1,357,019 research outputs found
Dmitry Panchenko: The Sherrington–Kirkpatrick Model
No full textA review of the Book by Dmitry Panchenko "The Sherrington Kirkpatrick Model" is given and perspectives are discussed
A framework for enriching lexical semantic resources with distributional semantics
No full textWe present an approach to combining distributional semantic representations induced from text corpora with manually constructed lexical semantic networks. While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks. We start with a distributional representation of induced senses of vocabulary terms, which are accompanied with rich context information given by related lexical items. We then automatically disambiguate such representations to obtain a full-fledged proto-conceptualization, i.e. a typed graph of induced word senses. In a final step, this proto-conceptualization is aligned to a lexical ontology, resulting in a hybrid aligned resource. Moreover, unmapped induced senses are associated with a semantic type in order to connect them to the core resource. Manual evaluations against ground-truth judgments for different stages of our method as well as an extrinsic evaluation on a knowledge-based Word Sense Disambiguation benchmark all indicate the high quality of the new hybrid resource. Additionally, we show the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text for addressing high-end knowledge acquisition tasks such as cleaning hypernym graphs and learning taxonomies from scratch
VIBRATIONAL ANHARMONICITY AND SCALING THE QUANTUM MECHANICAL MOLECULAR FORCE FIELD
No full text Yu. N. Panchenko, P. Pulay and F. T\""{o}r\""{o}k, J. Mol. Struct. 34, 283 (1976); V.I. Pupyshev, Yu.N. Panchenko, Ch. W. Bock and G. Pongor, J. Chem. Phys. 94, 1247 (1991); Yu. N. Panchenko, G.R. De Mar\'{e} and V.I. Pupyshev, J. Phys. Chem. 99, 17544 (1995); Yu. N. Panchenko, Moscow Univ. Chem. Bull. 51 (5), 23 (1996). D.M. Dennison, Rev. Mod, Phys. 12, 175 (1940); G.E. Hansen and D.M. Dennison, J. Chem. Phys. 20, 313 (1952).Author Institution: Laboratory of Molecular Spectroscopy, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University; Laboratory of Molecular Structure and Quantum Mechanics, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University; Chemistry Department, Philadelphia College of Textiles \& ScienceThe interrelationship between the scale factors obtained using Pulay's from the anharmonic and the harmonized vibrational frequencies of a light molecule and its heavy analogue is considered in terms of a Morse potential. The determination of the scale factors from the vibrational frequencies of a light molecule is shown to result in smaller deviations of the calculated and experimental vibrational frequencies of its heavy analogue than those of the reverse procedure. In this context the extent to which Dennison's is satisfied is also discussed
The contrastmedium algorithm: Taxonomy induction from noisy knowledge graphswith just a few links
No full textIn this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction
Hypernyms extracted from a large text corpus using Hearst lexical-syntactic patterns
No full text<p><br>
The list of hyponym-hypernym pairs was obtained by applying lexical-syntactic patterns described in Hearst (1992) on the corpus prepared by Panchenko et al. (2016). This corpus is a concatenation of the English Wikipedia (2016 dump), Gigaword, ukWaC and English news corpora from the Leipzig Corpora Collection. The lexical-syntactic patterns proposed by Marti Hearst (1992) and further extended and implemented in the form of FSTs by Panchenko et al. (2012) for extracting (noisy) hyponym-hypernym pairs are as follows -- (i) such NP as NP, NP[,] and/or NP; (ii) NP such as NP, NP[,] and/or NP; (iii) NP, NP [,] or other NP; (iv) NP, NP [,] and other NP; (v) NP, including NP, NP [,] and/or NP; (vi) NP, especially NP, NP [,] and/or NP. Pattern extraction on the corpus yields a list of 27.6 million hyponym-hypernym pairs along with the frequency of their occurrence in the corpus. </p>
Statistics for Applications
No full textDmitry Panchenko of the Massachusetts Institute of Technology is an undergraduate course in Statistics for Applications. The site features lecture notes, a syllabus and assignments. Course topics include hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression and correlation. This is a nice example of a course structure for an applied statistics course
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Enriching frame representations with distributionally induced senses
No full textWe introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora. These features are extracted from distributionally induced sense inventories and subsequently linked to the manually-constructed frame representations to boost the performance of frame disambiguation in context. Since Framester is a frame-based knowledge graph, which enables full-fledged OWL querying and reasoning, our resource paves the way for the development of novel, deeper semantic-aware applications that could benefit from the combination of knowledge from text and complex symbolic representations of events and participants. Together with the resource we also provide the software we developed for the evaluation in the task of Word Frame Disambiguation (WFD)
Linked disambiguated distributional semantic networks
No full textWe present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our count-based distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted by patterns and context clues for disambiguation in context. In contrast to dense vector representations, our resource is human readable and interpretable, and thus can be easily embedded within the Semantic Web ecosystem
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