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Found 30 papers, 1 papers with code
A voting scheme to detect semantic underspecification
The following work describes a voting system to automatically classify the sense selection of the complex types Location/Organization and Container/Content, which depend on regular polysemy, as described by the Generative Lexicon (Pustejovsky, 1995) .
Crowdsourcing as a preprocessing for complex semantic annotation tasks
This article outlines a methodology that uses crowdsourcing to reduce the workload of experts for complex semantic tasks.
Multilingual Projection for Parsing Truly Low-Resource Languages
We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages.
The SemDaX Corpus ― Sense Annotations with Scalable Sense Inventories
The aim of the developed corpus is twofold: i) to assess the reliability of the different sense annotation schemes for Danish measured by qualitative analyses and annotation agreement scores, and ii) to serve as training and test data for machine learning algorithms with the practical purpose of developing sense taggers for Danish.
Approximate unsupervised summary optimisation for selections of ROUGE
Approximate summary optimisation for selections of ROUGE It is standard to measure automatic summariser performance using the ROUGE metric.
From Noisy Questions to Minecraft Texts: Annotation Challenges in Extreme Syntax Scenario
User-generated content presents many challenges for its automatic processing.
Annotating omission in statement pairs
We focus on the identification of omission in statement pairs.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Improving neural tagging with lexical information
Neural part-of-speech tagging has achieved competitive results with the incorporation of character-based and pre-trained word embeddings.
Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models
We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
