Objective 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. Fields of science natural sciencescomputer and information sciencesdata sciencenatural language processingnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) 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 Topic(s) MSCA-IF-2018 - Individual Fellowships Call for proposal H2020-MSCA-IF-2018 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator KOBENHAVNS UNIVERSITET Net EU contribution € 207 312,00 Address NORREGADE 10 1165 Kobenhavn Denmark See on map Region Danmark Hovedstaden Byen København Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 207 312,00
