BibTeX

@article{sikos22:_improv_multil_frame_ident_estim_frame_trans,
  author =       {Jen Sikos and Michael Roth and Sebastian Padó},
  title =        {Improving Multilingual Frame Identification by Estimating Frame Transferability},
  year =         {2022},
  journal = {Linguistic Issues in Language Technology},
  keywords = {article myown},
  volume = {19},
  url = {https://doi.org/10.33011/lilt.v19i.939}}


@Article{sikos17:_framen_using_relat_as_sourc_concep_parap,
  author = {Jennifer Sikos and Sebastian Padó},
  title =        {FrameNet's 'Using' Relation As Source of Concept-driven Paraphrases},
  journal =      {Constructions and Frames},
  keywords =  {myown},
  volume = {10},
  number = {1},
  url = {https://doi.org/10.1075/cf.00010.sik},
  pages = {38-60},
  year =         2018}


@InProceedings{sikos18:_using_embed_compar_framen_frames_acros_languag,
  author = {Jennifer Sikos and Sebastian Padó},
  title =        {Using Embeddings to Compare {FrameNet} Frames Across Languages},
  booktitle = {Proceedings of the COLING Workshop on Linguistic Resources for Natural Language Processing},
  year =         2018,
keywords =  {workshop myown},
abstract = {Much of the recent interest in Frame Semantics is fueled by the substantial extent of its applicability across languages. At the same time, lexicographic studies have found that the applicability of individual frames can be diminished by cross-lingual divergences regarding polysemy, syntactic valency, and lexicalization. Due to the large effort involved in manual investigations, there are so far no broad-coverage resources with "problematic" frames for any language pair.
Our study investigates to what extent multilingual vector representations of frames learned from manually annotated corpora can address this need by serving as a wide coverage source for such divergences. We present a case study for the language pair English — German using the FrameNet and SALSA corpora and find that inferences can be made about cross-lingual frame applicability using a vector space model.},
  address =      {Santa Fe, NM},
  pages = {91-101},
  url = {https://aclweb.org/anthology/W18-3813}}


@InProceedings{sikos19:_frame_ident_categ,
  author = {Jennifer Sikos and Sebastian Padó},
  title =        {Frame Identification as Categorization: Exemplars vs Protoypes in {Embeddingland}},
  keywords =     {conference myown},
  booktitle = {Proceedings of IWCS},
  year =         2019,
  url = {https://aclweb.org/anthology/papers/W/W19/W19-0425/},
  abstract = {Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input, and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices over these variables, establishes a new state-of-the-art in frame identification.},
  address =      {Gothenburg, Sweden}}