Search Results for author:
Found 16 papers, 7 papers with code
Bilingual Topic Models for Comparable Corpora
However, this assumption is strong for comparable corpora that consist of documents thematically similar to an extent only, which are, in turn, the most commonly available or easy to obtain.
Query Understanding for Natural Language Enterprise Search
Natural Language Search (NLS) extends the capabilities of search engines that perform keyword search allowing users to issue queries in a more "natural" language.
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
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Lexical Bias In Essay Level Prediction
Automatically predicting the level of non-native English speakers given their written essays is an interesting machine learning problem.
Concurrent Learning of Semantic Relations
Discovering whether words are semantically related and identifying the specific semantic relation that holds between them is of crucial importance for NLP as it is essential for tasks like query expansion in IR.
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature.
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification
Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E.
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.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
Topical Coherence in LDA-based Models through Induced Segmentation
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.
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.
Modeling topic dependencies in semantically coherent text spans with copulas
The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent.
An empirical study on large scale text classification with skip-gram embeddings
We investigate the integration of word embeddings as classification features in the setting of large scale text classification.
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Specifically, we participated in Task 4, namely "Sentiment Analysis in Twitter" for which we implemented sentiment classification systems for subtasks A, B, C and D. Our approach consists of two steps.
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.
On a Topic Model for Sentences
Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them.
