no code implementations • 18 Dec 2023 • Vivi Nastase, Paola Merlo
We explore whether we can compress transformer-based sentence embeddings into a representation that separates different linguistic signals -- in particular, information relevant to subject-verb agreement and verb alternations.
1 code implementation • 15 Dec 2023 • Vivi Nastase, Paola Merlo
Next, we show that various architectures can detect patterns in these two-dimensional reshaped sentence embeddings and successfully learn a model based on smaller amounts of simpler training data, which performs well on more complex test data.
no code implementations • 11 Sep 2020 • Vivi Nastase, Stan Szpakowicz
The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha has been published in April 2021 by Morgan & Claypool (www. morganclaypoolpublishers. com/catalog_Orig/product_info. php? products_id=1627).
no code implementations • IJCNLP 2019 • Mahmoud Azab, Stephane Dadian, Vivi Nastase, Larry An, Rada Mihalcea
We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain.
no code implementations • WS 2019 • Yulia Clausen, Vivi Nastase
We present an analysis of metaphors in news text simplification.
no code implementations • WS 2019 • Ilia Markov, Vivi Nastase, Carlo Strapparava
In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2).
no code implementations • SEMEVAL 2019 • Vivi Nastase, Bhushan Kotnis
Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete.
no code implementations • WS 2019 • Maria Becker, Michael Staniek, Vivi Nastase, Anette Frank
Commonsense knowledge relations are crucial for advanced NLU tasks.
no code implementations • WS 2018 • Ilia Markov, Vivi Nastase, Carlo Strapparava, Grigori Sidorov
We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language.
no code implementations • COLING 2018 • Juri Opitz, Leo Born, Vivi Nastase
We induce and visualize a Knowledge Graph over the Regesta Imperii (RI), an important large-scale resource for medieval history research.
no code implementations • COLING 2018 • Ilia Markov, Vivi Nastase, Carlo Strapparava
In this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification.
no code implementations • EMNLP 2017 • Vivi Nastase, Carlo Strapparava
We present experiments that show the influence of native language on lexical choice when producing text in another language {--} in this particular case English.
1 code implementation • 22 Aug 2017 • Bhushan Kotnis, Vivi Nastase
We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.
no code implementations • SEMEVAL 2017 • Maria Becker, Michael Staniek, Vivi Nastase, Alexis Palmer, Anette Frank
Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features.
no code implementations • ACL 2017 • Lingzhen Chen, Carlo Strapparava, Vivi Nastase
We note that character n-grams from misspelled words are very indicative of the native language of the author.
no code implementations • 28 Jun 2017 • Bhushan Kotnis, Vivi Nastase
Learning relations based on evidence from knowledge bases relies on processing the available relation instances.
no code implementations • 28 Nov 2014 • Vivi Nastase, Angela Fahrni
We present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e. g. history, economy) are aligned based on induced multilingual topics.
no code implementations • LREC 2014 • Sp Gella, ana, Carlo Strapparava, Vivi Nastase
In this paper we present the mapping between WordNet domains and WordNet topics, and the emergent Wikipedia categories.
no code implementations • LREC 2012 • Alex Judea, Vivi Nastase, Michael Strube
This paper describes the derivation of distributional semantic representations for open class words relative to a concept inventory, and of concepts relative to open class words through grammatical relations extracted from Wikipedia articles.