7236920
MindNet: An Automatically-Created Lexical Resource
We will demonstrate MindNet, a lexical resource built automatically by processing text. We will present two forms of MindNet: as a static lexical resource, and, as a toolkit which allows MindNets to be built from arbitrary text. We will also introduce a web-based interface to MindNet lexicons (MNEX) that is intended to make the data contained within MindNets more accessible for exploration. Both English and Japanese MindNets will be shown and will be made available, through MNEX, for research purposes.
16286271
Learning to Predict Case Markers in Japanese
Japanese case markers, which indicate the grammatical relation of the complement NP to the predicate, often pose challenges to the generation of Japanese text, be it done by a foreign language learner, or by a machine translation (MT) system. In this paper, we describe the task of predicting Japanese case markers and propose machine learning methods for solving it in two settings: (i) monolingual, when given information only from the Japanese sentence; and (ii) bilingual, when also given information from a corresponding English source sentence in an MT context. We formulate the task after the well-studied task of English semantic role labelling, and explore features from a syntactic dependency structure of the sentence. For the monolingual task, we evaluated our models on the Kyoto Corpus and achieved over 84% accuracy in assigning correct case markers for each phrase. For the bilingual task, we achieved an accuracy of 92% per phrase using a bilingual dataset from a technical domain. We show that in both settings, features that exploit dependency information, whether derived from gold-standard annotations or automatically assigned, contribute significantly to the prediction of case markers.
38309831
An NC Parallel Algorithm for Edge-Coloring Series-Parallel Multigraphs
Many combinatorial problems can be efficiently solved in parallel for series?parallel multigraphs. The edge-coloring problem is one of a few combinatorial problems for which no NC parallel algorithm has been obtained for series?parallel multigraphs. This paper gives an NC parallel algorithm for the problem on series?parallel multigraphsG. It takesO(logn) time withO(?n/logn) processors, wherenis the number of vertices and ? is the maximum degree ofG.