Open Access

Semantic role labeling is traditionally viewed as a sentence-level task concerned with identifying semantic arguments that are overtly realized in a fairly local context (i.e., a clause or sentence). However, this local view potentially misses important information that can only be recovered if local argument structures are linked across sentence boundaries. One important link concerns semantic arguments that remain locally unrealized (null instantiations) but can be inferred from the context. In this paper, we report on the SemEval 2010 Task-10 on ‘‘Linking Events and Their Participants in Discourse’’, that addressed this problem. We discuss the corpus that was created for this task, which contains annotations on multiple levels: predicate argument structure (FrameNet and PropBank), null instantiations, and coreference. We also provide an analysis of the task and its difficulties.

Authors like Fillmore 1986 and Goldberg 2006 have made a strong case for regarding argument omission in English as a lexical and construction-based affordance rather than one based on general semantico-pragmatic constraints. They do not, however, address the question of how grammatical restrictions on null complementation might interact with broader narrative conventions, in particular those of genre. In this paper, we attempt to remedy this oversight by presenting a comprehensive overview of genre-based argument omissions and offering a construction-based analysis of genre-based omission conventions. We consider five genre-based omission types: instructional imperatives (Culy 1996, Bender 1999), labelese, diary style (Haegeman 1990), match reports (Ruppenhofer 2004) and quotative clauses. We show that these omission types share important traits; all, for example, have anaphoric rather than indefinite construals. We also show, however, that the omission types differ from each other in idiosyncratic ways. We then address several interrelated representational problems posed by the grammatical treatment of genre-based omissions. For example, the constructions that represent genre-based omission conventions must interact with the lexical entries of verbs, many of which do not generally permit omitted arguments. Accordingly, we offer constructional analyses of genre-based omissions that allow constructions to override lexical valence constraints.

This paper shows that the phenomenon of plesionymy deserves greater attention and needs to be approached outside its traditional framework, which considered it to be a subtype of synonymy (Cruse, 1986, 2002; Croft and Cruse, 2004). This view suggested that pairs of terms such as foggy–misty, fearless–brave exhibit significant shared semantic traits that are more salient than their differences. Differing properties were considered to be subordinate. These are sometimes contextually foregrounded resulting in occasional oppositeness. Corpus studies show that this view is a broad generalization. This study sheds new light on German plesionyms by employing a corpus-linguistic approach. In particular, terms designating gradable properties (e.g. kritisch–ernst ‘critical–serious’, sauber–rein ‘clean–unsoiled/immaculate’) at neighboring positions of gradable scales show variable behavior and do not show a stronger affinity for synonymy. The position taken is that a relation of synonymy and contrast are equally a matter of construal. Both types of semantic relations are part of the conceptual and lexical knowledge and subject to a cognitive principle. This work also examines how plesionym relations are realized in discourse. This article demonstrates that plesionyms are co-occurrences within typical lexico-syntactic sequences. Following Jones’ (2002) and Murphy’s (2006) observations, these patterns (e.g. nicht X, eher Y; mehr X als Y; etc.) have specific discourse functions and are evidence to account for a construction-based view.

Corpora with high-quality linguistic annotations are an essential component in many NLP applications and a valuable resource for linguistic research. For obtaining these annotations, a large amount of manual effort is needed, making the creation of these resources time-consuming and costly. One attempt to speed up the annotation process is to use supervised machine-learning systems to automatically assign (possibly erroneous) labels to the data and ask human annotators to correct them where necessary. However, it is not clear to what extent these automatic pre-annotations are successful in reducing human annotation effort, and what impact they have on the quality of the resulting resource. In this article, we present the results of an experiment in which we assess the usefulness of partial semi-automatic annotation for frame labeling. We investigate the impact of automatic pre-annotation of differing quality on annotation time, consistency and accuracy. While we found no conclusive evidence that it can speed up human annotation, we found that automatic pre-annotation does increase its overall quality.