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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.
fast, fast, fast ...
(1977)
Sprachverarbeitung mit getypten Attribut-Wert-Matrizen. Dependenzgrammatik und Konzeptuelle Semantik
(1996)
In dieser Arbeit wurden die Dependenzgrammatik und die Konzeptuelle Semantik formalisiert. Als Ausgangspunkt dafür diente eine detaillierte Darstellung der formalen Grundlage. Diese wurden im Kapitel 1 erarbeitet. Nicht alle in diesem Kapitel entwickelten Konzepte wurden in den späteren Kapiteln aufgegriffen. Ich halte es aber für sinnvoll die mathematischen Eigenschaften eines Formalismus ausführlich darzustellen, bevor dieser zur Anwendung gebracht wird. Die beschriebenen Eigenschaften sind dem Formalismus immanent. Auf die Einführung von Erweiterungen, z.B. die Definition von Mengen, wurde verzichtet, da sie im weiteren Verlauf keine Verwendung finden.
Im Kapitel 2 wird gezeigt, dass die Dependenzgrammatik mit dem dargestellten Formalismus beschrieben werden kann. Damit wurde eine Formalisierung erreicht, die zeigt, dass der seltene Einsatz dieser traditionsreichen Grammatiktheorie in der Computerlinguistik, zumindest aus formalen Gründen, nicht gerechtfertigt ist.
Das Kapitel 3 stellt die Konzeptuelle Semantik vor. Die ursprüngliche Formalisierung dieser Theorie wurde kritisiert. Es wurde gezeigt, dass die Beschreibung der Konzepte durch getypte Attribut-Wert-Matrizen eine bessere Alternative der formalen Darstellung ist. Desweiteren wurden einerseits Vereinfachungen (z.B. der Verzicht auf die Dekomposition der Konzepte) und andererseits Erweiterungen (d.h. insbesondere eine Erweiterung des Inventars der ontologischen Kategorien) vorgeschlagen.
Nachdem diese beiden linguistischen Theorien mit demselben formalen Apparat dargestellt wurden, wurde im Kapitel 4 dargestellt, dass sie sich ergänzen. In dem skizzierten Sprachverarbeitungssystem werden die syntaktische und die semantische Struktur parallel aufgebaut. Es ist erkennbar, dass sich beide Theorien ergänzen. Es wurde darüber hinaus gezeigt, dass ein solches System eine sehr gut geeignete Basis zur maschinellen Verarbeitung defizitärer sprachlicher Äußerungen bildet.
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.
In this paper, we will present a first attempt to classify commonly confused words in German by consulting their communicative functions in corpora. Although the use of so-called paronyms causes frequent uncertainties due to similarities in spelling, sound and semantics, up until now the phenomenon has attracted little attention either from the perspective of corpus linguistics or from cognitive linguistics. Existing investigations rely on structuralist models, which do not account for empirical evidence. Still, they have developed an elaborate model based on formal criteria, primarily on word formation (cf. Lăzărescu 1999). Looking from a corpus perspective, such classifications are incompatible with language in use and cognitive elements of misuse.
This article sketches first lexicological insights into a classification model as derived from semantic analyses of written communication. Firstly, a brief description of the project will be provided. Secondly, corpus-assisted paronym detection will be focused. Thirdly, in the main section the paper concerns the description of the datasets for paronym classification and the classification procedures. As a work in progress, new insights will continually be extended once spoken and CMC data are added to the investigations.