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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In this paper, we first introduce a new architecture for parsing, bidirectional incremental parsing. We propose a novel algorithm for incremental construction, which can be applied to many structure learning problems ...
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In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signa...
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Minimum-error-rate training (MERT) is a bottleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably optimize. Building on the work of Watanabe e...
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This task was meant to compare the results of two different retrieval techniques: the first one was based on the words found in documents and query texts;the second one was based on the senses (concepts) obtained by d...
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This task was meant to compare the results of two different retrieval techniques: the first one was based on the words found in documents and query texts;the second one was based on the senses (concepts) obtained by disambiguating the words in documents and queries. The underlying goal was to come up with a more precise knowledge about the possible improvements brought by word sense disambiguation (WSD) in the information retrieval process. The proposed task structure was interesting in that it drew up a clear separation between the actors (humans or computers): those who provide the corpus, those who disambiguate it, and those who query it. Thus it was possible to test the universality and the interoperability of the methods and algorithms involved.
We attempt programming spatial algorithms in naturallanguage. The input of the proposed system is a naturallanguage description of a spatial processing algorithm, and the output is the object-oriented program code t...
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The accuracy of current word sense disambiguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD examples provided through OntoNotes, we ...
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