Search Results for author: Philipp Cimiano

Found 44 papers, 6 papers with code

Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction

no code implementations BioNLP (ACL) 2022 Christian Witte, Philipp Cimiano

We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT).

PICO Slot Filling

Predicting independent living outcomes from written reports of social workers

no code implementations EMNLP (NLP+CSS) 2020 Angelika Maier, Philipp Cimiano

To support this task, we present an approach that extracts indications of independence on different life aspects from the day-to-day documentation that social workers create.

Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation

1 code implementation EMNLP (ArgMining) 2021 Juri Opitz, Philipp Heinisch, Philipp Wiesenbach, Philipp Cimiano, Anette Frank

When assessing the similarity of arguments, researchers typically use approaches that do not provide interpretable evidence or justifications for their ratings.

Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension

no code implementations EMNLP (spnlp) 2020 Hendrik ter Horst, Philipp Cimiano

We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties.

Reading Comprehension Structured Prediction

BiQuAD: Towards QA based on deeper text understanding

no code implementations Joint Conference on Lexical and Computational Semantics 2021 Frank Grimm, Philipp Cimiano

Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question.

Question Answering Reading Comprehension

Terme-\`a-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data

no code implementations LREC 2020 Maria Pia di Buono, Philipp Cimiano, Mohammad Fazleh Elahi, Frank Grimm

While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.

Recent Developments for the Linguistic Linked Open Data Infrastructure

no code implementations LREC 2020 Thierry Declerck, John Philip McCrae, Matthias Hartung, Jorge Gracia, Christian Chiarcos, Elena Montiel-Ponsoda, Philipp Cimiano, Artem Revenko, Roser Saur{\'\i}, Deirdre Lee, Stefania Racioppa, Jamal Abdul Nasir, Matthias Orlikowsk, Marta Lanau-Coronas, Christian F{\"a}th, Mariano Rico, Mohammad Fazleh Elahi, Maria Khvalchik, Meritxell Gonzalez, Katharine Cooney

In this paper we describe the contributions made by the European H2020 project {``}Pr{\^e}t-{\`a}-LLOD{''} ({`}Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors{'}) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure.

Zero-Shot Cross-Lingual Opinion Target Extraction

no code implementations NAACL 2019 Soufian Jebbara, Philipp Cimiano

In this work, we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions.

Aspect-Based Sentiment Analysis Multilingual Word Embeddings

Evaluating Architectural Choices for Deep Learning Approaches for Question Answering over Knowledge Bases

no code implementations6 Dec 2018 Sherzod Hakimov, Soufian Jebbara, Philipp Cimiano

We address the task of answering simple questions, consisting in predicting the subject and predicate of a triple given a question.

Question Answering

Learning Diachronic Analogies to Analyze Concept Change

1 code implementation COLING 2018 Matthias Orlikowski, Matthias Hartung, Philipp Cimiano

We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts.

Word Embeddings

AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data

1 code implementation26 Feb 2018 Sherzod Hakimov, Soufian Jebbara, Philipp Cimiano

We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference.

Knowledge Base Question Answering Machine Translation +1

Predicting Disease-Gene Associations using Cross-Document Graph-based Features

no code implementations26 Sep 2017 Hendrik ter Horst, Matthias Hartung, Roman Klinger, Matthias Zwick, Philipp Cimiano

In the context of personalized medicine, text mining methods pose an interesting option for identifying disease-gene associations, as they can be used to generate novel links between diseases and genes which may complement knowledge from structured databases.

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

no code implementations19 Sep 2017 Soufian Jebbara, Philipp Cimiano

We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis.

Aspect-Based Sentiment Analysis Term Extraction

Improving Opinion-Target Extraction with Character-Level Word Embeddings

no code implementations WS 2017 Soufian Jebbara, Philipp Cimiano

In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions.

Aspect-Based Sentiment Analysis Word Embeddings

Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture

1 code implementation19 Sep 2017 Soufian Jebbara, Philipp Cimiano

We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms.

Relation Extraction Sentiment Analysis +1

Crowdsourcing Ontology Lexicons

no code implementations LREC 2016 Bettina Lanser, Christina Unger, Philipp Cimiano

In order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be verbalized in natural language.


The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud

no code implementations LREC 2016 John Philip McCrae, Christian Chiarcos, Francis Bond, Philipp Cimiano, Thierry Declerck, Gerard de Melo, Jorge Gracia, Sebastian Hellmann, Bettina Klimek, Steven Moran, Petya Osenova, Antonio Pareja-Lora, Jonathan Pool

The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections.

Natural Language Processing

The USAGE review corpus for fine grained multi lingual opinion analysis

no code implementations LREC 2014 Roman Klinger, Philipp Cimiano

Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases.

Opinion Mining Sentiment Analysis

Collaborative semantic editing of linked data lexica

no code implementations LREC 2012 John McCrae, Elena Montiel-Ponsoda, Philipp Cimiano

The creation of language resources is a time-consuming process requiring the efforts of many people.

Cannot find the paper you are looking for? You can Submit a new open access paper.