no code implementations • SEMEVAL 2018 • Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, Manfred Pinkal
This report summarizes the results of the SemEval 2018 task on machine comprehension using commonsense knowledge.
no code implementations • LREC 2018 • Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, Manfred Pinkal
We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge.
no code implementations • SEMEVAL 2017 • Simon Ostermann, Michael Roth, Stefan Thater, Manfred Pinkal
Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks.
1 code implementation • IJCNLP 2017 • Dai Quoc Nguyen, Dat Quoc Nguyen, Cuong Xuan Chu, Stefan Thater, Manfred Pinkal
This paper presents an approach to the task of predicting an event description from a preceding sentence in a text.
no code implementations • SEMEVAL 2017 • Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal
Our model generalizes the previous works in that it allows to induce different weights of different senses of a word.
no code implementations • WS 2017 • Lilian Wanzare, Aless Zarcone, ra, Stefan Thater, Manfred Pinkal
We present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets (representing event types) and inducing their temporal order.
no code implementations • LREC 2016 • Lena Keiper, Andrea Horbach, Stefan Thater
We present a novel method to automatically improve the accurracy of part-of-speech taggers on learner language.
no code implementations • LREC 2016 • Stefan Ecker, Andrea Horbach, Stefan Thater
We propose an unsupervised system for a variant of cross-lingual lexical substitution (CLLS) to be used in a reading scenario in computer-assisted language learning (CALL), in which single-word translations provided by a dictionary are ranked according to their appropriateness in context.
no code implementations • LREC 2016 • Andrea Horbach, Andrea Hensler, Sabine Krome, Jakob Prange, Werner Scholze-Stubenrecht, Diana Steffen, Stefan Thater, Christian Wellner, Manfred Pinkal
We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts.
no code implementations • LREC 2016 • Lilian D. A. Wanzare, Aless Zarcone, ra, Stefan Thater, Manfred Pinkal
Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing).
no code implementations • TACL 2013 • Michaela Regneri, Marcus Rohrbach, Dominikus Wetzel, Stefan Thater, Bernt Schiele, Manfred Pinkal
Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used.
no code implementations • 1 Jul 2011 • Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, Gerhard Weikum
Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO.
Ranked #14 on Entity Linking on AIDA-CoNLL