Transition-Based Neural Word Segmentation

Abstract

Character-based and word-based methods are two main types of statistical models for Chinese word segmentation, the former exploiting sequence labeling models over characters and the latter typically exploiting a transition-based model, with the advantages that word-level features can be easily utilized. Neural models have been exploited for character-based Chinese word segmentation, giving high accuracies by making use of external character embeddings, yet requiring less feature engineering. In this paper, we study a neural model for word-based Chinese word segmentation, by replacing the manuallydesigned discrete features with neural features in a word-based segmentation framework. Experimental results demonstrate that word features lead to comparable performances to the best systems in the literature, and a further combination of discrete and neural features gives top accuracies.

Neural Word Segmentation Learning for Chinese

“…But both of them rely heavily on massive handcrafted features. Contemporary to this work, some neural models (Zhang et al, 2016a;Liu et al, 2016) Another notable exception is (Ma and Hinrichs, 2015), which is also an embedding-based model, but models CWS as configuration-action matching. However, again, this method only uses the context information within limited sized windows.…”

Transition-Based Disfluency Detection using LSTMs

“…Recently, deep learning methods have been widely used in many nature language processing tasks, such as name entity recognition (Lample et al, 2016), zero pronoun resolution (Yin et al, 2017) and word segmentation (Zhang et al, 2016). The effectiveness of neural features has also been studied for this framework Watanabe and Sumita, 2015;Andor et al, 2016).…”

Natural Language Processing with Small Feed-Forward Networks

“…The neural network with its non-linearity is in theory able to learn bigrams by conjoining unigrams, but it has been 4 Our calculation of BTS FLOPs is very conservative and favorable to BTS, as detailed in the supplementary material. shown that explicitly using character bigram features leads to better accuracy (Zhang et al, 2016;Pei et al, 2014). Zhang et al (2016) suggests that embedding manually specified feature conjunctions further improves accuracy ('Zhang et al (2016)-combo' in Table 4).…”