Andi Rexha


Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
Roman Kern | Stefan Falk | Andi Rexha
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them with one of the three categories: Task, Process, and Material. These scientific publications are selected from Computer Science, Material Sciences, and Physics domains. We followed a supervised approach for both subtasks by using a sequential classifier (CRF - Conditional Random Fields). For generating our solution we used a web-based application implemented in the EU-funded research project, named CODE. Our system achieved an F1 score of 0.39 for the Subtask 1 and 0.28 for the Subtask 2.


Identifying Referenced Text in Scientific Publications by Summarisation and Classification Techniques
Stefan Klampfl | Andi Rexha | Roman Kern
Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)

Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
Stefan Falk | Andi Rexha | Roman Kern
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


Modeling, Managing, Exposing, and Linking Ontologies with a Wiki-based Tool
Mauro Dragoni | Alessio Bosca | Matteo Casu | Andi Rexha
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In the last decade, the need of having effective and useful tools for the creation and the management of linguistic resources significantly increased. One of the main reasons is the necessity of building linguistic resources (LRs) that, besides the goal of expressing effectively the domain that users want to model, may be exploited in several ways. In this paper we present a wiki-based collaborative tool for modeling ontologies, and more in general any kind of linguistic resources, called MoKi. This tool has been customized in the context of an EU-funded project for addressing three important aspects of LRs modeling: (i) the exposure of the created LRs, (ii) for providing features for linking the created resources to external ones, and (iii) for producing multilingual LRs in a safe manner.