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In this paper, we present our Named Entity Recognition (NER) system for German – NERU (Named Entity Rules), which heavily relies on handcrafted rules as well as information gained from a cascade of existing external NER tools. The system combines large gazetteer lists, information obtained by comparison of different automatic translations and POS taggers. With NERU, we were able to achieve a score of 73.26% on the development set provided by the GermEval 2014 Named Entity Recognition Shared Task for German.
This paper presents a Named Entity Recognition system for German based on Conditional Random Fields. The model also includes language-independant features and features computed form large coverage lexical resources. Along side the results themselves, we show that by adding linguistic resources to a probabilistic model, the results improve significantly.
In the latest decades, machine learning approaches have been intensively experimented for natural language processing. Most of the time, systems rely on using statistics within the system, by analyzing texts at the token level and, for labelling tasks, categorizing each among possible classes. One may notice that previous symbolic approaches (e.g. transducers) where designed to delimit pieces of text. Our research team developped mXS, a system that aims at combining both approaches. It locates boundaries of entities by using sequential pattern mining and machine learning. This system, intially developped for French, has been adapted to German.
In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples.
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.
MoSTNER is a German NER system based on machine learning with log-linear models and morphology-aware features. We use morphological analysis with Morphisto for generating features, moreover we use German Wikipedia as a gazetteer and perform punctuation-aware and morphology-aware page title matching. We use four types of factor graphs where NER labels are single variables or split into prefix (BILOU) and type (PER, LOC, etc.) variables. Our system supports nested NER (two levels), for training we use SampleRank, for prediction Iterated Conditional Modes, the implementation is based on Python and Factorie.
Collobert et al. (2011) showed that deep neural network architectures achieve state- of-the-art performance in many fundamental NLP tasks, including Named Entity Recognition (NER). However, results were only reported for English. This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER. Our system achieves an F1 -measure of 75.09% according to the official metric.
Modular Classifier Ensemble Architecture for Named Entity Recognition on Low Resource Systems
(2014)
This paper presents the best performing Named Entity Recognition system in the GermEval 2014 Shared Task. Our approach combines semi-automatically created lexical resources with an ensemble of binary classifiers which extract the most likely tag sequence. Out-of-vocabulary words are tackled with semantic generalization extracted from a large corpus and an ensemble of part-of-speech taggers, one of which is unsupervised. Unknown candidate sequences are resolved using a look-up with the Wikipedia API.
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems.
Ironic speech act detection is indispensable for automatic opinion mining. This paper presents a pattern-based approach for the detection of ironic speech acts in German Web comments. The approach is based on a multilevel annotation model. Based on a gold standard corpus with labeled ironic sentences, multilevel patterns are deter- mined according to statistical and linguis- tic analysis. The extracted patterns serve to detect ironic speech acts in a Web com- ment test corpus. Automatic detection and inter-annotator results achieved by human annotators show that the detection of ironic sentences is a challenging task. However, we show that it is possible to automatically detect ironic sentences with relatively high precision up to 63%.
