Word Combination Kernel for Text Classification with Support Vector Machines

In this paper we propose a novel kernel for text categorization. This kernel is an inner product defined in the feature space generated by all word combinations of specified length. A word combination is a collection of unique words co-occurring in the same sentence. The word combination of length k is weighted by the k rm th root of the product of the inverse document frequencies (IDF) of its words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the feature dimensions of documents can be reduced significantly to improve the sparseness of feature representations. By restricting the words to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. A computationally simple and efficient algorithm was proposed to calculate this kernel. We conducted a series of experiments on the Reuters-21578 and 20 Newsgroups datasets. This kernel achieves better performance than the word kernel and word-sequence kernel. We also evaluated the computing efficiency of this kernel and observed the impact of the word combination length on performance

Gaussian Processes for Text Regression

Text Regression is the task of modelling and predicting numerical indicators or response variables from textual data. It arises in a range of different problems, from sentiment and emotion analysis to text-based forecasting. Most models in the literature apply simple text representations such as bag-of-words and predict response variables in the form of point estimates. These simplifying assumptions ignore important information coming from the data such as the underlying uncertainty present in the outputs and the linguistic structure in the textual inputs. The former is particularly important when the response variables come from human annotations while the latter can capture linguistic phenomena that go beyond simple lexical properties of a text. In this thesis our aim is to advance the state-of-the-art in Text Regression by improving these two aspects, better uncertainty modelling in the response variables and improved text representations. Our main workhorse to achieve these goals is Gaussian Processes (GPs), a Bayesian kernelised probabilistic framework. GP-based regression models the response variables as well-calibrated probability distributions, providing additional information in predictions which in turn can improve subsequent decision making. They also model the data using kernels, enabling richer representations based on similarity measures between texts. To be able to reach our main goals we propose new kernels for text which aim at capturing richer linguistic information. These kernels are then parameterised and learned from the data using efficient model selection procedures that are enabled by the GP framework. Finally we also capitalise on recent advances in the GP literature to better capture uncertainty in the response variables, such as multi-task learning and models that can incorporate non-Gaussian variables through the use of warping functions. Our proposed architectures are benchmarked in two Text Regression applications: Emotion Analysis and Machine Translation Quality Estimation. Overall we are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions. Furthermore we show how these models can be probed to obtain insights about the relation between the data and the response variables and also how to apply predictive distributions in subsequent decision making procedures

Gaussian Processes for Text Regression

Text Regression is the task of modelling and predicting numerical indicators or response variables from textual data. It arises in a range of different problems, from sentiment and emotion analysis to text-based forecasting. Most models in the literature apply simple text representations such as bag-of-words and predict response variables in the form of point estimates. These simplifying assumptions ignore important information coming from the data such as the underlying uncertainty present in the outputs and the linguistic structure in the textual inputs. The former is particularly important when the response variables come from human annotations while the latter can capture linguistic phenomena that go beyond simple lexical properties of a text. In this thesis our aim is to advance the state-of-the-art in Text Regression by improving these two aspects, better uncertainty modelling in the response variables and improved text representations. Our main workhorse to achieve these goals is Gaussian Processes (GPs), a Bayesian kernelised probabilistic framework. GP-based regression models the response variables as well-calibrated probability distributions, providing additional information in predictions which in turn can improve subsequent decision making. They also model the data using kernels, enabling richer representations based on similarity measures between texts. To be able to reach our main goals we propose new kernels for text which aim at capturing richer linguistic information. These kernels are then parameterised and learned from the data using efficient model selection procedures that are enabled by the GP framework. Finally we also capitalise on recent advances in the GP literature to better capture uncertainty in the response variables, such as multi-task learning and models that can incorporate non-Gaussian variables through the use of warping functions. Our proposed architectures are benchmarked in two Text Regression applications: Emotion Analysis and Machine Translation Quality Estimation. Overall we are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions. Furthermore we show how these models can be probed to obtain insights about the relation between the data and the response variables and also how to apply predictive distributions in subsequent decision making procedures

Modèle de traduction statistique à fragments enrichi par la syntaxe

Traditional Statistical Machine Translation models are not aware of linguistic structure. Thus, target lexical choices and word order are controlled only by surface-based statistics learned from the training corpus. However, knowledge of linguistic structure can be beneficial since it provides generic information compensating data sparsity. The purpose of our work is to study the impact of syntactic information while preserving the general framework of Phrase-Based SMT. First, we study the integration of syntactic information using a reranking approach. We define features measuring the similarity between the dependency structures of source and target sentences, as well as features of linguistic coherence of the target sentences. The importance of each feature is assessed by learning their weights through a Structured Perceptron Algorithm. The evaluation of several reranking models shows that these features often improve the quality of translations produced by the basic model, in terms of manual evaluations as opposed to automatic measures. Then, we propose different models in order to increase the quality and diversity of the search graph produced by the decoder, through filtering out uninteresting hypotheses based on the source syntactic structure. This is done either by learning limits on the phrase recordering, or by decomposing the source sentence in order to simplify the translation process. The initial evaluations of these models look promising.Les modèles de traduction automatique probabiliste traditionnel ignorent la structure syntaxique des phrases source et cible. Le choix des unités lexicales cible et de leur ordre est contrôlé uniquement par des statistiques de surface sur le corpus d'entraînement. La connaissance de la structure linguistique peut-être bénéfique, car elle fournit des informations génériques compensant la pauvreté des données directement observables. Nos travaux ont pour but d'étudier l'impact des informations syntaxiques sur un modèle de traduction probabiliste de base, fondé sur des fragments, dans le cadre d'un analyseur dépendanciel particulier, XIP, dont la performance est bien adaptée à nos besoins. Nous étudions d'abord l'intégration des informations syntaxiques dans un but de reclassement des traductions proposées par le modèle de base? Nous définissons un ensemble de traits mesurant la similarité entre les structures de dépendance source et cible, et des traits de cohérence linguistique (basés sur l'analyse cible). L'apprentissage automatique des poids de ces traits permet de détecter leurs importance. L'évaluation manuelle des différents modèles de reclassement nous a permis de montrer le potentiel de ces traits pour améliorer la qualité des traductions proposées par le modèle de base. Ensuite, nous avons proposé un modèle pour réduire la taille du graphe des hypothèses exploré par le modèle de base à l'aide de connaissances sur la structure syntaxique source. Nous avons également proposé une procédure de décomposition d'une phrase source initiale en sous-phrases pour simplifier la tâche de traduction. Les évaluations initiales de ces modèles se sont montrées prometteuses