Correlation-based Intrinsic Evaluation of Word Vector Representations

We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVEC-CCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.Comment: RepEval 2016, 5 page

Lexical Semantic Recognition

In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.Comment: 11 pages, 3 figures; to appear at MWE 202

Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.Comment: Published at NoDaLiDa 2019; updated (system, data and repository details

Fra begrebsordbog til sprogteknologisk ressource: verber, semantiske roller og rammer – et pilotstudie

This paper describes a method of compiling a lexicon of Danish semantic frames within the model of the Berkeley FrameNet (BFN). Large groups of near-synonymous verbs and verbal nouns, including multiword units, within the domains of communication and cognition are identified and extracted from the source manuscript of a newly published Danish the-saurus. Each word or expression is then assigned an appropriate frame from BFN. The fact that words within the same domain all belong to a manageable subset of frames in BFN makes is possible to map a high number of words to their corresponding frames simultaneously. In a forthcoming annotation project where words within the same two do-mains are already identified in the corpus, the idea is to pre-annotate with the frames in our lexicon, leaving afterwards human annotators to con-firm the frame and test whether it is possible to identify the BFN seman-tic roles described for English in the Danish text. Our method reveals some interesting divergences between the semantic divisions established in the thesaurus in contrast to the ones found in BFN, showing that the two resources contribute with different types of linguistic information and thereby constitute a useful supplement to one another