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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)
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.