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“My Curiosity was Satisfied, but not in a Good Way”: Predicting User Ratings for Online Recipes
(2014)
In this paper, we develop an approach to automatically predict user ratings for recipes at Epicurious.com, based on the recipes’ reviews. We investigate two distributional methods for feature selection, Information Gain and Bi-Normal Separation; we also compare distributionally selected features to linguistically motivated features and two types of frameworks: a one-layer system where we aggregate all reviews and predict the rating vs. a two-layer system where ratings of individual reviews are predicted and then aggregated. We obtain our best results by using the two-layer architecture, in combination with 5 000 features selected by Information Gain. This setup reaches an overall accuracy of 65.60%, given an upper bound of 82.57%.
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years.
Prejudice against a social group may lead to discrimination of members of this group. One very strong cue of group membership is a (non)standard accent in speech. Surprisingly, hardly any interventions against accent-based discrimination have been tested. In the current article, we introduce an intervention in which what participants experience themselves unobtrusively changes their evaluations of others. In the present experiment, participants in the experimental condition talked to a confederate in a foreign language before the experiment, whereas those in the control condition received no treatment. Replicating previous research, participants in the control condition discriminated against Turkish-accented job candidates. In contrast, those in the experimental condition evaluated Turkish- and standard-accented candidates as similarly competent. We discuss potential mediating and moderating factors of this effect.
This paper presents challenges and opportunities resulting from the application of geographical information systems (GIS) in the (digital) humanities. First, we provide an overview of the intersection and interaction between geography (and cartography), and the humanities. Second, the “GeoBib” project is used as a case study to exemplify challenges for such collaborative, interdisciplinary projects, both for the humanists and the geoscientists. Finally, we conclude with an outlook on further applications of GIS in the humanities, and the potential scientific benefit for both sides, humanities and geosciences.
Uncertain about Uncertainty: Different ways of processing fuzziness in digital humanities data
(2014)
The GeoBib project is constructing a georeferenced online bibliography of early Holocaust and camp literature published between 1933 and 1949 (Entrup et al. 2013a). Our immediate objectives include identifying the texts of interest in the first place, composing abstracts for them, researching their history, and annotating relevant places and times. Relations between persons, texts, and places will be visualized using digital maps and GIS software as an integral part of the resulting GeoBib information portal. The combination of diverse data from varying sources not only enriches our knowledge of these otherwise mostly forgotten texts; it also confronts us with vague, uncertain or even conflicting information. This situation yields challenges for all researchers involved – historians, literary scholars, geographers and computer scientists alike. While the project operates at the intersection of historical and literary studies, the involved computer scientists are in charge of providing a working environment (Entrup et al. 2013b) and processing the collected information in a way that is formalized yet capable of dealing with inevitable vagueness, uncertainty and contradictions. In this paper we focus on the problems and opportunities of encoding and processing fuzzy data.
Measuring the quality of metadata is only possible by assessing the quality of the underlying schema and the metadata instance. We propose some factors that are measurable automatically for metadata according to the CMD framework, taking into account the variability of schemas that can be defined in this framework. The factors include among others the number of elements, the (re-)use of reusable components, the number of filled in elements. The resulting score can serve as an indicator of the overall quality of the CMD instance, used for feedback to metadata providers or to provide an overview of the overall quality of metadata within a repository. The score is independent of specific schemas and generalizable. An overall assessment of harvested metadata is provided in form of statistical summaries and the distribution, based on a corpus of harvested metadata. The score is implemented in XQuery and can be used in tools, editors and repositories.
This paper describes a first version of an integrated e-dictionary translating possessive constructions from English to Zulu. Zulu possessive constructions are difficult to learn for non-mother tongue speakers. When translating from English into Zulu, a speaker needs to be acquainted with the nominal classification of nouns indicating possession and possessor. Furthermore, (s)he needs to be informed about the morpho-syntactic rules associated with certain combinations of noun classes. Lastly, knowledge of morpho-phonetic changes is also required, because these influence the orthography of the output word forms. Our approach is a novel one in that we combine e-lexicography and natural language processing by developing a (web) interface supporting learners, as well as other users of the dictionary to produce Zulu possessive constructions. The final dictionary that we intend to develop will contain several thousand nouns which users can combine as they wish. It will also translate single words and frequently used multiword expressions, and allow users to test their own translations. On request, information about the morpho-syntactic and morpho-phonetic rules applied by the system are displayed together with the translation. Our approach follows the function theory: the dictionary supports users in text production, at the same time fulfilling a cognitive function.
This article presents preliminary results indicating that speakers have a different pitch range when they speak a foreign language compared to the pitch variation that occurs when they speak their native language. To this end, a learner corpus with French and German speakers was analyzed. Results suggest that speakers indeed produce a smaller pitch range in the respective L2. This is true for both groups of native speakers. A possible explanation for this finding is that speakers are less confident in their productions, therefore, they concentrate more on segments and words and subsequently refrain from realizing pitch range more native-like. For language teaching, the results suggest that learners should be trained extensively on the more pronounced use of pitch in the foreign language.
This paper presents the first release of the KiezDeutsch Korpus (KiDKo), a new language resource with multiparty spoken dialogues of Kiezdeutsch, a newly emerging language variety spoken by adolescents from multi-ethnic urban areas in Germany. The first release of the corpus includes the transcriptions of the data as well as a normalisation layer and part-of-speech annotations. In the paper, we describe the main features of the new resource and then focus on automatic POS tagging of informal spoken language. Our tagger achieves an accuracy of nearly 97% on KiDKo. While we did not succeed in further improving the tagger using ensemble tagging, we present our approach to using the tagger ensembles for identifying error patterns in the automatically tagged data.