Abstract In this paper, we explore the feasibility of irony detection in Dutch social media. To end, investigate both transformer models with embedding representations, as well traditional machine learning classifiers extensive feature sets. Our feature-based methodology implements a variety information sources including lexical, semantic, syntactic, sentiment features, two new data-driven feat...

We introduce the task of identifying information-dense texts, which report important factual information in direct, succinct manner. We describe a procedure that allows us to label automatically a large training corpus of New York Times texts. We train a classifier based on lexical, discourse and unlexicalized syntactic features and test its performance on a set of manually annotated articles f...

Lexical cues are linguistic expressions that can signal the presence of a rhetorical relation. However, such cues can be ambiguous as they may signal more than one relation or may not always function as a relation indicator. In this study, we first conduct a corpus-based analysis to derive a set of n-grams as potential lexical cues. These cues are then utilized in graph-based probabilistic mode...

We investigate the problem of reading level assessment for German texts on a newly compiled corpus of freely available easy and difficult articles, targeted at adult and child readers respectively. We adapt a wide range of syntactic, lexical and language model features from previous research on English and combined them with new features that make use of the rich morphology of German. We show t...

This dissertation concerns the morphophonological alternation of lexical items, determined by syntactic position. It considers three types of inflection, positional allomorphy and certain examples of phenomena collectively called "contraction", and argues that the notion of lexical insertion into d-structure, underlying many current syntactic theories, has difficulty in accounting for these cas...

A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities. We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surf...

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these ...