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The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English ...
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Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve ...
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The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English ...
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Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve ...
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Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News ...
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Verbose, Laconic or Just Right: A Simple Computational Model of Content Appropriateness under Length Constraints
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Prosodic cues for emotion: analysis with discrete characterization of intonation
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In: Speech Prosody (2014)
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Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations
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Action Unit Models of Facial Expression of Emotion in the Presence of Speech
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Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering
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Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries
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General Versus Specific Sentences: Automatic Identification and Application to Analysis of News Summaries
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In: Technical Reports (CIS) (2011)
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Abstract:
In this paper, we introduce the task of identifying general and specific sentences in news articles. Instead of embarking on a new annotation effort to obtain data for the task, we explore the possibility of leveraging existing large corpora annotated with discourse information to train a classifier. We introduce several classes of features that capture lexical and syntactic information, as well as word specificity and polarity. We then use the classifier to analyze the distribution of general and specific sentences in human and machine summaries of news articles. We discover that while all types of summaries tend to be more specific than the original documents, human abstracts contain a more balanced mix of general and specific sentences but automatic summaries are overwhelmingly specific. Our findings give strong evidence for the need for a new task in (abstractive) summarization: identification and generation of general sentences.
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URL: https://repository.upenn.edu/cis_reports/951 https://repository.upenn.edu/cgi/viewcontent.cgi?article=1997&context=cis_reports
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Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries
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In: Departmental Papers (CIS) (2011)
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