Danijela Merkler


Do dependency parsing metrics correlate with human judgments?
Barbara Plank | Héctor Martínez Alonso | Željko Agić | Danijela Merkler | Anders Søgaard
Proceedings of the Nineteenth Conference on Computational Natural Language Learning


Cross-lingual Dependency Parsing of Related Languages with Rich Morphosyntactic Tagsets
Željko Agić | Jörg Tiedemann | Danijela Merkler | Simon Krek | Kaja Dobrovoljc | Sara Može
Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants

Croatian Dependency Treebank 2.0: New Annotation Guidelines for Improved Parsing
Željko Agić | Daša Berović | Danijela Merkler | Marko Tadić
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a new version of the Croatian Dependency Treebank. It constitutes a slight departure from the previously closely observed Prague Dependency Treebank syntactic layer annotation guidelines as we introduce a new subset of syntactic tags on top of the existing tagset. These new tags are used in explicit annotation of subordinate clauses via subordinate conjunctions. Introducing the new annotation to Croatian Dependency Treebank, we also modify head attachment rules addressing subordinate conjunctions and subordinate clause predicates. In an experiment with data-driven dependency parsing, we show that implementing these new annotation guidelines leeds to a statistically significant improvement in parsing accuracy. We also observe a substantial improvement in inter-annotator agreement, facilitating more consistent annotation in further treebank development.

RECSA: Resource for Evaluating Cross-lingual Semantic Annotation
Achim Rettinger | Lei Zhang | Daša Berović | Danijela Merkler | Matea Srebačić | Marko Tadić
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In recent years large repositories of structured knowledge (DBpedia, Freebase, YAGO) have become a valuable resource for language technologies, especially for the automatic aggregation of knowledge from textual data. One essential component of language technologies, which leverage such knowledge bases, is the linking of words or phrases in specific text documents with elements from the knowledge base (KB). We call this semantic annotation. In the same time, initiatives like Wikidata try to make those knowledge bases less language dependent in order to allow cross-lingual or language independent knowledge access. This poses a new challenge to semantic annotation tools which typically are language dependent and link documents in one language to a structured knowledge base grounded in the same language. Ultimately, the goal is to construct cross-lingual semantic annotation tools that can link words or phrases in one language to a structured knowledge database in any other language or to a language independent representation. To support this line of research we developed what we believe could serve as a gold standard Resource for Evaluating Cross-lingual Semantic Annotation (RECSA). We compiled a hand-annotated parallel corpus of 300 news articles in three languages with cross-lingual semantic groundings to the English Wikipedia and DBPedia. We hope that this new language resource, which is freely available, will help to establish a standard test set and methodology to comparatively evaluate cross-lingual semantic annotation technologies.


Lemmatization and Morphosyntactic Tagging of Croatian and Serbian
Željko Agić | Nikola Ljubešić | Danijela Merkler
Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing

Parsing Croatian and Serbian by Using Croatian Dependency Treebanks
Željko Agić | Danijela Merkler | Daša Berović
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages


Rule-Based Sentiment Analysis in Narrow Domain: Detecting Sentiment in Daily Horoscopes Using Sentiscope
Zeljko Agic | Danijela Merkler
Proceedings of the 2nd Workshop on Sentiment Analysis where AI meets Psychology