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SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages ...
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Enriching Frame Representations with Distributionally Induced Senses ...
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A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
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Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models
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Abstract:
Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing. Acknowledging the argumentative nature of scientific text, in this work we investigate the link between the argumentative structure of scientific publications and rhetorical aspects such as discourse categories or citation contexts. To this end, we (1) augment a corpus of scientific publications annotated with four layers of rhetoric annotations with argumentation annotations and (2) investigate neural multi-task learning architectures combining argument extraction with a set of rhetorical classification tasks. By coupling rhetorical classifiers with the extraction of argumentative components in a joint multi-task learning setting, we obtain significant performance gains for different rhetorical analysis tasks.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/46086/ https://madoc.bib.uni-mannheim.de/46086/1/emnlp-18-multi%20%2831%29.pdf https://madoc.bib.uni-mannheim.de/46086
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Automatic detection of uncertain statements in the financial domain
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UniMa at SemEval-2018 Task 7 : semantic relation extraction and classification from scientific publications
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An unsupervised word sense disambiguation system for under-resourced languages
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A resource-light method for cross-lingual semantic textual similarity
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Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation ...
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