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The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is still ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.
Editorial
(2020)
Journal for language technology and computational linguistics. Special Issue on offensive language
(2020)
Recent years have seen a sharp increase in studies of offensive language (and related notions such as abusive language, hate speech, verbal aggression etc.) as well as of patterns of online behavior such as cyberbullying and trolling. Multiple efforts have been launched for the exploration of computational approaches and the establishment of benchmark datasets for various languages (Basile et al. (2019), Wiegand et al. (2018), Zampieri et al. (2019)).
We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a coarse-grained binary classification task and a fine-grained multi-class classification task. As a novel subtask, we introduce the classification of offensive tweets as explicit or implicit.
The shared task had 13 participating groups submitting 28 runs for the coarse-grained
task, another 28 runs for the fine-grained task, and 17 runs for the implicit-explicit
task.
We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.fzai.h-da.de/iggsa/
We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German. We present an extrinsic evaluation where we use the compound embeddings as input to a neural dependency parser and compare the results to the ones obtained with other types of embeddings. Our evaluation shows that adding compound embeddings yields a significant improvement of 2% LAS over using word embeddings when no POS information is available. When adding POS embeddings to the input, however, the effect levels out. This suggests that it is not the missing information about the semantics of the unknown words that causes problems for parsing German, but the lack of morphological information for unknown words. To augment our evaluation, we also test the new embeddings in a language modelling task that requires both syntactic and semantic information.
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.
Tagset und Richtlinie für das PoSTagging von Sprachdaten aus Genres internetbasierter Kommunikation
(2015)
The development of tools for computer-assisted transcription and analysis of extensive speech corpora is one main issue at the Institute of German Language (IDS) and the Institute of Natural Language Processing (IMS). Corpora of natural spoken dialogue have been transcribed, and the analogue recordings of these discourses are digitized. An automatic segmentation system is employed which is based on Hidden Markov Models. The orthographic representation of the speech signal is transformed into a phonetic representation, the phonetic transcription is transformed into a system-internal representation, and the time alignment between text and speech signal follows. In this article, we also describe the retrieval software Cosmas II and its special features for searching discourse transcripts and playing time aligned passages.