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The paper presents the process of developing the AirFrame database, a specialized lexical resource in which aviation terminology is defined in the form of semantic frames, following the methodology of the Berkeley FrameNet (FN). First, the structure of the database is presented, and then the methodology applied in developing and populating the database is described. The link between specialized aviation frames and general language semantic frames, of which frames defining entities, processes, attributes and events are particularly relevant, is discussed on the example of the semantic frame of Flight and its related frames. The paper ends with discussing possibilities of using AirFrame as a model for further developing resources in which general and specialized knowledge are linked.
This paper discusses an investigation of how senses are ordered across eight dictionaries. A dataset of 75 words was used for this purpose, and two senses were examined for each word. The words are divided into three groups of 25 words each according to the relationship between the senses: Homonymy, Metaphor, and Systematic Polysemy. The primary finding is that WordNet differs from the other dictionaries in terms of Metaphor. The order of the senses was more often figurative/literal, and it had the highest percentage of figurative senses that were not found. We discuss leveraging another dictionary, COBUILD, to re-order the senses according to frequency.
Adieu, Fremdwort!
(1991)
Das Vokabular von Songtexten im gesellschaftlichen Kontext – ein diachron-empirischer Beitrag
(2022)
Der Beitrag untersucht den Stellenwert gesellschaftlich relevanter Thematiken in deutschsprachigen Songtexten der zurückliegenden fünf Jahrzehnte. Dabei zeigt sich, dass neben individuellen Befindlichkeiten auch politische, sozialkritische oder umweltbezogene Themen signifikant angesprochen werden. Wir kontrastieren Songtexte mit anderen Testsorten und wenden dabei quantitative Methoden auf umfangreiche, breit stratifizierte Datensamples an, um die Phänomenbeschreibungen präzisierbar, generalisierbar und reproduzierbar zu machen. Das longitudinale Korpusdesign bietet Potenzial für diachrone Vergleiche. Im Sinne eines erweiterten „Mixed Methods“-Ansatzes exploriert die Studie zudem ausgewählte Aspekte qualitativ und bettet sie in den zeitlichen Kontext ein.
Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.
This paper deals with different views of lexical semantics. The focus is on the relationship between lexical expressions and conceptual components. First the assumptions about lexicalization and decompositionality of concepts shared by the most semanticists are presented, followed by a discussion of the differences between two-level-semantics and one-level-semantics. The final part is concentrated on the interpretation of conceptual components in situations of communication.
The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like no, not or without, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e. g. abandoned in “abandoned hope” or alleviate in “alleviate pain”. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. Recoup shifts negative to positive in “recoup your losses”, but does not affect the positive polarity of fortune in “recoup a fortune”. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.