Search Results for author:
Found 8 papers, 0 papers with code
Now, It’s Personal : The Need for Personalized Word Sense Disambiguation
We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author’s sense distributions or predominant senses can greatly increase its performance.
UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders
In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1.
Evaluating Approaches to Personalizing Language Models
In this work, we consider the problem of personalizing language models, that is, building language models that are tailored to the writing style of an individual.
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language
In this paper we apply a range of approaches to language modeling {--} including word-level n-gram and neural language models, and character-level neural language models {--} to the problem of detecting hate speech and offensive language.
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations
In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations.
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency.
Supervised and unsupervised approaches to measuring usage similarity
Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory.
