Philipp Cimiano

Also published as: P. Cimiano


2021

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Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation
Juri Opitz | Philipp Heinisch | Philipp Wiesenbach | Philipp Cimiano | Anette Frank
Proceedings of the 8th Workshop on Argument Mining

When assessing the similarity of arguments, researchers typically use approaches that do not provide interpretable evidence or justifications for their ratings. Hence, the features that determine argument similarity remain elusive. We address this issue by introducing novel argument similarity metrics that aim at high performance and explainability. We show that Abstract Meaning Representation (AMR) graphs can be useful for representing arguments, and that novel AMR graph metrics can offer explanations for argument similarity ratings. We start from the hypothesis that similar premises often lead to similar conclusions—and extend an approach for AMR-based argument similarity rating by estimating, in addition, the similarity of conclusions that we automatically infer from the arguments used as premises. We show that AMR similarity metrics make argument similarity judgements more interpretable and may even support argument quality judgements. Our approach provides significant performance improvements over strong baselines in a fully unsupervised setting. Finally, we make first steps to address the problem of reference-less evaluation of argumentative conclusion generations.

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Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
Milad Alshomary | Timon Gurcke | Shahbaz Syed | Philipp Heinisch | Maximilian Spliethöver | Philipp Cimiano | Martin Potthast | Henning Wachsmuth
Proceedings of the 8th Workshop on Argument Mining

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

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BiQuAD: Towards QA based on deeper text understanding
Frank Grimm | Philipp Cimiano
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4,190 closed-domain texts and a total of 99,149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-the-art models achieve F1-scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.

2020

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Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension
Hendrik ter Horst | Philipp Cimiano
Proceedings of the Fourth Workshop on Structured Prediction for NLP

Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question. Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text. In this work, we focus on the challenging sub-problem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model. 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. We approach this task with probabilistic graphical models computing the maximum-a-posteriori instance of the semantic model. Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling. We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.

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Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data
Maria Pia di Buono | Philipp Cimiano | Mohammad Fazleh Elahi | Frank Grimm
Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)

In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-‘a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. 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.

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Predicting independent living outcomes from written reports of social workers
Angelika Maier | Philipp Cimiano
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

In social care environments, the main goal of social workers is to foster independent living by their clients. An important task is thus to monitor progress towards reaching independence in different areas of their patients’ life. 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. We describe the process of collecting and annotating a corresponding corpus created from data records of two social work institutions with a focus on disability care. We show that the agreement on the task of annotating the observations of social workers with respect to discrete independent levels yields a high agreement of .74 as measured by Fleiss’ Kappa. We present a classification approach towards automatically classifying an observation into the discrete independence levels and present results for different types of classifiers. Against our original expectation, we show that we reach F-Measures (macro) of 95% averaged across topics, showing that this task can be automatically solved.

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Recent Developments for the Linguistic Linked Open Data Infrastructure
Thierry Declerck | John Philip McCrae | Matthias Hartung | Jorge Gracia | Christian Chiarcos | Elena Montiel-Ponsoda | Philipp Cimiano | Artem Revenko | Roser Saurí | Deirdre Lee | Stefania Racioppa | Jamal Abdul Nasir | Matthias Orlikowsk | Marta Lanau-Coronas | Christian Fäth | Mariano Rico | Mohammad Fazleh Elahi | Maria Khvalchik | Meritxell Gonzalez | Katharine Cooney
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper we describe the contributions made by the European H2020 project “Prêt-à-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. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

2019

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Extending Neural Question Answering with Linguistic Input Features
Fabian Hommel | Philipp Cimiano | Matthias Orlikowski | Matthias Hartung
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)

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Zero-Shot Cross-Lingual Opinion Target Extraction
Soufian Jebbara | Philipp Cimiano
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Aspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains. 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. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language. Depending on the source and target language pairs, we reach performances in a zero-shot regime of up to 77% of a model trained on target language data. Furthermore, we can increase this performance up to 87% of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages.

2018

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SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling
Matthias Hartung | Hendrik ter Horst | Frank Grimm | Tim Diekmann | Roman Klinger | Philipp Cimiano
Proceedings of ACL 2018, System Demonstrations

Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.

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Learning Diachronic Analogies to Analyze Concept Change
Matthias Orlikowski | Matthias Hartung | Philipp Cimiano
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. 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. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.

2017

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Improving Opinion-Target Extraction with Character-Level Word Embeddings
Soufian Jebbara | Philipp Cimiano
Proceedings of the First Workshop on Subword and Character Level Models in NLP

Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system’s performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.

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Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases
Matthias Hartung | Fabian Kaupmann | Soufian Jebbara | Philipp Cimiano
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count-based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.

2016

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The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud
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
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.

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How to Address Smart Homes with a Social Robot? A Multi-modal Corpus of User Interactions with an Intelligent Environment
Patrick Holthaus | Christian Leichsenring | Jasmin Bernotat | Viktor Richter | Marian Pohling | Birte Carlmeyer | Norman Köster | Sebastian Meyer zu Borgsen | René Zorn | Birte Schiffhauer | Kai Frederic Engelmann | Florian Lier | Simon Schulz | Philipp Cimiano | Friederike Eyssel | Thomas Hermann | Franz Kummert | David Schlangen | Sven Wachsmuth | Petra Wagner | Britta Wrede | Sebastian Wrede
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.

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Crowdsourcing Ontology Lexicons
Bettina Lanser | Christina Unger | Philipp Cimiano
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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. One way to provide this kind of information are ontology lexicons, which apart from the actual verbalizations in a given target language can provide further, rich linguistic information about them. Compiling such lexicons manually is a very time-consuming task and requires expertise both in Semantic Web technologies and lexicon engineering, as well as a very good knowledge of the target language at hand. In this paper we present an alternative approach to generating ontology lexicons by means of crowdsourcing: We use CrowdFlower to generate a small Japanese ontology lexicon for ten exemplary ontology elements from the DBpedia ontology according to a two-stage workflow, the main underlying idea of which is to turn the task of generating lexicon entries into a translation task; the starting point of this translation task is a manually created English lexicon for DBpedia. Comparison of the results to a manually created Japanese lexicon shows that the presented workflow is a viable option if an English seed lexicon is already available.

2015

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Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis
Roman Klinger | Philipp Cimiano
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Semantic parsing of speech using grammars learned with weak supervision
Judith Gaspers | Philipp Cimiano | Britta Wrede
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications
Christian Chiarcos | John Philip McCrae | Petya Osenova | Philipp Cimiano | Nancy Ide
Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications

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Reconciling Heterogeneous Descriptions of Language Resources
John Philip McCrae | Philipp Cimiano | Victor Rodríguez Doncel | Daniel Vila-Suero | Jorge Gracia | Luca Matteis | Roberto Navigli | Andrejs Abele | Gabriela Vulcu | Paul Buitelaar
Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications

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Linking Four Heterogeneous Language Resources as Linked Data
Benjamin Siemoneit | John Philip McCrae | Philipp Cimiano
Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications

2014

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A multimodal corpus for the evaluation of computational models for (grounded) language acquisition
Judith Gaspers | Maximilian Panzner | Andre Lemme | Philipp Cimiano | Katharina J. Rohlfing | Sebastian Wrede
Proceedings of the 5th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)

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An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews
Konstantin Buschmeier | Philipp Cimiano | Roman Klinger
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach
Matthias Hartung | Roman Klinger | Matthias Zwick | Philipp Cimiano
Proceedings of BioNLP 2014

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Modelling the Semantics of Adjectives in the Ontology-Lexicon Interface
John P. McCrae | Francesca Quattri | Christina Unger | Philipp Cimiano
Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)

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Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments
Benjamin Paassen | Andreas Stöckel | Raphael Dickfelder | Jan Philip Göpfert | Nicole Brazda | Tarek Kirchhoffer | Hans Werner Müller | Roman Klinger | Matthias Hartung | Philipp Cimiano
Proceedings of the Third Workshop on Semantic Web and Information Extraction

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Bielefeld SC: Orthonormal Topic Modelling for Grammar Induction
John Philip McCrae | Philipp Cimiano
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0
Maud Ehrmann | Francesco Cecconi | Daniele Vannella | John Philip McCrae | Philipp Cimiano | Roberto Navigli
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Recent years have witnessed a surge in the amount of semantic information published on the Web. Indeed, the Web of Data, a subset of the Semantic Web, has been increasing steadily in both volume and variety, transforming the Web into a ‘global database’ in which resources are linked across sites. Linguistic fields -- in a broad sense -- have not been left behind, and we observe a similar trend with the growth of linguistic data collections on the so-called ‘Linguistic Linked Open Data (LLOD) cloud’. While both Semantic Web and Natural Language Processing communities can obviously take advantage of this growing and distributed linguistic knowledge base, they are today faced with a new challenge, i.e., that of facilitating multilingual access to the Web of data. In this paper we present the publication of BabelNet 2.0, a wide-coverage multilingual encyclopedic dictionary and ontology, as Linked Data. The conversion made use of lemon, a lexicon model for ontologies particularly well-suited for this enterprise. The result is an interlinked multilingual (lexical) resource which can not only be accessed on the LOD, but also be used to enrich existing datasets with linguistic information, or to support the process of mapping datasets across languages.

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The USAGE review corpus for fine grained multi lingual opinion analysis
Roman Klinger | Philipp Cimiano
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Opinion mining has received wide attention in recent years. Models for this task are typically trained or evaluated with a manually annotated dataset. However, fine-grained annotation of sentiments including information about aspects and their evaluation is very labour-intensive. The data available so far is limited. 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. It provides information on segments in the text which denote an aspect or a subjective evaluative phrase which refers to the aspect. Relations and coreferences are explicitly annotated. This dataset contains 622 English and 611 German reviews, allowing to investigate how to port sentiment analysis systems across languages and domains. We describe the methodology how the corpus was created and provide statistics including inter-annotator agreement. We further provide figures for a baseline system and results for German and English as well as in a cross-domain setting. The results are encouraging in that they show that aspects and phrases can be extracted robustly without the need of tuning to a particular type of products.

2013

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Orthonormal Explicit Topic Analysis for Cross-Lingual Document Matching
John Philip McCrae | Philipp Cimiano | Roman Klinger
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Bi-directional Inter-dependencies of Subjective Expressions and Targets and their Value for a Joint Model
Roman Klinger | Philipp Cimiano
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Exploiting Ontology Lexica for Generating Natural Language Texts from RDF Data
Philipp Cimiano | Janna Lüker | David Nagel | Christina Unger
Proceedings of the 14th European Workshop on Natural Language Generation

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Ontology Lexicalization as a core task in a language-enhanced Semantic Web
Philipp Cimiano
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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Mining translations from the web of open linked data
John Philip McCrae | Philipp Cimiano
Proceedings of the Joint Workshop on NLP&LOD and SWAIE: Semantic Web, Linked Open Data and Information Extraction

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Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data
Christian Chiarcos | Philipp Cimiano | Thierry Declerck | John P. McCrae
Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data

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Linguistic Linked Open Data (LLOD). Introduction and Overview
Christian Chiarcos | Philipp Cimiano | Thierry Declerck | John P. McCrae
Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data

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Releasing multimodal data as Linguistic Linked Open Data: An experience report
Peter Menke | John Philip McCrae | Philipp Cimiano
Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data

2012

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Collaborative semantic editing of linked data lexica
John McCrae | Elena Montiel-Ponsoda | Philipp Cimiano
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The creation of language resources is a time-consuming process requiring the efforts of many people. The use of resources collaboratively created by non-linguistists can potentially ameliorate this situation. However, such resources often contain more errors compared to resources created by experts. For the particular case of lexica, we analyse the case of Wiktionary, a resource created along wiki principles and argue that through the use of a principled lexicon model, namely Lemon, the resulting data could be better understandable to machines. We then present a platform called Lemon Source that supports the creation of linked lexical data along the Lemon model. This tool builds on the concept of a semantic wiki to enable collaborative editing of the resources by many users concurrently. In this paper, we describe the model, the tool and present an evaluation of its usability based on a small group of users.

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Up from Limited Dialog Systems!
Giuseppe Riccardi | Philipp Cimiano | Alexandros Potamianos | Christina Unger
NAACL-HLT Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012)

2011

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Combining statistical and semantic approaches to the translation of ontologies and taxonomies
John McCrae | Mauricio Espinoza | Elena Montiel-Ponsoda | Guadalupe Aguado-de-Cea | Philipp Cimiano
Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Representing and resolving ambiguities in ontology-based question answering
Christina Unger | Philipp Cimiano
Proceedings of the TextInfer 2011 Workshop on Textual Entailment

2010

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Generating LTAG grammars from a lexicon/ontology interface
Christina Unger | Felix Hieber | Philipp Cimiano
Proceedings of the 10th International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+10)

2009

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Flexible Semantic Composition with DUDES (short paper)
Philipp Cimiano
Proceedings of the Eight International Conference on Computational Semantics

2007

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Automatic Acquisition of Ranked Qualia Structures from the Web
Philipp Cimiano | Johanna Wenderoth
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Paul Buitelaar | Philipp Cimiano | Berenike Loos
Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge

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Ingredients of a first-order account of bridging
Philipp Cimiano
Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)

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Ontology-based Information Extraction with SOBA
Paul Buitelaar | Philipp Cimiano | Stefania Racioppa | Melanie Siegel
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper we describe SOBA, a sub-component of the SmartWeb multi-modal dialog system. SOBA is a component for ontologybased information extraction from soccer web pages for automatic population of a knowledge base that can be used for domainspecific question answering. SOBA realizes a tight connection between the ontology, knowledge base and the information extraction component. The originality of SOBA is in the fact that it extracts information from heterogeneous sources such as tabular structures, text and image captions in a semantically integrated way. In particular, it stores extracted information in a knowledge base, and in turn uses the knowledge base to interpret and link newly extracted information with respect to already existing entities.

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Finding the Appropriate Generalization Level for Binary Ontological Relations Extracted from the Genia Corpus
P. Cimiano | M. Hartung | E. Ratsch
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Recent work has aimed at discovering ontological relations from text corpora. Most approaches are based on the assumption that verbs typically indicate semantic relations between concepts. However, the problem of finding the appropriate generalization level for the verb's arguments with respect to a given taxonomy has not received much attention in the ontology learning community. In this paper, we address the issue of determining the appropriate level of abstraction for binary relations extracted from a corpus with respect to a given concept hierarchy. For this purpose, we reuse techniques from the subcategorization and selectional restrictions acquisition communities. The contribution of our work lies in the systematic analysis of three different measures. We conduct our experiments on the Genia corpus and the Genia ontology and evaluate the different measures by comparing the results of our approach with a gold standard provided by one of the authors, a biologist.

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Generating and Visualizing a Soccer Knowledge Base
Paul Buitelaar | Thomas Eigner | Greg Gul-rajani | Alexander Schutz | Melanie Siegel | Nicolas Weber | Philipp Cimiano | Günter Ladwig | Matthias Mantel | Honggang Zhu
Demonstrations

2005

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Automatically Learning Qualia Structures from the Web
Philipp Cimiano | Johanna Wenderoth
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

2004

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Clustering Concept Hierarchies from Text
Philipp Cimiano | Andreas Hotho | Steffen Staab
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2003

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Ontology-based Linguistic Annotation
Philipp Cimiano | Siegfried Handschuh
Proceedings of the ACL 2003 Workshop on Linguistic Annotation: Getting the Model Right

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