7176707
Multi-Document Summarization by Maximizing Informative Content-Words
We show that a simple procedure based on maximizing the number of informative content-words can produce some of the best reported results for multi-document summarization. We first assign a score to each term in the document cluster, using only frequency and position information, and then we find the set of sentences in the document cluster that maximizes the sum of these scores, subject to length constraints. Our overall results are the best reported on the DUC-2004 summarization task for the ROUGE-1 score, and are the best, but not statistically significantly different from the best system in MSE-2005. Our system is also substantially simpler than the previous best system.
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.
11423197
Unsupervised Learning of Dependency Structure for Language Modeling
This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency. Second, we present an unsupervised learning method that discovers the dependency structure of a sentence using a bootstrapping procedure. Finally, we evaluate the proposed models on a realistic application (Japanese Kana-Kanji conversion). Experiments show that the best DLM achieves an 11.3% error rate reduction over the word trigram model.
1253703
An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity
This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion. We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In particular, we attempt to interpret the results given in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain measured using the information-theoretic notion of cross entropy. We show that such a metric correlates well with the CER performance of the adaptation methods, and also show that the discriminative methods are not only superior to a MAP-based method in terms of achieving larger CER reduction, but are also more robust against the similarity of background and adaptation domains.
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.
1345080
Using a Broad-Coverage Parser for Word-Breaking in Japanese
We describe a method of word segmentation in Japanese in which a broad-coverage parser selects the best word sequence while producing a syntactic analysis. This technique is substantially different from traditional statistics- or heuristics-based models which attempt to select the best word sequence before handing it to the syntactic component. By breaking up the task of finding the best word sequence into the identification of words (in the word-breaking component) and the selection of the best sequence (a by-product of parsing), we have been able to simplify the task of each component and achieve high accuracy over a wide variety of data. Word-breaking accuracy of our system is currently around 97-98%.
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.