This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for
neural sequence labeling in natural language processing. Based on conventional
conditional random fields (CRFs), SCRFs have been designed for the tasks of
assigning labels to segments by extracting features from and describing
transitions between segments instead of words. In this paper, we improve the
existing SCRF methods by employing word-level and segment-level information
simultaneously. First, word-level labels are utilized to derive the segment
scores in SCRFs. Second, a CRF output layer and an SCRF output layer are
integrated into an unified neural network and trained jointly. Experimental
results on CoNLL 2003 named entity recognition (NER) shared task show that our
model achieves state-of-the-art performance when no external knowledge is used.

本文提出基于条件随机场的混合半马尔可夫模型，应用于自然语言处理中的神经序列标注任务，该模型采用词级别和片段级别的信息同时作为特征来提升性能，在 CoNLL 2003 命名实体识别任务中取得了最好的结果。

混合半 - Markov CRF 用于神经序列标记

Hybrid semi-Markov CRF for Neural Sequence Labeling

We study the problem of recognizing video sequences of fingerspelled letters
in American Sign Language (ASL). Fingerspelling comprises a significant but
relatively understudied part of ASL. Recognizing fingerspelling is challenging
for a number of reasons: It involves quick, small motions that are often highly
coarticulated; it exhibits significant variation between signers; and there has
been a dearth of continuous fingerspelling data collected. In this work we
collect and annotate a new data set of continuous fingerspelling videos,
compare several types of recognizers, and explore the problem of signer
variation. Our best-performing models are segmental (semi-Markov) conditional
random fields using deep neural network-based features. In the signer-dependent
setting, our recognizers achieve up to about 92% letter accuracy. The
multi-signer setting is much more challenging, but with neural network
adaptation we achieve up to 83% letter accuracies in this setting.

研究美国手语中指拼字字母的视频序列识别问题，并通过收集和注释新的连续指拼视频数据集，比较多种类型的识别器，探索签名者差异问题。基于深度神经网络的特征，我们的半马尔可夫条件随机场模型在签名者依赖和多签名者情景下均取得较高的字母识别准确率。