This paper introduces our SAU-KERC system that achieved F1 score of 0.39 in the world-level quality estimation task in WMT2015. The goal is to assign each translated word a “OK” or “BAD” label indicating translation quality. We adopt the sequence labeling model, conditional random fields (CRF), to predict the labels. Since “BAD” labels are rare in the training and development sets, recognition ...

Dans cet article, nous proposons une approche de conversion graphème-phonème pour les noms propres. L'approche repose sur une méthode probabiliste : les Champs Aléatoires Conditionnels (Conditional Random Fields, CRF). Les CRFs donnent une prévision à long terme, n'exigent pas l'indépendance des observations et permettent l'intégration de tags. Dans nos travaux antérieurs, l'approche de convers...

In order to build a simulated robot that accepts instructions in unconstrained natural language, a corpus of 427 route instructions was collected from human subjects in the office navigation domain. The instructions were segmented by the steps in the actual route and labeled with the action taken in each step. This flat formulation reduced the problem to an IE/Segmentation task, to which we app...

paper presents the identification of clause boundary for the Urdu language. We have used Conditional Random Field as the classification method and the clause markers. The clause markers play the role to detect the type of subordinate clause, which is with or within the main clause. If there is any misclassification after testing with different sentences then more rules are identified to get hig...

Making speech recognition output readable is an important task. The first step here is automatic sentence end detection (SED). We introduce novel F0 derivative-based features and sentence end distance features for SED that yield significant improvements in slot error rate (SER) in a multi-pass framework. Three different SED approaches are compared on a spoken lecture task: hidden event language...

This paper describes our approach to tackling the task of Technical Trend Map Creation as posed in NTCIR-8. The basic method is Conditional Random Fields, which is considered as the most advanced method in Named Entity Recognition. In order to improve the performance, we further resort a tag modification approach and pattern-based method. Our system performed competitively, achieving the top F-...

In this paper we describe the SpRL-CWW entry into SemEval 2015: Task 8 SpaceEval. It detects spatial and motion relations as defined by the ISO-Space specifications in two phases: (1) it detects spatial elements and spatial/motion signals with a Conditional Random Field model that uses a combination of distributed word representations and lexicosyntactic features; (2) given relation candidate t...

We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete and real-valued variables. Our bounds apply to composite likelihood [14], which generalizes maximum likelihood and pseudolikelihood [3]. Moreover, we show that the only existing algorithm with a PAC bound for learning high-treewidth discrete models [1] can be viewed as...

This work studies the usefulness of syntactic information in the context of automatic dialogue act recognition in Czech. Several pieces of evidence are presented in this work that support our claim that syntax might bring valuable information for dialogue act recognition. In particular, a parallel is drawn with the related domain of automatic punctuation generation and a set of syntactic featur...