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We introduce a system that learns the participants of arbitrary given scripts. This system processes data from web experiments, in which each participant can be realized with different expressions. It computes participants by encoding semantic similarity and global structural information into an Integer Linear Program. An evaluation against a gold standard shows that we significantly outperform two informed baselines.
Semantic argument structures are often incomplete in that core arguments are not locally instantiated. However, many of these implicit arguments can be linked to referents in the wider context. In this paper we explore a number of linguistically motivated strategies for identifying and resolving such null instantiations (NIs). We show that a more sophisticated model for identifying definite NIs can lead to noticeable performance gains over the state-of-the- art for NI resolution.
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.
In the NLP literature, adapting a parser to new text with properties different from the training data is commonly referred to as domain adaptation. In practice, however, the differences between texts from different sources often reflect a mixture of domain and genre properties, and it is by no means clear what impact each of those has on statistical parsing. In this paper, we investigate how differences between articles in a newspaper corpus relate to the concepts of genre and domain and how they influence parsing performance of a transition-based dependency parser. We do this by applying various similarity measures for data point selection and testing their adequacy for creating genre-aware parsing models.
This presentation introduces a new collaborative project: the International Comparable Corpus (ICC) (https://korpus.cz/icc), to be compiled from European national, standard(ised) languages, using the protocols for text categories and their quantities of texts in the International Corpus of English (ICE).
This paper discusses the interaction of Freezing with movement and focus on the basis of subextraction from the pivot of it-cleft sentences. It shows that subextraction is in principle possible, and that it is not sensitive to whether the pivot is related to a derived subject or real object. However, if the context induces an additional contrastive focus on the pivot, extraction is less acceptable. It is suggested that the problem is that two different sets of alternatives need to be construed on the basis of one and the same syntactically marked focus phrase, the pivot. Once the two sets of alternatives are syntactically separated, interpretation is less complex and licit.
Repairs for Reasoning
(2013)
We describe and experimentally investigate phenomena of modal enrichment, that is, phenomena in which a recipient non-literally interprets an utterance by creating and applying a modal operator. We give competing explanations for these phenomena - namely an explanation according to which modal enrichment is a repair procedure for making the utterance match a script of information processing vs. an explanation according to which modal enrichment is triggered by rhetorical structure.
Accentuation, Uncertainty and Exhaustivity - Towards a Model of Pragmatic Focus Interpretation
(2010)
This paper presents a model of pragmatic focus interpretation that is assumed to be part of a complete language comprehension model and that is inspired by Levelt's language processing model. The model is derived from our empirical data on the role of accentuation, prosodic indicators of uncertainty and context for pragmatic focus interpretation. In its present state, the model is restricted to these data, but nevertheless generates predictions.