NonSequeToR SIGNED
Non-sequence models for tokenization replacement
Total Cost €
0EC-Contrib. €
0Partnership
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0NonSequeToR project word cloud
Explore the words cloud of the NonSequeToR project. It provides you a very rough idea of what is the project "NonSequeToR" about.
Project "NonSequeToR" data sheet
The following table provides information about the project.
| Coordinator |
LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Organization address contact info |
| Coordinator Country | Germany [DE] |
| Total cost | 2˙500˙000 € |
| EC max contribution | 2˙500˙000 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2016-ADG |
| Funding Scheme | ERC-ADG |
| Starting year | 2017 |
| Duration (year-month-day) | from 2017-10-01 to 2022-09-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN | DE (MUENCHEN) | coordinator | 2˙500˙000.00 |
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Project objective
Natural language processing (NLP) is concerned with computer-based processing of natural language, with applications such as human-machine interfaces and information access. The capabilities of NLP are currently severely limited compared to humans. NLP has high error rates for languages that differ from English (e.g., languages with higher morphological complexity like Czech) and for text genres that are not well edited (or noisy) and that are of high economic importance, e.g., social media text.
NLP is based on machine learning, which requires as basis a representation that reflects the underlying structure of the domain, in this case the structure of language. But representations currently used are symbol-based: text is broken into surface forms by sequence models that implement tokenization heuristics and treat each surface form as a symbol or represent it as an embedding (a vector representation) of that symbol. These heuristics are arbitrary and error-prone, especially for non-English and noisy text, resulting in poor performance.
Advances in deep learning now make it possible to take the embedding idea and liberate it from the limitations of symbolic tokenization. I have the interdisciplinary expertise in computational linguistics, computer science and deep learning required for this project and am thus in the unique position to design a radically new robust and powerful non-symbolic text representation that captures all aspects of form and meaning that NLP needs for successful processing.
By creating a text representation for NLP that is not impeded by the limitations of symbol-based tokenization, the foundations are laid to take NLP applications like human-machine interaction, human-human communication supported by machine translation and information access to the next level.
Deliverables
| Data Management Plan | Open Research Data Pilot | 2019-03-25 09:52:52 |
Take a look to the deliverables list in detail: detailed list of NonSequeToR deliverables.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Timo Schick, Hinrich Schütze Learning Semantic Representations for Novel Words: Leveraging Both Form and Context published pages: , ISSN: , DOI: 10.5282/ubm/epub.61859 |
2019-06-06 | |
| 2018 |
Philipp Dufter, Hinrich Schütze A Stronger Baseline for Multilingual Word Embeddings published pages: , ISSN: , DOI: 10.5282/ubm/epub.61864 |
2019-06-06 | |
| 2019 |
Apostolos Kemos, Heike Adel, Hinrich Schütze Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging published pages: , ISSN: , DOI: 10.5282/ubm/epub.61846 |
2019-06-06 | |
| 2019 |
Timo Schick, Hinrich Schütze Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking published pages: , ISSN: , DOI: 10.5282/ubm/epub.61863 |
2019-06-06 | |
| 2018 |
Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schuetze Corpus-Level Fine-Grained Entity Typing published pages: 835-862, ISSN: 1076-9757, DOI: 10.1613/jair.5601 |
Journal of Artificial Intelligence Research 61 | 2019-06-06 |
| 2019 |
Timo Schick, Hinrich Schütze Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts published pages: , ISSN: , DOI: 10.5282/ubm/epub.61844 |
2019-06-06 | |
| 2018 |
Wenpeng Yin, Hinrich Schütze Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms published pages: 687-702, ISSN: 2307-387X, DOI: 10.1162/tacl_a_00249 |
Transactions of the Association for Computational Linguistics 6 | 2019-06-06 |
| 2018 |
Nina Poerner, Masoud Jalili Sabet, Benjamin Roth and Hinrich Schütze Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective published pages: , ISSN: , DOI: 10.5282/ubm/epub.61865 |
2019-06-06 | |
| 2019 |
Marco Cornolti, Paolo Ferragina, Massimiliano Ciaramita, Stefan Rüd, Hinrich Schütze SMAPH published pages: 1-42, ISSN: 1046-8188, DOI: 10.1145/3284102 |
ACM Transactions on Information Systems 37/1 | 2019-06-06 |
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The information about "NONSEQUETOR" are provided by the European Opendata Portal: CORDIS opendata.