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We describe a minimalist approach to shallow discourse parsing in the context of the CoNLL 2015 Shared Task.1 Our parser integrates a rule-based component for argument identification and datadriven models for the classification of explicit and implicit relations. We place special emphasis on the evaluation of implicit sense labeling, we present different(More)
We describe our contribution to the CoNLL 2016 Shared Task on shallow discourse parsing.1 Our system extends the two best parsers from previous year’s competition by integration of a novel implicit sense labeling component. It is grounded on a highly generic, language-independent feedforward neural network architecture incorporating weighted word embeddings(More)
Gold annotations for supervised implicit semantic role labeling are extremely sparse and costly. As a lightweight alternative, this paper describes an approach based on unsupervised parsing which can do without iSRL-specific training data: We induce prototypical roles from large amounts of explicit SRL annotations paired with their distributed word(More)
We induce semantic association networks from translation relations in parallel corpora. The resulting semantic spaces are encoded in a single reference language, which ensures cross-language comparability. As our main contribution, we cluster the obtained (crosslingually comparable) lexical semantic spaces. We find that, in our sample of languages, lexical(More)
We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also(More)
This paper describes a novel approach to find evidence for implicit semantic roles. Our data-driven models generalize over large amounts of explicit annotations only, in order to acquire information about implicit roles. We establish a generic background knowledge base of probablistic predicate-role co-occurrences in an unsupervised manner, and estimate(More)
We propose a generic, memory-based approach for the detection of implicit semantic roles. While state-of-the-art methods for this task combine hand-crafted rules with specialized and costly lexical resources, our models use large corpora with automated annotations for explicit semantic roles only to capture the distribution of predicates and their(More)
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