XLike Project Language Analysis Services

Carreras, Xavier; Padró, Lluís; Zhang, Lei; Rettinger, Achim; Li, Zhixing; García-Cuesta, Esteban; Agić, Željko; Bekavac, Božo; Fortuna, Blaž; Štajner, Tadej

doi:10.3115/v1/e14-2003

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…In this paper, we demonstrate X-LiSA, an infrastructure for cross-lingual semantic annotation developed within the XLike project. The services of X-LiSA have been widely used as components of the XLike pipeline architecture [2] for enabling cross-lingual processing for publishers, media monitoring and new business intelligence applications.…”

Section: Resultsmentioning

confidence: 99%

X-LiSA

Zhang

Rettinger

2014

Proc. VLDB Endow.

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The ever-increasing quantities of structured knowledge on the Web and the impending need of multilinguality and cross-linguality for information access pose new challenges but at the same time open up new opportunities for knowledge extraction research. In this regard, cross-lingual semantic annotation has emerged as a topic of major interest and it is essential to build tools that can link words and phrases in unstructured text in one language to resources in structured knowledge bases in any other language. In this paper, we demonstrate X-LiSA, an infrastructure for cross-lingual semantic annotation, which supports both service-oriented and user-oriented interfaces for annotating text documents and web pages in different languages using resources from Wikipedia and Linked Open Data (LOD).

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Section: Resultsmentioning

confidence: 99%

X-LiSA

Zhang

Rettinger

2014

Proc. VLDB Endow.

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“…Regarding the work for Spanish, ref. [10] presented an extension of the Freeling Spanish analyzer [11], FreelingMed, by extending the resources of Freeling with medical dictionaries and SNOMED-CT. Others works, such as [12,13], have focused on crosslingual approaches in order to take advantage of the English resources and make different projections into Spanish. However, both works have Oracle terms as their starting point.…”

Section: Introductionmentioning

confidence: 99%

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

Villaplana,

Martínez,

Montalvo

2023

Electronics

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Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results.

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“…We used the UKB (Agirre and Soroa, 2009) and IMS ( (Zhong and Ng, 2010) to score the WordNet synsets for each predicate. Next, we take the highest scoring synsets and use the Predicate Matrix (Carreras et al, 2014) to obtain the associated FrameNet frames. The interpretation of the predicates as events for the task is thus derived from the SRL output in combination with the WSD output and the Predicate Matrix association.…”

Section: The Newsreader Systemmentioning

confidence: 99%

NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs

Vossen

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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In this paper, we describe the participation of the NewsReader system in the SemEval-2018 Task 5 on Counting Events and Participants in the Long Tail. NewsReader is a generic unsupervised text processing system that detects events with participants, time and place to generate Event Centric Knowledge Graphs (ECKGs). We minimally adapted these ECKGs to establish a baseline performance for the task. We first use the ECKGs to establish which documents report on the same incident and what event mentions are coreferential. Next, we aggregate ECKGs across coreferential mentions and use the aggregated knowledge to answer the questions of the task. Our participation tests the quality of NewsReader to create ECKGs, as well as the potential of ECKGs to establish event identity and reason over the result to answer the task queries.

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XLike Project Language Analysis Services

Cited by 10 publications

References 7 publications

X-LiSA

X-LiSA

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs

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