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- Computerlinguistik (4)
- NER (4)
- Named entity recognition (4)
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This paper presents the TWEETDICT system prototype, which uses co-occurrence and frequency distributions of Twitter hashtags to generate clusters of keywords that could be used for topic summarization/identification. They also contain mentions referring to the same entity, which is a valuable resource for coreference resolution. We provide a web interface to the co-occurrence counts where an interactive search through the dataset collected from Twitter can be started. Additionally, the used data is also made freely available.
In this paper we present Nessy (Named Entity Searching System) and its application to German in the context of the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). We tackle the challenge by using a combination of machine learning (Naive Bayes classification) and rule-based methods. Altogether, Nessy achieves an F-score of 58.78% on the final test set.
This paper presents the BECREATIVE Named Entity Recognition system and its participation at the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). BECREATIVE uses a hybrid approach of two commonly used procedural methods, namely list-based lookups and machine learning (Naive Bayes Classification), which centers around the classifier. BECREATIVE currently reaches an F-score of 37.34 on the strict evaluation setting applied on the development set provided by GermEval.
This paper describes the DRIM Named Entity Recognizer (DRIM), developed for the GermEval 2014 Named Entity (NE) Recognition Shared Task. The shared task did not pose any restrictions regarding the type of named entity recognition (NER) system submissions and usage of external data, which still resulted in a very challenging task. We employ Linear Support Vector Classification (Linear SVC) in the implementation of SckiKit, with variety of features, gazetteers and further contextual information of the target words. As there is only one level of embedding in the dataset, two separate classifiers are trained for the outer and inner spans. The system was developed and tested on the dataset provided by the GermEval 2014 NER Shared Task. The overall strict (fine-grained) score is 70.94% on the development set, and 69.33% on the final test set which is quite promising for the German language.
This paper describes our classification and rule-based attempt at nested Named Entity Recognition for German. We explain how both approaches interact with each other and the resources we used to achieve our results. Finally, we evaluate the overall performance of our system which achieves an F-score of 52.65% on the development set and 52.11% on the final test set of the GermEval 2014 Shared Task.
