Weak Disambiguation for Partial Structured Output Learning
| Autor: | Xiaolei Lu, Tommy W. S. Chow |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Computer science business.industry Process (computing) computer.software_genre Sequence labeling Machine Learning (cs.LG) Computer Science Applications Human-Computer Interaction Text mining Control and Systems Engineering Margin (machine learning) Piecewise Learning Data mining Electrical and Electronic Engineering business Computation and Language (cs.CL) computer Algorithms Software Natural Language Processing Information Systems |
| Popis: | Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a novel weak disambiguation for partial structured output learning (WD-PSL). First, a piecewise large margin formulation is generalized to partial structured output learning, which effectively avoids handling a large number of candidate-structured outputs for complex structures. Second, in the proposed weak disambiguation strategy, each candidate label is assigned with a confidence value indicating how likely it is the true label, which aims to reduce the negative effects of wrong ground-truth label assignment in the learning process. Then, two large margins are formulated to combine two types of constraints which are the disambiguation between candidates and noncandidates, and the weak disambiguation for candidates. In the framework of alternating optimization, a new 2n -slack variables cutting plane algorithm is developed to accelerate each iteration of optimization. The experimental results on several sequence labeling tasks of natural language processing show the effectiveness of the proposed model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|