &bdquo;prokletstvom dimenzionalnosti&ldquo;, u kontekstu ma&scaron;inskog<br />učenja, data mining-a i information retrieval-a. Opisana istraživanja<br />prate dva pravca: izučavanje pona&scaron;anja metrika (ne)sličnosti u odnosu<br />na rastuću dimenzionalnost, i proučavanje metoda odabira atributa,<br />prvenstveno u interakciji sa tehnikama reprezentacije dokumenata za<br />klasifikaciju teksta. Centralni rezultati disertacije, relevantni za prvi<br />pravac istraživanja, uključuju teorijske uvide u fenomen koncentracije<br />kosinusne mere sličnosti, i detaljnu analizu fenomena habovitosti koji<br />se odnosi na tendenciju nekih tačaka u skupu podataka da postanu<br />habovi tako &scaron;to bivaju uvr&scaron;tene u neočekivano mnogo lista k najbližih<br />suseda ostalih tačaka. Mehanizmi koji pokreću fenomen detaljno su<br />proučeni, kako iz teorijske tako i iz empirijske perspektive. Habovitost<br />je povezana sa (latentnom) dimenzionalno&scaron;ću podataka, opisana<br />je njena interakcija sa strukturom klastera u podacima i informacijama<br />koje pružaju oznake klasa, i demonstriran je njen efekat na<br />poznate algoritme za klasifikaciju, semi-supervizirano učenje, klastering<br />i detekciju outlier-a, sa posebnim osvrtom na klasifikaciju vremenskih<br />serija i information retrieval. Rezultati koji se odnose na<br />drugi pravac istraživanja uključuju kvantifikaciju interakcije između<br />različitih transformacija vi&scaron;edimenzionalnih reprezentacija dokumenata<br />i odabira atributa, u kontekstu klasifikacije teksta.</p>