Semantic Tagging for the Penn Treebank

Sense Tagging the Penn Treebank

This paper describes the methodology that is being used to augment the Penn Treebank annotation with sense tags and other types of semantic information. Inspired by the results of SENSEVAL, and the high inter-annotator agreement that was achieved there, similar methods were used for a pilot study of 5000 words of running text from the Penn Treebank. Using the same techniques of allowing the ann...

Parsing the Penn Chinese Treebank with Semantic Knowledge

We build a class-based selection preference sub-model to incorporate external semantic knowledge from two Chinese electronic semantic dictionaries. This sub-model is combined with modifier-head generation sub-model. After being optimized on the held out data by the EM algorithm, our improved parser achieves 79.4% (F1 measure), as well as a 4.4% relative decrease in error rate on the Penn Chines...

Proposition Bank II: Delving Deeper

The PropBank project is creating a corpus of text annotated with information about basic semantic propositions. PropBank I (Kingsbury & Palmer, 2002) added a layer of predicateargument information, or semantic roles, to the syntactic structures of the English Penn Treebank. This paper presents an overview of the second phase of PropBank Annotation, PropBank II, which is being applied to English...

Part-of-Speech Tagging Guidelines for the Penn Treebank Project

Using Part-of-Speech Reranking to Improve Chinese Word Segmentation

Chinese word segmentation and Part-ofSpeech (POS) tagging have been commonly considered as two separated tasks. In this paper, we present a system that performs Chinese word segmentation and POS tagging simultaneously. We train a segmenter and a tagger model separately based on linear-chain Conditional Random Fields (CRF), using lexical, morphological and semantic features. We propose an approx...