The aim of this study is to identify idiomatic expressions in English using the measure perplexity. The assumption is that idiomatic expressions cause higher perplexity than literal expressions given a reference text. Perplexity in our study is calculated based on n-grams of (i) PoS tags, (ii) tokens, and (iii) thematic roles within the boundaries of a sentence. In the setting of our study, we observed that no perplexity in the contexts of (i), (ii) and (iii) manages to distinguish idiomatic expressions from literals. We postulate that larger, extra-sentential contexts should be used for the determination of perplexity. In addition, the number of thematic roles in (iii) should be reduced to a smaller number of basic roles in order to avaiod an uniform distribution of n-grams.

BibTex | DOI: 10.3233/SSW230006

This study focuses on the identification of English Idiomatic Expressions (IE) using an information theoretic model. In the focus are verb-noun constructions only. We notice significant differences in semantic surprisal and information density between IE-data and literals-data. Surprisingly, surprisal and information density in the IE-data and in a large reference data set do not differ significantly, while, in contrast, we observe significant differences between literals and a large reference data set.

Poster | BibTex | https://aclanthology.org/2023.konvens-main.15/

This paper describes a study on keyword extraction in German with a model that utilises Shannon information as a lexical feature. Lexical information content was derived from large, extra-sentential semantic contexts of words in the framework of the novel Topic Context Model. We observed that lexical information content increased the performance of a Recurrent Neural Network in keyword extraction, outperforming TexTRank and other two models, i.e., Named Entity Recognition and Latent Dirichlet Allocation used comparatively in this study.

BibTex | DOI: 10.1007/978-3-031-10464-0_53

This study investigates three challenges for developing machine learning-based self-service web apps for consumers. First, we argue that user research must accompany the development of ML-based products so that they better serve users’ needs at all stages of development. Second, we discuss the data sourcing dilemma in developing consumer-oriented ML-based apps and propose a way to solve it by implementing an interaction design that balances the workload between users and computers according to the ML component’s performance. To dynamically define the role of the user-in-the-loop, we monitor user success and ML performance over time. Finally, we propose a lightweight typology of ML-based systems to assess the generalizability of our findings to other ML use cases.

Our case study uses a newly developed web application that allows consumers to analyze their heating bills for potential energy and cost savings. Based on domain-specific data values extracted from user-provided document images, an assessment of potential savings is derived and reported back to the user.

BibTex | DOI: 10.1007/978-3-031-05434-1_1

The starting point of this paper is the observation that methods based on the direct match of keywords are inadequate because they do not consider the cognitive ability of concept formation and abstraction. We argue that keyword evaluation needs to be based on a semantic model of language capturing the semantic relatedness of words to satisfy the claim of the human-like ability of concept formation and abstraction and achieve better evaluation results. Evaluation of keywords is difficult since semantic informedness is required for this purpose. This model must be capable of identifying semantic relationships such as synonymy, hypernymy, hyponymy, and location-based abstraction. For example, when gathering texts from online sources, one usually finds a few keywords with each text. Still, these keyword sets are neither complete for the text nor are they in themselves closed, i.e., in most cases, the keywords are a random subset of all possible keywords and not that informative w.r.t. the complete keyword set. Therefore all algorithms based on this cannot achieve good evaluation results and provide good/better keywords or even a complete keyword set for a text. As a solution, we propose a word graph that captures all these semantic relationships for a given language. The problem with the hyponym/hyperonym relationship is that, unlike synonyms, it is not bidirectional. Thus the space of keyword sets requires a metric that is non-symmetric, in other words, a quasi-metric. We sketch such a metric that works on our graph. Since it is nearly impossible to obtain such a complete word graph for a language, we propose for the keyword task a simpler graph based on the base text upon which the keyword sets should be evaluated. This reduction is usually sufficient for evaluating keyword sets.

BibTex | DOI: 10.3389/frai.2022.801564