Topic
Valency
About: Valency is a research topic. Over the lifetime, 1632 publications have been published within this topic receiving 26141 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the crystal structure of the addition compound N2O4-1,4-dioxane was studied and the interatomic distances and valency angles of the N 2O4 molecule closely correspond to those of the free molecule as determined by electron-diffraction, but are rather different from those reported for the solid oxide.
Abstract: IN a recently published work on the crystal structure of the addition compound N2O4-1,4-dioxane1 the interatomic distances and valency angles of the N2O4 molecule closely correspond to those of the free molecule as determined by electron-diffraction2, but are rather different from those reported for the solid oxide3 (Table 1).
8 citations
TL;DR: In this article, a comparison of the optical properties such as optical absorption band edge, ordinary and extraordinary refractive indices, powder second harmonic generation and the non-linear coefficient, d33, of congruent LiNbO3 crystals doped with di-, tri- and hexa-valent cations is reported.
8 citations
19 Mar 2012
8 citations
17 Sep 2007
TL;DR: This thesis investigates the potential of statistical disambiguation of verb senses and compares the Näıve Bayes classifier, decision trees, rule-based method, maximum entropy, and support vector machines.
Abstract: Semantic analysis has become a bottleneck of many natural language applications. Machine translation, automatic question answering, dialog management, and others rely on high quality semantic analysis. Verbs are central elements of clauses with strong influence on the realization of whole sentences. Therefore the semantic analysis of verbs plays a key role in the analysis of natural language. We believe that solid disambiguation of verb senses can boost the performance of many real-life applications. In this thesis, we investigate the potential of statistical disambiguation of verb senses. Each verb occurrence can be described by diverse types of information. We investigate which information is worth considering when determining the sense of verbs. Different types of classification methods are tested with regard to the topic. In particular, we compared the Näıve Bayes classifier, decision trees, rule-based method, maximum entropy, and support vector machines. The proposed methods are thoroughly evaluated on two different Czech corpora, VALEVAL and the Prague Dependency Treebank. Significant improvement over the baseline is observed.
8 citations