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20172019
most citedA Mixture Model for Learning Multi-Sense Word Embeddings

12 citations · 22 across the 3 of their papers we have counts for

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Showing 2017 · cs.CLShow all

5 papers · 2 filters

cs.CL2017

Aligning Script Events with Narrative Texts

Simon Ostermann, Michael Roth, Stefan Thater +1

Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich a…

cs.CL2017★ 10 cited

Sequence to Sequence Learning for Event Prediction

Dai Quoc Nguyen, Dat Quoc Nguyen, Cuong Xuan Chu +2

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidi…

cs.CL2017★ 12 cited

A Mixture Model for Learning Multi-Sense Word Embeddings

Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi +2

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses…

cs.CL2017

InScript: Narrative texts annotated with script information

Ashutosh Modi, Tatjana Anikina, Simon Ostermann +1

This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and no…

cs.CL2017

Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

Ashutosh Modi, Ivan Titov, Vera Demberg +2

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in h…