This paper explores the research issue and methodology of a query focused multidocument summarizer. Considering its possible application area is Web, the computation is clearly divided into offline and online tasks. A...
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graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of beam-sea...
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Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of bootstrapping has the same root as the topic dri...
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The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relationships in the documents, under the assumption that all the sentences...
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We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined ...
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This paper presents a graph-theoretic model of the acquisition of lexical syntactic representations. The representations the model learns are non-categorical or graded. We propose a new evaluation methodology of synta...
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We present a graph-based semi-supervised label propagation algorithm for acquiring open-domain labeled classes and their instances from a combination of unstructured and structured text sources. This acquisition metho...
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The proceedings contain 114 papers. The topics discussed include: revealing the structure of medical dictations with conditional random fields;modeling annotators: a generative approach to learning from annotator rati...
The proceedings contain 114 papers. The topics discussed include: revealing the structure of medical dictations with conditional random fields;modeling annotators: a generative approach to learning from annotator rationales;dependency-based semantic role labeling of PropBank;maximum entropy based rule selection model for syntax-based statistical machine translation;indirect-HMM-based hypothesis alignment for combining outputs from machine translation systems;adding redundant features for CRFs-based sentence sentiment classification;ranking reader emotions using pairwise loss minimization and emotional distribution regression;sentence fusion via dependency graph compression;revisiting readability: a unified framework for predicting text quality;online large-margin training of syntactic and structural translation features;and incorporating temporal and semantic information with eye gaze for automatic word acquisition in multimodal conversational systems.
Defining all words in a Japanese dictionary by using a limited number of words (defining vocabulary) is helpful for Japanese children and second-language learners of Japanese. Although some English dictionaries have t...
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We present a novel unsupervised sentence fusion method which we apply to a corpus of biographies in German. Given a group of related sentences, we align their dependency trees and build a dependency graph. Using integ...
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