Index of papers in Proc. ACL 2014 that mention
  • sequence labeling
Pei, Wenzhe and Ge, Tao and Chang, Baobao
Abstract
Despite Chinese word segmentation being a specific case, MMTNN can be easily generalized and applied to other sequence labeling tasks.
Introduction
Most previous systems address this problem by treating this task as a sequence labeling problem where each character is assigned a tag indicating its position in the word.
Introduction
(2011) developed the SENNA system that approaches or surpasses the state-of-the-art systems on a variety of sequence labeling tasks for English.
Introduction
0 Despite Chinese word segmentation being a specific case, our approach can be easily generalized to other sequence labeling tasks.
Max-Margin Tensor Neural Network
Despite tensor-based transformation being effective for capturing the interactions, introducing tensor-based transformation into neural network models to solve sequence labeling task is time prohibitive since the tensor product operation drastically slows down the model.
Related Work
The most popular approach treats word segmentation as a sequence labeling problem which was first proposed in Xue (2003).
Related Work
However, given the small size of their tensor matrix, they do not have the problem of high time cost and overfitting problem as we faced in modeling a sequence labeling task like Chinese word segmentation.
Related Work
By introducing tensor factorization into the neural network model for sequence labeling tasks, the model training and inference are speeded up and overfitting is prevented.
sequence labeling is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Ma, Ji and Zhang, Yue and Zhu, Jingbo
Introduction
While most previous work focus on in-domain sequential labelling or cross-domain classification tasks, we are the first to learn representations for web-domain structured prediction.
Learning from Web Text
This may partly be due to the fact that unlike computer vision tasks, the input structure of POS tagging or other sequential labelling tasks is relatively simple, and a single nonlinear layer is enough to model the interactions within the input (Wang and Manning, 2013).
Related Work
Regarding using neural networks for sequential labelling , our approach shares similarity with that of Collobert et al.
sequence labeling is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: