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Post-specialisation: Retrofitting vectors of words unseen in lexical resources
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82 |
Discriminating between lexico-semantic relations with the specialization tensor model
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83 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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85 |
A resource-light method for cross-lingual semantic textual similarity
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90 |
University of Mannheim @ CLSciSumm-17: Citation-Based Summarization of Scientific Articles Using Semantic Textual Similarity
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92 |
Improving neural knowledge base completion with cross-lingual projections
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93 |
Leveraging event-based semantics for automated text simplification
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94 |
Two layers of annotation for representing event mentions in news stories
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95 |
If sentences could see: Investigating visual information for semantic textual similarity
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96 |
Dual tensor model for detecting asymmetric lexico-semantic relations
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98 |
Unsupervised text segmentation using semantic relatedness graphs
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Abstract:
Segmenting text into semantically coherent fragments improves readability of text and facilitates tasks like text summarization and passage retrieval. In this paper, we present a novel unsupervised algorithm for linear text segmentation (TS) that exploits word embeddings and a measure of semantic relatedness of short texts to construct a semantic relatedness graph of the document. Semantically coherent segments are then derived from maximal cliques of the relatedness graph. The algorithm performs competitively on a standard synthetic dataset and outperforms the best-performing method on a real-world (i.e., non-artificial) dataset of political manifestos.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/41341/1/S16-2016.pdf https://madoc.bib.uni-mannheim.de/41341/ https://madoc.bib.uni-mannheim.de/41341
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99 |
Spanish NER with word representations and conditional random fields
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