31,281 research outputs found

    On nonprimary selectional restrictions

    Get PDF
    This paper argues for non-primary c- and s-selectional restrictions of verbs in computing nonprimary predicatives such as resultatives, depictives, and manners. Our discussion is based both on the selection violations in the presence of nonprimary predicates and on the cross-linguistic and language-internal variations of categorial and semantic constraints on nonprimary predicates. We claim that all types of thematic predication are represented by an extended projection, and that the merger of lexical heads with another element, regardless of the type of the element, consistently has c- and s-selectional restrictions

    The Composite Nature of Interlanguage as a Developing System

    Get PDF
    This paper explores the nature of interlanguage (IL) as a developing system with a focus on the abstract lexical structure underlying IL construction. The developing system of IL is assumed to be ‘composite’ in that in second language acquisition (SLA) several linguistic systems are in contact, each of which may contribute different amounts to the developing system. The lexical structure is assumed to be ‘abstract’ in that the mental lexicon contains abstract elements called ‘lemmas’, which contain information about individual lexemes, and lemmas in the bilingual mental lexicon are language-specific and are in contact in IL production. Based on the research findings, it concludes that language transfer in IL production should be understood as lemma transfer of the learner's first language (L1) lexical structure at three abstract levels: lexical-conceptual structure, predicate-argument structure, and morphological realization patterns, and IL construction is driven by an incompletely acquired abstract lexical structure of a target language (TL) item

    Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

    Full text link
    Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.Comment: Accepted at ACL 202

    Functional versus lexical: a cognitive dichotomy

    Get PDF

    On the syntax of external possession in Korean

    Get PDF

    The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations

    Full text link
    The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.Comment: To appear at EACL 201
    • 

    corecore