Proceedings of Machine Learning Research. Proceedings of The International Conference on Artificial Intelligence and Statistics (AISTATS 2016)
Proceedings of Machine Learning Research. Volume 51: Artificial Intelligence and Statistics, 9-11 May 2016, Cadiz, Spain
The recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task.
In theory, a poverty line can be defined as the cost of a common (inter-personally comparable) utility level across a population. But how can one know if this holds in practice? For groups sharing common consumption needs but facing different prices, the theory of revealed preference can be used to derive testable implications of utility consistency knowing only the "poverty bundles" and their prices. Heterogeneity in needs calls for extra information. We argue that subjective welfare data offer a credible means of testing utility consistency across different needs groups. A case study of Russia's official poverty lines shows how revealed preference tests can be used in conjunction with qualitative information on needs heterogeneity. The results lead us to question the utility consistency of Russia's official poverty lines.
The volume contains the abstracts of the 12th International Conference "Intelligent Data Processing: Theory and Applications". The conference is organized by the Russian Academy of Sciences, the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences and the Scientific and Coordination Center "Digital Methods of Data Mining". The conference has being held biennially since 1989. It is one of the most recognizable scientific forums on data mining, machine learning, pattern recognition, image analysis, signal processing, and discrete analysis. The Organizing Committee of IDP-2018 is grateful to Forecsys Co. and CFRS Co. for providing assistance in the conference preparation and execution. The conference is funded by RFBR, grant 18-07-20075. The conference website http://mmro.ru/en/.
The paper continues research into words denoting everyday life objects in the Russian language. This research is conducted for developing a new encyclopedic thesaurus of Russian everyday life terminology. Working on this project brings up linguistic material which leads to discovering new trends and phenomena not covered by the existing dictionaries. We discuss derivation models which gain polularity: clipped forms (komp < komp’juter ‘computer’, nout < noutbuk ‘notebook computer’, vel < velosiped ‘bicycle’, mot<motocikl ‘motorbike’), competing masculine and feminine con- tracted nouns derived from adjectival noun phrases (mobil’nik (m.) / mo- bilka (f.) < mobil’nyj telefon (m.) ‘mobile phone’, zarjadnik (m.) / zarjadka (f.) < zarjadnoe ustrojstvo (n.) ‘AC charger’), hybrid compounds (plat’e- sviter ‘sweater dress’, jubka-brjuki ‘skirt pants’, shapkosharf ‘scarf hat’, vilkolozhka ‘spork, foon’). These words vary in spelling and syntactic behav- iour. We describe a newly formed series of words denoted multifunctional objects: mfushkaZ< MFU < mnogofunkcional’noe ustrojstvo ‘MFD, multi- function device’, mul’titul ‘multitool’, centr ‘unit, set’. Explaining the need to compose frequency lists of word meanings rather than just words, we of- fer a technique for gathering such lists and provide a sample produced from our own data. We also analyze existing dictionaries and perform various experiments to study the changes in word meanings and their comparative importance for speakers. We believe that, apart from the practical usage for our lexicographic project, our results might prove interesting for research in the evolution of the Russian lexical system.
In an effort to make reading more accessible, an automated readability formula can help students to retrieve appropriate material for their language level. This study attempts to discover and analyze a set of possible features that can be used for single-sentence readability prediction in Russian. We test the influence of syntactic features on predictability of structural complexity. The readability of sentences from SynTagRus corpus was marked up manually and used for evaluation.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.