A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection - Lapesa, Gabriella; Evert, Stephanie (Transactions of the Association for Computational Linguistics, 2014)
This paper presents the results of a large-scale evaluation study of window-based Distributional Semantic Models on a wide variety of tasks. Our study combines a broad coverage of model parameters with a model selection methodology that is robust to overfitting and able to capture parameter interactions. We show that our strategy allows us to identify parameter configurations that achieve good performance across different datasets and tasks.
Keywords: word space

Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models - Lapesa, Gabriella; Evert, Stephanie; Schulte im Walde, Sabine (Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), 2014)
Keywords: word space

Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming - Lapesa, Gabriella; Evert, Stephanie (Proceedings of the ACL Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2013), 2013)
Keywords: word space; psycholinguistics

KLUMSy@KIPoS: Experiments on Part-of-Speech Tagging of Spoken Italian - Proisl, Thomas; Lapesa, Gabriella (Proceedings of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2020), 2020)
In this paper, we describe experiments on part-of-speech tagging of spoken Italian that we conducted in the context of the EVALITA 2020 KIPoS shared task (Bosco et al., 2020). Our submission to the shared task is based on SoMeWeTa (Proisl, 2018), a tagger which supports domain adaptation and is designed to flexibly incorporate external resources. We document our approach and discuss our results in the shared task along with a statistical analysis of the factors which impact performance the most. Additionally, we report on a set of additional experiments involving the combination of neural language models with unsupervised HMMs, and compare its performance to that of our system. </e

Large-scale evaluation of dependency-based DSMs: Are they worth the effort? - Lapesa, Gabriella; Evert, Stephanie (Proceedings of the 15th Annual Meeting of the European Association for Computational Linguistics (EACL 2017): Volume 2, Short Papers, 2017)
This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependency-filtered and dependency-structured DSMs in a number of standard semantic similarity tasks, systematically exploring their parameter space in order to give them a “fair shot” against window-based models. Our results show that properly tuned window-based DSMs still outperform the dependency-based models in most tasks. There appears to be little need for the language-dependent resources and computational cost associated with syntactic analysis.
Keywords: word space; distributional semantics; evaluation

NaDiR: Naive Distributional Response Generation - Lapesa, Gabriella; Evert, Stephanie (Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex), 2014)
Keywords: word space; shared task

SemantiKLUE: Semantic Textual Similarity with Maximum Weight Matching - Plotnikova, Nataliia; Lapesa, Gabriella; Proisl, Thomas; Evert, Stephanie (Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015)
Keywords: shared task; semantics