Search Results for author:
Found 52 papers, 2 papers with code
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.
Deep Learning Models For Multiword Expression Identification
Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words.
Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models.
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.
Determining the Multiword Expression Inventory of a Surprise Language
Much previous research on multiword expressions (MWEs) has focused on the token- and type-level tasks of MWE identification and extraction, respectively.
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.
Evaluating a Topic Modelling Approach to Measuring Corpus Similarity
Web corpora are often constructed automatically, and their contents are therefore often not well understood.
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus
We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms.
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.
Evaluating Sub-word Embeddings in Cross-lingual Models
This is particularly problematic for low-resource and morphologically-rich languages, which often have relatively high OOV rates.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
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Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling
In this paper we consider a range of n-gram and RNN language models for Mi{'}kmaq.
Evaluating a Multi-sense Definition Generation Model for Multiple Languages
Most prior work on definition modeling has not accounted for polysemy, or has done so by considering definition modeling for a target word in a given context.
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora
In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon.
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages
Cross-lingual word embeddings provide a way for information to be transferred between languages.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
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.
Contextualized Embeddings Encode Monolingual and Cross-lingual Knowledge of Idiomaticity
We consider monolingual experiments for English and Russian, and show that the proposed model outperforms previous approaches, including in the case that the model is tested on instances of PIE types that were not observed during training.
Cross-Lingual Wolastoqey-English Definition Modelling
In this paper, we consider cross-lingual definition generation.
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.
Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations
As there exist no large corpora of running text for Wolastoqey, in this paper, we leverage a bilingual dictionary to learn Wolastoqey word embeddings by encoding their corresponding English definitions into vector representations using pretrained English word and sequence representation models.
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey
Finite-state approaches to morphological analysis have been shown to improve the performance of natural language processing systems for polysynthetic languages, in-which words are generally composed of many morphemes, for tasks such as language modelling (Schwartz et al., 2020).
