no code implementations • RANLP 2021 • Keith Cortis, Kanishk Verma, Brian Davis
This paper presents multidimensional Social Opinion Mining on user-generated content gathered from newswires and social networking services in three different languages: English —a high-resourced language, Maltese —a low-resourced language, and Maltese-English —a code-switched language.
no code implementations • LATERAISSE (LREC) 2022 • Kanishk Verma, Tijana Milosevic, Keith Cortis, Brian Davis
Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms.
1 code implementation • INLG (ACL) 2021 • Thiago castro Ferreira, Helena Vaz, Brian Davis, Adriana Pagano
This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG.
no code implementations • INLG (ACL) 2021 • Simon Mille, Thiago castro Ferreira, Anya Belz, Brian Davis
Clarity had a higher degree of reproducibility than Fluency, as measured by the coefficient of variation.
no code implementations • SIGUL (LREC) 2022 • Keith Cortis, Brian Davis
This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection.
no code implementations • TRAC (COLING) 2022 • Kanishk Verma, Tijana Milosevic, Brian Davis
Automated textual cyberbullying detection is known to be a challenging task.
no code implementations • 13 Feb 2024 • Chinonso Cynthia Osuji, Thiago castro Ferreira, Brian Davis
Relevant literature in this field on datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods is reviewed.
no code implementations • 12 Nov 2023 • Lahari Bandaru, Bharat C Irigireddy, Brian Davis
Flagit corrected flagged 95. 5% of the corrected observations and 50. 3% of the anomaly observations, indicating its limitations in identifying anomalies.
2 code implementations • 30 Mar 2022 • Brian Davis, Bryan Morse, Bryan Price, Chris Tensmeyer, Curtis Wigington, Vlad Morariu
Dessurt is a more flexible model than prior methods and is able to handle a variety of document domains and tasks.
Ranked #31 on Visual Question Answering (VQA) on DocVQA test
3 code implementations • 17 May 2021 • Brian Davis, Bryan Morse, Brian Price, Chris Tensmeyer, Curtis Wiginton
FUDGE edits the graph structure by combining text segments (graph vertices) and pruning edges in an iterative fashion to obtain the final text entities and relationships.
no code implementations • 5 Dec 2020 • Keith Cortis, Brian Davis
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels.
1 code implementation • 1 Sep 2020 • Brian Davis, Chris Tensmeyer, Brian Price, Curtis Wigington, Bryan Morse, Rajiv Jain
This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors.
no code implementations • LREC 2020 • John Roberto, Diego Ortego, Brian Davis
The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting.
no code implementations • LREC 2020 • John Roberto, Brian Davis
The purpose of this paper is to present a prospective and interdisciplinary research project seeking to ontologize knowledge of the domain of Outsider Art, that is, the art created outside the boundaries of official culture.
BIG-bench Machine Learning Cultural Vocal Bursts Intensity Prediction +1
no code implementations • WS 2019 • Keith Cortis, Brian Davis
We present a gold standard of annotated social opinion for the Malta Government Budget 2018.
3 code implementations • 5 Sep 2019 • Brian Davis, Bryan Morse, Scott Cohen, Brian Price, Chris Tensmeyer
Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts.
no code implementations • 20 Jan 2019 • Brian Davis, Umang Bhatt, Kartikeya Bhardwaj, Radu Marculescu, José M. F. Moura
In this paper, we present a new approach to interpret deep learning models.
1 code implementation • ECCV 2018 • Curtis Wigington, Chris Tensmeyer, Brian Davis, William Barrett, Brian Price, Scott Cohen
Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives.
Ranked #12 on Handwritten Text Recognition on IAM
no code implementations • 4 Aug 2018 • Chris Tensmeyer, Curtis Wigington, Brian Davis, Seth Stewart, Tony Martinez, William Barrett
Training state-of-the-art offline handwriting recognition (HWR) models requires large labeled datasets, but unfortunately such datasets are not available in all languages and domains due to the high cost of manual labeling. We address this problem by showing how high resource languages can be leveraged to help train models for low resource languages. We propose a transfer learning methodology where we adapt HWR models trained on a source language to a target language that uses the same writing script. This methodology only requires labeled data in the source language, unlabeled data in the target language, and a language model of the target language.
no code implementations • WS 2018 • Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross McDermott, Manel Zarrouk, Brian Davis
This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis.
no code implementations • WS 2016 • Vivian S. Silva, Manuela Hürliman, Brian Davis, Siegfried Handschuh, André Freitas
This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.
no code implementations • 16 May 2018 • Siamak Barzegar, Juliano Efson Sales, Andre Freitas, Siegfried Handschuh, Brian Davis
This demonstration presents an infrastructure for computing multilingual semantic relatedness and correlation for twelve natural languages by using three distributional semantic models (DSMs).
no code implementations • 16 May 2018 • Siamak Barzegar, Andre Freitas, Siegfried Handschuh, Brian Davis
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text.
no code implementations • 16 May 2018 • Andre Freitas, Siamak Barzegar, Juliano Efson Sales, Siegfried Handschuh, Brian Davis
The results also show that the benefit of using the most informative corpus outweighs the possible errors introduced by the machine translation.
no code implementations • JEPTALNRECITAL 2018 • Thomas Gaillat, Ann Sousa, a, Manel Zarrouk, Brian Davis
FinSentiA: Sentiment Analysis in English Financial Microblogs The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English.
3 code implementations • 5 Sep 2017 • Chris Tensmeyer, Brian Davis, Curtis Wigington, Iain Lee, Bill Barrett
When digitizing a document into an image, it is common to include a surrounding border region to visually indicate that the entire document is present in the image.
no code implementations • SEMEVAL 2017 • Keith Cortis, Andr{\'e} Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, H, Siegfried schuh, Brian Davis
This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme.
no code implementations • 11 Jun 2014 • Hazem Safwat, Brian Davis
One of the main challenges for building the Semantic web is Ontology Authoring.