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Abstract Meaning Representation for Sembanking
TLDR
A sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation, like the Penn Treebank encouraged work on statistical parsing. Expand
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
TLDR
A tagset is developed, data is annotated, features are developed, and results nearing 90% accuracy are reported on the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. Expand
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
TLDR
This work systematically evaluates the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy on Twitter and achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks. Expand
Frame-Semantic Parsing
TLDR
A two-stage statistical model that takes lexical targets in their sentential contexts and predicts frame-semantic structures and results in qualitatively better structures than naïve local predictors, which outperforms the prior state of the art by significant margins. Expand
A Dependency Parser for Tweets
TLDR
A new dependency parser for English tweets, TWEEBOPARSER, which builds on several contributions: new syntactic annotations for a corpus of tweets, with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Expand
Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut
TLDR
A novel representation, evaluation measure, and supervised models are presented for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation, enabling efficient sequence tagging algorithms for feature-rich discriminative models. Expand
Probabilistic Frame-Semantic Parsing
TLDR
An implemented parser that transforms an English sentence into a frame-semantic representation and uses two feature-based, discriminative probabilistic models to permit disambiguation of new predicate words is described. Expand
SEMAFOR: Frame Argument Resolution with Log-Linear Models
TLDR
The SEMAFOR system's performance in the SemEval 2010 task on linking events and their participants in discourse is described, showing it fares the best of the submitted systems with respect to recall and F1. Expand
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