Search Results for author: Manfred Pinkal

Found 32 papers, 1 papers with code

MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants

no code implementations SEMEVAL 2019 Simon Ostermann, Michael Roth, Manfred Pinkal

Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge.

Reading Comprehension

Grounding Semantic Roles in Images

no code implementations EMNLP 2018 Carina Silberer, Manfred Pinkal

We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation.

Image Captioning Question Answering +1

MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script 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.

Natural Language Understanding Reading Comprehension

Aligning Script Events with Narrative Texts

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.

Sequence to Sequence Learning for Event Prediction

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.

Sentence

A Mixture Model for Learning Multi-Sense Word Embeddings

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.

Word Embeddings

Inducing Script Structure from Crowdsourced Event Descriptions via Semi-Supervised Clustering

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.

Clustering Question Answering +1

A Crowdsourced Database of Event Sequence Descriptions for the Acquisition of High-quality Script Knowledge

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).

Clustering

Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data

no code implementations23 Feb 2015 Marcus Rohrbach, Anna Rohrbach, Michaela Regneri, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele

To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts.

Activity Recognition

Grounding Action Descriptions in Videos

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.

Semantic Textual Similarity Video Understanding

Robust Disambiguation of Named Entities in Text

no code implementations1 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.

Entity Disambiguation Entity Linking

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