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Found 19 papers, 1 papers with code
A Gentle Introduction to Deep Nets and Opportunities for the Future
The first half of this tutorial will make deep nets more accessible to a broader audience, following “Deep Nets for Poets” and “A Gentle Introduction to Fine-Tuning.” We will also introduce GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models).
Metaphor annotation for German
For the corpus, we developed extended annotation guidelines, which specifically focus not only on the identification of these metaphoric contexts but also analyse in detail specific linguistic challenges for metaphor annotation that emerge due to the grammar of German.
Benchmarking: Past, Present and Future
There used to be more top-down leadership from government (and industry, in the case of systems, with benchmarks such as SPEC).
Beyond Multiword Expressions: Processing Idioms and Metaphors
Idioms and metaphors are characteristic to all areas of human activity and to all types of discourse.
Beyond Words: Deep Learning for Multiword Expressions and Collocations
There is now a lot of work which goes beyond this by adopting a distributed representation of words, by constructing a so-called ``neural embedding'' or vector space representation of each word or document.
Enlarging Scarce In-domain English-Croatian Corpus for SMT of MOOCs Using Serbian
Massive Open Online Courses have been growing rapidly in size and impact.
Enhancing Access to Online Education: Quality Machine Translation of MOOC Content
The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content.
Multiword Expressions in Machine Translation
This work describes an experimental evaluation of the significance of phrasal verb treatment for obtaining better quality statistical machine translation (SMT) results.
Task-Driven Linguistic Analysis based on an Underspecified Features Representation
In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.
Using Verb Subcategorization for Word Sense Disambiguation
We develop a model for predicting verb sense from subcategorization information and integrate it into SSI-Dijkstra, a wide-coverage knowledge-based WSD algorithm.
