Discriminative Sequence Labeling by Z-Score Optimization
نویسندگان
چکیده
We consider a new discriminative learning approach to sequence labeling based on the statistical concept of the Z -score. Given a training set of pairs of hidden-observed sequences, the task is to determine some parameter values such that the hidden labels can be correctly reconstructed from observations. Maximizing the Z -score appears to be a very good criterion to solve this problem both theoretically and empirically. We show that the Z -score is a convex function of the parameters and it can be efficiently computed with dynamic programming methods. In addition to that, the maximization step turns out to be solvable by a simple linear system of equations. Experiments on artificial and real data demonstrate that our approach is very competitive both in terms of speed and accuracy with respect to previous algorithms.
منابع مشابه
Discriminative Learning of Probabilistic Sequence Models for Sequence Labeling Problems
The problem of labeling (or segmenting) sequences is very important in many applications such as part-of-speech tagging in natural language processing, multimodal object detection in computer vision, and DNA/protein structure prediction in bioinformatics. Conditional Random Fields (CRFs) of [1] are known to be the best sequence models ever for the problem. CRF is a conditional model, P (s|y), i...
متن کاملLearning Discriminative Relational Features for Sequence Labeling
Discovering relational structure between input features in sequence labeling models has shown to improve their accuracy in several problem settings. However, the search space of relational features is exponential in the number of basic input features. Consequently, approaches that learn relational features, tend to follow a greedy search strategy. In this paper, we study the possibility of opti...
متن کاملAn Empirical Evaluation of Sequence-Tagging Trainers
The task of assigning label sequences to a set of observed sequences is common in computational linguistics. Several models for sequence labeling have been proposed over the last few years. Here, we focus on discriminative models for sequence labeling. Many batch and online (updating model parameters after visiting each example) learning algorithms have been proposed in the literature. On large...
متن کاملSemi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach. We define the objective function of our hybrid model, which is written in log-linear form, by discriminatively combining discriminative structured predictor(s) with generative model(s) that incorporate unlabeled data....
متن کاملPassive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets
Recognising entities in social media text is difficult. NER on newswire text is conventionally cast as a sequence labeling problem. This makes implicit assumptions regarding its textual structure. Social media text is rich in disfluency and often has poor or noisy structure, and intuitively does not always satisfy these assumptions. We explore noise-tolerant methods for sequence labeling and ap...
متن کامل