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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.
We study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often evaluative and/or intensifying and thus of relevance to sentiment analysis; and the existence of a free morpheme counterpart – but not been validated empirically. In experiments on a new data set that we make available, we put these key assumptions from the morphological literature to the test and show that despite the fact that affixoids generate many low-frequency formations, we can classify these as affixoid or non-affixoid instances with a best F1-score of 74%.
In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.
Both for psychology and linguistics, emotion concepts are a continuing challenge for analysis in several respects. In this contribution, we take up the language of emotion as an object of study from several angles. First, we consider how frame semantic analyses of this domain by the FrameNet project have been developing over time, due to theory-internal as well as application-oriented goals, towards ever more fine-grained distinctions and greater within-frame consistency. Second, we compare how FrameNet’s linguistically oriented analysis of lexical items in the emotion domain compares to the analysis by domain experts of the experiences that give rise (directly or indirectly) to the lexical items. And finally, we consider to what extent frame semantic analysis can capture phenomena such as connotation and inference about attitudes, which are important in the field of sentiment analysis and opinion mining, even if they do not involve the direct evocation of emotion.
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.
We present an approach for modeling German negation in open-domain fine grained sentiment analysis. Unlike most previous work in sentiment analysis, we assume that negation can be conveyed by many lexical units (and not only common negation words) and that different negation words have different scopes. Our approach is examined on a new dataset comprising sentences with mentions of polar expressions and various negation words. We identify different types of negation words that have the same scopes. We show that already negation modeling based on these types largely outperforms traditional negation models which assume the same scope for all negation words and which employ a window-based scope detection rather than a scope detection based on syntactic information.
This paper provides a formal semantic analysis of past interpretation in Medumba (Grassfields Bantu), a graded tense language. Based on original fieldwork, the study explores the empirical behavior and meaning contribution of graded past morphemes in Medumba and relates these to the account of the phenomenon proposed in Cable (Nat Lang Semant 21:219–276, 2013) for Gĩkũyũ. Investigation reveals that the behavior of Medumba gradedness markers differs from that of their Gĩkũyũ counterparts in meaningful ways and, more broadly, discourages an analysis as presuppositional eventuality or reference time modifiers. Instead, the Medumba markers are most appropriately analyzed as quantificational tenses. It also turns out that Medumba, though belonging to the typological class of graded tense languages, shows intriguing similarities to genuinely tenseless languages in allowing for temporally unmarked sentences and exploiting aspectual and pragmatic cues for reference time resolution. The more general cross-linguistic implication of the study is that the set of languages often subsumed under the label “graded tense” does not in fact form a natural class and that more case-by-case research is needed to refine this category.