no code implementations • MSR (COLING) 2020 • Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens
In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar.
no code implementations • 20 Feb 2024 • Andrei Neagu, Frédéric Godin, Clarence Simard, Leila Kosseim
Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability.
no code implementations • COLING 2020 • MohammadReza Davari, Leila Kosseim, Tien Bui
In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses.
no code implementations • JEPTALNRECITAL 2020 • Elham Mohammadi, Louis Marceau, Eric Charton, Leila Kosseim, Luka Nerima, Marie-Jean Meurs
Nous pr{\'e}sentons un mod{\`e}le d{'}apprentissage automatique qui combine mod{\`e}les neuronaux et linguistiques pour traiter les t{\^a}ches de classification dans lesquelles la distribution des {\'e}tiquettes des instances est d{\'e}s{\'e}quilibr{\'e}e. Les performances de ce mod{\`e}le sont mesur{\'e}es {\`a} l{'}aide d{'}exp{\'e}riences men{\'e}es sur les t{\^a}ches de classification de recettes de cuisine de la campagne DEFT 2013 (Grouin et al., 2013).
no code implementations • LREC 2020 • Elham Mohammadi, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton, Leila Kosseim, Marie-Jean Meurs
In this paper, we propose a neural-based model to address the first task of the DEFT 2013 shared task, with the main challenge of a highly imbalanced dataset, using state-of-the-art embedding approaches and deep architectures.
no code implementations • LREC 2020 • Elham Mohammadi, Timothe Beiko, Leila Kosseim
We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones.
no code implementations • WS 2019 • Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens
This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.
no code implementations • RANLP 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper describes a new approach for the task of contextual emotion detection.
Ranked #9 on Emotion Recognition in Conversation on EC
no code implementations • WS 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC.
no code implementations • SEMEVAL 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues.
no code implementations • 24 Apr 2019 • MohammadReza Davari, Leila Kosseim, Tien D. Bui
These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
no code implementations • JEPTALNRECITAL 2018 • Simon Jacques, Farhood Farahnak, Leila Kosseim
CLaC @ DEFT 2018: Analysis of tweets on transport on the {\^I}le-de-France This paper describes the system deployed by the CLaC lab at Concordia University in Montreal for the DEFT 2018 shared task.
no code implementations • SEMEVAL 2016 • Elnaz Davoodi, Leila Kosseim
This paper describes the system deployed by the CLaC-EDLK team to the "SemEval 2016, Complex Word Identification task".
no code implementations • WS 2016 • Elnaz Davoodi, Leila Kosseim
This paper investigates the influence of discourse features on text complexity assessment.
no code implementations • SEMEVAL 2013 • Reda Siblini, Leila Kosseim
The measurement of phrasal semantic relatedness is an important metric for many natural language processing applications.
no code implementations • 19 Aug 2017 • Elnaz Davoodi, Leila Kosseim
This paper describes our approach to the 2016 QATS quality assessment shared task.
no code implementations • IJCNLP 2013 • Shamima Mithun, Leila Kosseim
The work presented in this paper attempts to evaluate and quantify the use of discourse relations in the context of blog summarization and compare their use to more traditional and factual texts.
1 code implementation • CONLL 2015 • Majid Laali, Elnaz Davoodi, Leila Kosseim
This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing.
no code implementations • CONLL 2016 • Majid Laali, Andre Cianflone, Leila Kosseim
This paper describes our submission "CLaC" to the CoNLL-2016 shared task on shallow discourse parsing.
no code implementations • RANLP 2017 • Sohail Hooda, Leila Kosseim
Argument labeling of explicit discourse relations is a challenging task.
no code implementations • RANLP 2017 • Elnaz Davoodi, Leila Kosseim
The automatic identification of discourse relations is still a challenging task in natural language processing.
no code implementations • WS 2016 • Andre Cianflone, Leila Kosseim
This paper describes our submission (named clac) to the 2016 Discriminating Similar Languages (DSL) shared task.
1 code implementation • RANLP 2017 • Majid Laali, Leila Kosseim
We then used this corpus to train a classifier to identify the discourse-usage of French discourse connectives and show a 15% improvement of F1-score compared to the classifier trained on the non-filtered annotations.
1 code implementation • WS 2017 • Majid Laali, Leila Kosseim
In this paper, we present an approach to exploit phrase tables generated by statistical machine translation in order to map French discourse connectives to discourse relations.
1 code implementation • 18 Apr 2017 • Majid Laali, Leila Kosseim
Discourse connectives (e. g. however, because) are terms that can explicitly convey a discourse relation within a text.