arXiv subjects

Massih-Reza Amini

Publications and source records attributed to Massih-Reza Amini.

45 records · Page 3Linked to original sources

arXiv2017

CAp 2017 challenge: Twitter Named Entity Recognition

The paper describes the CAp 2017 challenge. The challenge concerns the problem of Named Entity Recognition (NER) for tweets written in French. We first present the data preparation steps we followed for constructing the dataset released in the framework of the challenge. We begin by demonstrating why NER for tweets is a challenging problem especially when the number of entities increases. We detail the annotation process and the necessary decisions we made. We provide statistics on the inter-annotator agreement, and we conclude the data description part with examples and statistics for the data. We, then, describe the participation in the challenge, where 8 teams participated, with a focus on the methods employed by the challenge participants and the scores achieved in terms of F$_1$ measure. Importantly, the constructed dataset comprising $\sim$6,000 tweets annotated for 13 types of entities, which to the best of our knowledge is the first such dataset in French, is publicly available at \url{http://cap2017.imag.fr/competition.html} .

cs.CL↗

arXiv2017

PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach

We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main theoretical result is a generalization bound on the risk of the majority vote which exhibits a term of diversity in the predictions of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection.

stat.ML↗

arXiv2017

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. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.

cs.IR↗

arXiv2017

An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges

In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parallel on its local function and updates a shared parameter asynchronously. In this way, all machines can continuously work even though they do not have the latest version of the shared parameter. We prove the convergence of the consistency of this general distributed asynchronous method for gradient iterations then show its efficiency on the matrix factorization problem for recommender systems and on binary classification.

stat.ML↗

arXiv2016

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. Such representations have been used in a plethora of tasks, however their application in classification scenarios with thousands of classes has not been extensively researched, partially due to hardware limitations. In this work, we examine efficient composition functions to obtain document-level from word-level embeddings and we subsequently investigate their combination with the traditional one-hot-encoding representations. By presenting empirical evidence on large, multi-class, multi-label classification problems, we demonstrate the efficiency and the performance benefits of this combination.

cs.CL↗

arXiv2016

TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification

This paper describes the participation of the team "TwiSE" in the SemEval 2016 challenge. 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. In the first step, we generate and validate diverse feature sets for twitter sentiment evaluation, inspired by the work of participants of previous editions of such challenges. In the second step, we focus on the optimization of the evaluation measures of the different subtasks. To this end, we examine different learning strategies by validating them on the data provided by the task organisers. For our final submissions we used an ensemble learning approach (stacked generalization) for Subtask A and single linear models for the rest of the subtasks. In the official leaderboard we were ranked 9/35, 8/19, 1/11 and 2/14 for subtasks A, B, C and D respectively.\footnote{We make the code available for research purposes at \url{https://github.com/balikasg/SemEval2016-Twitter\_Sentiment\_Evaluation}.}

cs.CL↗

arXiv2016

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. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as sentences, contains much information which is generally lost with these models. In this paper, we propose sentenceLDA, an extension of LDA whose goal is to overcome this limitation by incorporating the structure of the text in the generative and inference processes. We illustrate the advantages of sentenceLDA by comparing it with LDA using both intrinsic (perplexity) and extrinsic (text classification) evaluation tasks on different text collections.

cs.CL↗

arXiv2015

Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions

The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoretical guarantees that can be expected on the generalization capacity of the classifier associated to a given metric. The theoretical framework of $(ε, γ, τ)$-good similarity functions (Balcan et al., 2008) has been one of the first attempts to draw a link between the properties of a similarity function and those of a linear classifier making use of it. In this paper, we extend and complete this theory by providing a new generalization bound for the associated classifier based on the algorithmic robustness framework.

cs.LG↗

arXiv2015

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). This paper describes the dataset that have been released along the LSHTC series. The paper details the construction of the datsets and the design of the tracks as well as the evaluation measures that we implemented and a quick overview of the results. All of these datasets are available online and runs may still be submitted on the online server of the challenges.

cs.IR↗