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

This pilot study addresses the question of whether the Uniform Information Density principle (UID) can be proved for eight typologically diverse languages. The lexical information of words is derived from dependency structures both in sentences preceding the sentences and within the sentence in which the target word occurs. Dependency structures are a realisation of extra-sentential contexts for deriving information as formulated in the surprisal model. Only subject, object and oblique, i.e., the level directly below the verbal root node, were considered. UID says that in natural language, the variance of information and information jumps from word to word should be small so as not to make the processing of a linguistic message an insurmountable hurdle. We observed cross-linguistically different information distributions but an almost identical UID, which provides evidence for the UID hypothesis and assumes that dependency structures can function as proxies for extra-sentential contexts. However, for the dependency structures chosen as contexts, the information distributions in some languages were not statistically significantly different from distributions from a random corpus. This might be an effect of too low complexity of our model's dependency structures, so lower hierarchical levels (e.g. phrases) should be considered.

BibTex | DOI: 10.5220/0000155600003116

This paper reports the results of a study on automatic keyword extraction in German. We employed in general two types of methods: (A) unsupervised, based on information theory, i.e., (i) a bigram model, (ii) a probabilistic parser model, and (iii) a novel model which considers topics within the discourse of target word for the calculation of their information content, and (B) supervised, employing a recurrent neural network (RNN). As baselines, we employed TextRank and the TF-IDF ranking function. The topic model (A)(iii) outperformed clearly all remaining models, even TextRank and TF-IDF. In contrast, RNN performed poorly. We take the results as first evidence that (i) information content can be employed for keyword extraction tasks and has thus a clear correspondence to semantics of natural language, and (ii) that – as a cognitive principle – the information content of words is determined from extra-sentential contexts, i.e., from the discourse of words.

BibTex | DOI: 10.1007/978-3-030-63787-3_5

This paper reports the results of a study on automatic keyword extraction in German. We employed in general two types of methods: (A) an unsupervised method based on information theory (Shannon, 1948). We employed (i) a bigram model, (ii) a probabilistic parser model (Hale, 2001) and (iii) an innovative model which utilises topics as extra-sentential contexts for the calculation of the information content of the words, and (B) a supervised method employing a recurrent neural network (RNN). As baselines, we employed TextRank and the TF-IDF ranking function. The topic model (A)(iii) outperformed clearly all remaining models, even TextRank and TF-IDF. In contrast, RNN performed poorly. We take the results as first evidence, that (i) information content can be employed for keyword extraction tasks and has thus a clear correspondence to semantics of natural language’s, and (ii) that - as a cognitive principle - the information content of words is determined from extra-sentential contexts, that is to say, from the discourse of words.

BibTex | DOI: 10.5220/0009374704590464