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Preface
(2015)
In recent years, theoretical and computational linguistics has paid much attention to linguistic items that form scales. In NLP, much research has focused on ordering adjectives by intensity (tiny < small). Here, we address the task of automatically ordering English adverbs by their intensifying or diminishing effect on adjectives (e.g. extremely small < very small). We experiment with 4 different methods: 1) using the association strength between adverbs and adjectives; 2) exploiting scalar patterns (such as not only X but Y); 3) using the metadata of product reviews; 4) clustering. The method that performs best is based on the use of metadata and ranks adverbs by their scaling factor relative to unmodified adjectives.
Based on specific linguistic landmarks in the speech signal, this study investigates pitch level and pitch span differences in English, German, Bulgarian and Polish. The analysis is based on 22 speakers per language (11 males and 11 females). Linear mixed models were computed that include various linguistic measures of pitch level and span, revealing characteristic differences across languages and between language groups. Pitch level appeared to have significantly higher values for the female speakers in the Slavic than the Germanic group. The male speakers showed slightly different results, with only the Polish speakers displaying significantly higher mean values for pitch level than the German males. Overall, the results show that the Slavic speakers tend to have a wider pitch span than the German speakers. But for the linguistic measure, namely for span between the initial peaks and the non-prominent valleys, we only find the difference between Polish and German speakers. We found a flatter intonation contour in German than in Polish, Bulgarian and English male and female speakers and differences in the frequency of the landmarks between languages. Concerning “speaker liveliness” we found that the speakers from the Slavic group are significantly livelier than the speakers from the Germanic group.
Centering on German self-motion verbs, this paper demonstrates the advantages of free-sorting over creating and delineating word fields with more traditional methods. In particular, I draw a comparison to Snell-Hornby’s (1983) work on German descriptive verbs, which produces lexical fields with the help of dictionary entries, a thesaurus, a small corpus of written text and limited speaker feedback. While these methods have benefits, they are limited in their ability to represent the average organization of semantic fields in the mind of everyday speakers. Freesorting, by contrast, does not rely on academic resources, corpora or singular speaker judgments. In sorting, a group of informants creates visible sets of items according to perceived similarity. Psycholinguists have used the method to quantitatively explore the perception of color terms across cultures (c.f. Roberson et al. 2005). With a sufficiently large number of informants, one can generate lexical sorting data that is apt for cluster analysis, the results of which are represented by dendrograms. The experiment I conducted involved 33 school children from a middle class neighborhood in Braunschweig, Northern Germany. My experiment shows that Snell-Hornby’s (1983) representation of the self-motion field can be improved by integrating further dimensions of meaning, such as body-space relations and sound, that young speakers find salient in the grouping procedure.
We investigate whether non-configurational languages, which display more word order variation than configurational ones, require more training data for a phenomenon to be parsed successfully. We perform a tightly controlled study comparing the dative alternation for English (a configurational language), German, and Russian (both non-configurational). More specifically, we compare the performance of a dependency parser when only canonical word order is present with its performance on data sets when all word orders are present. Our results show that for all languages, canonical data not only is easier to parse, but there exists no direct correspondence between the size of training sets containing free(er) word order variation and performance.