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Found 10 papers, 0 papers with code
Quick Starting Dialog Systems with Paraphrase Generation
Acquiring training data to improve the robustness of dialog systems can be a painstakingly long process.
Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems
Clarification resolution plays an important role in various information retrieval tasks such as interactive question answering and conversational search.
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).
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.
A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data
Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms.
SemLinker, a Modular and Open Source Framework for Named Entity Discovery and Linking
This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically.
Improving Entity Linking using Surface Form Refinement
The approach extends the surface form coverage of our entity linking system, and rewrites or reformulates misspelled mentions (entities) prior to starting the annotation process.
A disambiguation resource extracted from Wikipedia for semantic annotation
The Semantic Annotation (SA) task consists in establishing the relation between a textual entity (word or group of words designating a named entity of the real world or a concept) and its corresponding entity in an ontology.
