Morphologically-informed representations for natural language processing

Projektinformationen

MorphIRe

ID Finanzhilfevereinbarung: 845995

DOI 10.3030/845995

Abgeschlossenes Projekt

EK-Unterschriftsdatum 18 März 2019

Startdatum 1 April 2019

Enddatum 31 März 2021

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten € 207 312,00

EU-Beitrag € 207 312,00

Koordiniert durch

KOBENHAVNS UNIVERSITET
Denmark

Ziel

The morphological structure of a word plays an important role in determining its function and meaning, yet it is often disregarded by current machine learning models aimed at natural language processing (NLP). State-of-the-art NLP models typically rely on word-level or character-level representations. This arguably works well for English, the dominant language in NLP research, since it is morphologically simple, but poses a challenge for morphologically-rich languages like Basque, Estonian, or Kurdish. As a consequence, the current state of the art is biased against these languages, preventing us from building better NLP technology for them.

The MorphIRe project aims to learn morphologically-informed representations for NLP. It proposes to explore the fine-grained morphological analysis of word forms in order to learn representations that are grounded in morphemes, the smallest grammatical unit of language. Using these representations as input to NLP models is expected to improve their performance particularly for morphologically-rich languages. To this end, MorphIRe will make use of deep learning with neural network architectures both to learn the representations and to apply them to state-of-the-art models for a variety of NLP tasks, such as language modelling and dependency parsing.

The impact of MorphIRe is twofold: 1) Learning input representations that can be used in a variety of models encourages reusability of the results and promises that improvements will carry over to future NLP research. 2) Through improving the state of the art on morphologically-rich languages, speakers of these languages will ultimately benefit from better NLP technology. This way, MorphIRe has the potential for making both a scientific and a societal impact.

Wissenschaftliches Gebiet

• natural sciences › computer and information sciences › data science › natural language processing
• natural sciences › computer and information sciences › artificial intelligence › machine learning › deep learning
• natural sciences › computer and information sciences › artificial intelligence › computational intelligence

Programm/Programme

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility

Thema/Themen

• MSCA-IF-2018 - Individual Fellowships

Aufforderung zur Vorschlagseinreichung

H2020-MSCA-IF-2018

Finanzierungsplan

MSCA-IF-EF-ST - Standard EF

Koordinator

KOBENHAVNS UNIVERSITET

• Netto-EU-Beitrag: € 207 312,00
• Adresse: NORREGADE 10 ; 1165 Kobenhavn ; Dänemark
• Region: Danmark › Hovedstaden › Byen København
• Aktivitätstyp: Higher or Secondary Education Establishments
• Gesamtkosten: € 207 312,00