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Found 29 papers, 4 papers with code
Surface Realization Using Pretrained Language Models
In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar.
Deep Hedging with Market Impact
Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability.
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT
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.
Du bon usage d'ingr\'edients linguistiques sp\'eciaux pour classer des recettes exceptionnelles (Using Special Linguistic Ingredients to Classify Exceptional Recipes )
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).
On the Creation of a Corpus for Coherence Evaluation of Discursive Units
We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones.
Cooking Up a Neural-based Model for Recipe Classification
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.
The Concordia NLG Surface Realizer at SRST 2019
This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.
Neural Feature Extraction for Contextual Emotion Detection
This paper describes a new approach for the task of contextual emotion detection.
Ranked #9 on
Emotion Recognition in Conversation
on EC
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM
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.
CLaC at CLPsych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts
This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC.
Toponym Identification in Epidemiology Articles - A Deep Learning Approach
These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
CLaC @ DEFT 2018: Sentiment analysis of tweets on transport from \^Ile-de-France
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.
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
This paper describes the system deployed by the CLaC-EDLK team to the "SemEval 2016, Complex Word Identification task".
ClaC: Semantic Relatedness of Words and Phrases
The measurement of phrasal semantic relatedness is an important metric for many natural language processing applications.
Measuring the Effect of Discourse Relations on Blog Summarization
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.
CLaC @ QATS: Quality Assessment for Text Simplification
This paper describes our approach to the 2016 QATS quality assessment shared task.
On the Contribution of Discourse Structure on Text Complexity Assessment
This paper investigates the influence of discourse features on text complexity assessment.
The CLaC Discourse Parser at CoNLL-2016
This paper describes our submission "CLaC" to the CoNLL-2016 shared task on shallow discourse parsing.
The CLaC Discourse Parser at CoNLL-2015
This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing.
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks
Argument labeling of explicit discourse relations is a challenging task.
Automatic Identification of AltLexes using Monolingual Parallel Corpora
The automatic identification of discourse relations is still a challenging task in natural language processing.
N-gram and Neural Language Models for Discriminating Similar Languages
This paper describes our submission (named clac) to the 2016 Discriminating Similar Languages (DSL) shared task.
Improving Discourse Relation Projection to Build Discourse Annotated Corpora
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.
Automatic Mapping of French Discourse Connectives to PDTB Discourse Relations
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.
Automatic Disambiguation of French Discourse Connectives
Discourse connectives (e. g. however, because) are terms that can explicitly convey a discourse relation within a text.
