|12.00–13.00||Lunch in Frederikke|
|13.05–14.00||Invited presentation: Ontologies versus lexical semantics
|14.00–14.30||Automatic identification of construction candidates for a Swedish constructicon
Linnéa Bäckström, Lars Borin, Markus Forsberg, Benjamin Lyngfelt, Julia Prentice and Emma Sköldberg
|15.00–15.30||LBK2013: A balanced, annotated national corpus for Norwegian Bokmål
Rune Lain Knudsen and Ruth Vatvedt Fjeld
|15.30–16.00||Clustering word senses from semantic mirroring data
Hampus Lilliehöök and Magnus Merkel
|16.00–16.30||Enriching a wordnet from a thesaurus
Sanni Nimb, Bolette S. Pedersen, Anna Braasch, Nicolai H. Sørensen and Thomas Troelsgård
High-quality lexical semantic resources with sufficiently large vocabularies still prove to be a serious bottleneck not only in purely rule-based NLP applications but also in supervised corpus-based approaches. The oldest widely-known lexical semantic resource, Princeton WordNet (PWN), has been around for over two decades. While PWN and the numerous wordnet projects for other languages that it has inspired adhere fairly closely to the traditional dictionary in their conception and organization, there are also lexical-semantic resources where a closer integration of lexical data information and corpus data is attempted. Such resources can be seen either as extremely richly exemplified lexicons or extremely deeply annotated corpora, depending on your outlook. Berkeley FrameNet, VerbNet, PropBank and several others can be mentioned in this connnection. A recent trend in the wake of the increased awareness of the importance of standardization and interoperability of language resources, is the development towards large-scale integration of lexical resources (variously referred to as “lexical cores”, “lexical macroresources”, “lexical resource networks”, and the like) both within and across languages, the ultimate expression of which is at the moment the linked open data in linguistics movement.
For largely extraneous reasons, English-language resources tend to receive most attention in the LT literature, but there is an increasing number of lexical semantic resources under development for many other languages, including Nordic, Baltic and other languages of the NEALT area.
In parallel to this development of new lexical semantic resources, much effort is put into exploring how such resources and formal ontologies can be made to work together in knowledge-based systems. The workshop – a follow-up on the succesful Nodalida 2009 workshop where the focus was on wordnets – intends to bring together researchers involved in building and integrating lexical semantic resources for NLP as well as researchers that are more theoretically interested in investigating the interplay between lexical semantics, lexicography, terminology and formal ontologies.
We invite papers presenting original research relating to lexical semantic resources for NLP on topics such as:
Papers should conform to the main Nodalida stylesheet .
Submissions must be anonymous, i.e. not reveal author(s) on the title page or through self-references. Papers must be submitted digitally, in PDF, and uploaded through the on-line conference system. Paper submissions that violate either of these requirements will be returned without review.
The page limit for submissions is up to fourteen pages of text, plus unlimited additional pages with bibliographic references. Please note that NoDaLiDa 2013 adapts a single-column, smaller page format, optimized for on-screen reading. In terms of actual word counts, this page limit corresponds to approximately eight pages in a ‘classic’, two-column conference proceedings layout.
All submissions to the workshop must be uploaded electronically, following the above requirements. All submissions will be reviewed by the program committee. All accepted papers will be collected into a proceedings volume to be submitted for publication in the NEALT Proceeding Series (Linköping Electronic Conference Proceedings).
For all inquiries, please email Lars Borin <lars dot borin at svenska dot gu dot se>.