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Improvements in Part-of-Speech Tagging with an Application to German
This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive process of manually tagging part-of-speech content in a variety of languages. Expand
Efficient Higher-Order CRFs for Morphological Tagging
This work presents an approximated conditional random field using coarse-to-fine decoding and early updating that yields fast and accurate morphological taggers across six languages with different morphological properties and that across languages higher-order models give significant improvements over 1- order models. Expand
SMOR: A German Computational Morphology Covering Derivation, Composition and Inflection
A morphological analyser for German inflection and word formation implemented in finite state technology that can account for productive word formation like derivation and composition is presented. Expand
Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging
A HMM part-of-speech tagging method which is particularly suited for POS tagsets with a large number of fine-grained tags based on splitting of the POS tags into attribute vectors and decomposition of the contextual POS probabilities of the HMM into a product of attribute probabilities. Expand
Part-of-Speech Tagging With Neural Networks
It is shown that the Net-Tagger performs as well as the trigram-based tagger and better than the HMM-tagger. Expand
Efficient Parsing of Highly Ambiguous Context-Free Grammars with Bit Vectors
An efficient bit-vector-based CKY-style parser for context-free parsing is presented. The parser computes a compact parse forest representation of the complete set of possible analyses for largeExpand
A Joint Sequence Translation Model with Integrated Reordering
A novel machine translation model which models translation by a linear sequence of operations which includes not only translation but also reordering operations, and a joint sequence model for the translation and reordering probabilities which is more flexible than standard phrase-based MT. Expand
LoPar: Design and Implementation
Combining EM Training and the MDL Principle for an Automatic Verb Classification Incorporating Selectional Preferences
This paper presents an innovative, complex approach to semantic verb classification that relies on selectional preferences as verb properties, trained by a combination of the EM algorithm and the MDL principle, providing soft clusters with two dimensions. Expand
Trace Prediction and Recovery with Unlexicalized PCFGs and Slash Features
A parser which generates parse trees with empty elements in which traces and fillers are co-indexed and which outperformed other unlexicalized PCFG parsers in terms of labeled bracketing f-score. Expand