Hagen Fürstenau

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Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective disambiguation, by harnessing context from knowledge bases and using a new form of coherence graph. It unifies prior(More)
We present a syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It employs a systematic combination of firstand second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previous work on a paraphrase(More)
We present a model that represents word meaning in context by vectors which are modified according to the words in the target’s syntactic context. Contextualization of a vector is realized by reweighting its components, based on distributional information about the context words. Evaluation on a paraphrase ranking task derived from the SemEval 2007 Lexical(More)
Large-scale annotated corpora are a prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. The key(More)
We propose two general and robust methods for enriching resources annotated in the Frame Semantic paradigm with syntactic dependency graphs, which can provide useful additional information for applications such as semantic role labeling methods. One method incorporates information of a dependency parser, while the other one assumes the resource to be based(More)
We present a method for learning syntaxsemantics mappings for verbs from unannotated corpora. We learn linkings, i.e., mappings from the syntactic arguments and adjuncts of a verb to its semantic roles. By learning such linkings, we do not need to model individual semantic roles independently of one another, and we can exploit the relation between different(More)