Search Results for author:
Found 12 papers, 2 papers with code
Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages
This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi.
Combining Language Models and Linguistic Information to Label Entities in Memes
This paper describes the system we developed for the shared task ‘Hero, Villain and Victim: Dissecting harmful memes for Semantic role labelling of entities’ organised in the framework of the Second Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (Constraint 2022).
A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek
This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically.
SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis
In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT
In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT).
LT3 at SemEval-2021 Task 6: Using Multi-Modal Compact Bilinear Pooling to Combine Visual and Textual Understanding in Memes
Internet memes have become ubiquitous in social media networks today.
LT3 at SemEval-2020 Task 8: Multi-Modal Multi-Task Learning for Memotion Analysis
Internet memes have become a very popular mode of expression on social media networks today.
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment.
Ranked #13 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(Restaurant (Acc) metric)
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text
The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets.
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings
We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz.
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings
This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch.
Multidomain Document Layout Understanding using Few Shot Object Detection
We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain.
