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Found 12 papers, 2 papers with code
Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation
The results show that the proposed approach can align the two embedding spaces while achieving good performance in both brands.
Wasserstein distances for evaluating cross-lingual embeddings
Word embeddings are high dimensional vector representations of words that capture their semantic similarity in the vector space.
Cross-Lingual Document Classification
Document Classification
+4
Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision
We propose a neural network architecture for learning vector representations of hotels.
CAp 2017 challenge: Twitter Named Entity Recognition
We begin by demonstrating why NER for tweets is a challenging problem especially when the number of entities increases.
On the effectiveness of feature set augmentation using clusters of word embeddings
Word clusters have been empirically shown to offer important performance improvements on various tasks.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
We address the problem of multi-class classification in the case where the number of classes is very large.
Learning to Search for Recognizing Named Entities in Twitter
We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task.
Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs Machine Learning
Comparing Named-Entity Recognizers in a Targeted Domain : Handcrafted Rules vs. Machine Learning Named-Entity Recognition concerns the classification of textual objects in a predefined set of categories such as persons, organizations, and localizations.
e-Commerce product classification: our participation at cDiscount 2015 challenge
This report describes our participation in the cDiscount 2015 challenge where the goal was to classify product items in a predefined taxonomy of products.
LSHTC: A Benchmark for Large-Scale Text Classification
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands).
On Flat versus Hierarchical Classification in Large-Scale Taxonomies
We study in this paper flat and hierarchical classification strategies in the context of large-scale taxonomies.
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
Hierarchical classification addresses the problem of classifying items into a hierarchy of classes.
