Search Results for author:
Found 18 papers, 7 papers with code
Combining BERT with Static Word Embeddings for Categorizing Social Media
A particularly striking example is the performance of AraBERT, an LM for the Arabic language, which is successful in categorizing social media posts in Arabic dialects, despite only having been trained on Modern Standard Arabic.
Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions
Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri.
Pre-Training Language Models for Identifying Patronizing and Condescending Language: An Analysis
Patronizing and Condescending Language (PCL) is a subtle but harmful type of discourse, yet the task of recognizing PCL remains under-studied by the NLP community.
Sentence Selection Strategies for Distilling Word Embeddings from BERT
Many applications crucially rely on the availability of high-quality word vectors.
CollFrEn: Rich Bilingual English–French Collocation Resource
Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike.
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
We show that by augmenting the label set with shared properties, we can improve the performance of the state-of-the-art models for this task.
Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift.
Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction
In this paper, we analyze zero-shot taxonomy learning methods which are based on distilling knowledge from language models via prompting and sentence scoring.
TimeLMs: Diachronic Language Models from Twitter
Despite its importance, the time variable has been largely neglected in the NLP and language model literature.
Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press
This paper summarizes the main findings of the ADoBo 2021 shared task, proposed in the context of IberLef 2021.
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection
Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word.
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention.
Ranked #2 on
Sentiment Analysis
on TweetEval
Evaluating language models for the retrieval and categorization of lexical collocations
We first construct a dataset of apparitions of lexical collocations in context, categorized into 17 representative semantic categories.
Collocation Classification with Unsupervised Relation Vectors
Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words.
ELMD: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain
In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain.
Example-based Acquisition of Fine-grained Collocation Resources
Collocations such as {``}heavy rain{''} or {``}make [a] decision{''}, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base.
