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This replication study aims to investigate a potential bias toward addition in the German language, building upon previous findings of Winter and colleagues who identified a similar bias in English. Our results confirm a bias in word frequencies and binomial expressions, aligning with these previous findings. However, the analysis of distributional semantics based on word vectors did not yield consistent results for German. Furthermore, our study emphasizes the crucial role of selecting appropriate translational equivalents, highlighting the significance of considering language-specific factors when testing for such biases for languages other than English.
A Supervised learning approach for the extraction of opinion sources and targets from German text
(2019)
We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.
The sentiment polarity of a phrase does not only depend on the polarities of its words, but also on how these are affected by their context. Negation words (e.g. not, no, never) can change the polarity of a phrase. Similarly, verbs and other content words can also act as polarity shifters (e.g. fail, deny, alleviate). While individually more sparse, they are far more numerous. Among verbs alone, there are more than 1200 shifters. However, sentiment analysis systems barely consider polarity shifters other than negation words. A major reason for this is the scarcity of lexicons and corpora that provide information on them. We introduce a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet. We provide a fine-grained annotation of individual word senses, as well as information for each verbal shifter on the syntactic scopes that it can affect.
Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably large class of negation content words, also referred to as shifters, such as the verbs diminish, reduce or reverse. However, many of these shifters are ambiguous. For instance, spoil as in spoil your chance reverses the polarity of the positive polar expression chance while in spoil your loved ones, no negation takes place. We present a supervised learning approach to disambiguating verbal shifters. Our approach takes into consideration various features, particularly generalization features.
This paper argues that there is a correlation between functional and purely grammatical patterning in language, yet the nature of this correlation has to be explored. This claim is based on the results of a corpus-driven study of the Slavic aspect, drawing on the socalled Distributional Hypothesis. According to the East-West Theory of the Slavic aspect, there is a broad east-west isogloss dividing the Slavic languages into an eastern group and a western group. There are also two transitional zones in the north and south, which share some properties with each group (Dickey 2000; Barentsen 1998, 2008). The East-West Theory uses concepts of cognitive grammar such as totality and temporal definiteness, and is based on various parameters of aspectual usage in discourse, including contexts such as habituals, general factuals, historical (narrative) present, performatives, sequenced events in the past etc. The purpose of the above-mentioned study is to challenge the semantic approach to the Slavic aspect by comparing the perfective and imperfective verbal aspect on the basis of purely grammatical co-occurrence patterns (see also Janda & Lyashevskaya 2011). The study focused on three Slavic languages: Russian, which, following the East-West Theory, belongs to the eastern group, Czech, which belongs to the western group, and Polish, which is considered as transitional in its aspectual patterning.
Many applications in Natural Language Processing require a semantic analysis of sentences in terms of truth-conditional representations, often with specific desiderata in terms of which information needs to be included in the semantic analysis. However, there are only very few tools that allow such an analysis. We investigate the representations of an automatic analysis pipeline of the C&C parser and Boxer to determine whether Boxer’s analyses in form of Discourse Representation Structure can be successfully converted into a more surface oriented event semantic representation, which will serve as input for a fusion algorithm for fusing hard and soft information. We use a data set of synthetic counter intelligence messages for our investigation. We provide a basic pipeline for conversion and subsequently discuss areas in which ambiguities and differences between the semantic representations present challenges in the conversion process.