Obiettivo There are noticeable asymmetries in availability of high-quality natural language processing (NLP). We can adequately summarize English newspapers and translate them into Korean, but we cannot translate Korean newspaper articles into English, and summarizing micro-blogs is much more difficult than summarizing newspaper articles. This is a fundamental problem for modern societies, their development and democracy, as well as perhaps the most important research problem in NLP right now.Most NLP technologies rely on highly accurate syntactic parsing. Reliable parsing models can be induced from large collections of manually annotated data, but such collections are typically limited to sampled newswire in major languages. Highly accurate parsing is therefore not available for other languages and other domains.The NLP community is well aware of this problem, but unsupervised techniques that do not rely on manually annotated data cannot be used for real-world applications, where highly accurate parsing is needed, and sample bias correction methods that automatically correct the bias in newswire when parsing, say, micro-blogs, do not yet lead to robust improvements across the board.The objective of this project is to develop new learning methods for parsing natural language for which no unbiased labeled data exists. In order to do so, we need to fundamentally rethink the unsupervised parsing problem, including how we evaluate unsupervised parsers, but we also need to supplement unsupervised learning techniques with robust methods for automatically correcting sample selection biases in related data. Such methods will be applicable to both cross-domain and cross-language syntactic parsing and will pave the way toward robust and scalable NLP. The societal impact of robust and scalable NLP is unforeseeable and comparable to how efficient information retrieval techniques have revolutionized modern societies. Campo scientifico natural sciencescomputer and information sciencesdata sciencenatural language processingnatural sciencescomputer and information sciencesartificial intelligencemachine learningunsupervised learningsocial sciencespolitical sciencesgovernment systemsdemocracy Programma(i) FP7-IDEAS-ERC - Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Argomento(i) ERC-SG-SH4 - ERC Starting Grant - The Human Mind and its complexity Invito a presentare proposte ERC-2012-StG_20111124 Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-SG - ERC Starting Grant Istituzione ospitante KOBENHAVNS UNIVERSITET Contributo UE € 1 126 183,20 Indirizzo NORREGADE 10 1165 Kobenhavn Danimarca Mostra sulla mappa Regione Danmark Hovedstaden Byen København Tipo di attività Higher or Secondary Education Establishments Ricercatore principale Anders Søgaard (Dr.) Contatto amministrativo Tine Matthiesen (Mrs.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato Beneficiari (1) Classifica in ordine alfabetico Classifica per Contributo UE Espandi tutto Riduci tutto KOBENHAVNS UNIVERSITET Danimarca Contributo UE € 1 126 183,20 Indirizzo NORREGADE 10 1165 Kobenhavn Mostra sulla mappa Regione Danmark Hovedstaden Byen København Tipo di attività Higher or Secondary Education Establishments Ricercatore principale Anders Søgaard (Dr.) Contatto amministrativo Tine Matthiesen (Mrs.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato