From Word Segmentation to POS Tagging for Vietnamese

From Word Segmentation to POS Tagging for Vietnamese

Dat Quoc Nguyen, Thanh Vu, Dai Quoc Nguyen, Mark Dras    Mark Johnson
Department of Computing, Macquarie University, Australia
{dat.nguyen, mark.dras, mark.johnson}@mq.edu.au
NIHR Innovation Observatory, Newcastle University, United Kingdom
thanh.vu@newcastle.ac.uk
PRaDA Centre, Deakin University, Australia
dai.nguyen@deakin.edu.au
Abstract

This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable. We also make a comparison between state-of-the-art (SOTA) feature-based and neural network-based models. On the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that the pipeline strategy produces better scores of POS tagging from unsegmented text than the joint strategy, and the highest accuracy is obtained by using a feature-based model.

From Word Segmentation to POS Tagging for Vietnamese


Dat Quoc Nguyen, Thanh Vu, Dai Quoc Nguyen, Mark Drasand Mark Johnson Department of Computing, Macquarie University, Australia {dat.nguyen, mark.dras, mark.johnson}@mq.edu.au NIHR Innovation Observatory, Newcastle University, United Kingdom thanh.vu@newcastle.ac.uk PRaDA Centre, Deakin University, Australia dai.nguyen@deakin.edu.au

1 Introduction

POS tagging is one of the most fundamental natural language processing (NLP) tasks. In English where white space is a strong indicator of word boundaries, POS tagging is an important first step towards many other NLP tasks. However, white space when written in Vietnamese is also used to separate syllables that constitute words. So for Vietnamese NLP, word segmentation is referred to as the key first step (Dien et al., 2001).

When applying POS tagging to real-world Vietnamese text where gold word-segmentation is not available, the pipeline strategy is to first segment the text by using a word segmenter, and then feed the word-segmented text—which is the output of the word segmenter—as the input to a POS tagger. For example, given a written text “thuế thu nhập cá nhân” (individualcá_nhân incomethu_nhập taxthuế) consisting of 5 syllables, the word segmenter returns a two-word phrase “thuế_thu_nhập cá_nhân.”111In the traditional underscore-based representation in Vietnamese word segmentation (Nguyen et al., 2009), white space is only used to separate words while underscore is used to separate syllables inside a word. Then given the input segmented text “thuế_thu_nhập cá_nhân”, the POS tagger returns “thuế_thu_nhập/N cá_nhân/N.”

A class of approaches to POS tagging from unsegmented text that has been actively explored in other languages, such as in Chinese and Japanese, is joint word segmentation and POS tagging (Zhang and Clark, 2008). A possible joint strategy is to assign a combined segmentation and POS tag to each syllable (Kruengkrai et al., 2009). For example, given the input text “thuế thu nhập cá nhân”, the joint strategy would produce “thuế/B-N thu/I-N nhập/I-N cá/B-N nhân/I-N”, where B refers to the beginning of a word and I refers to the inside of a word. Shao et al. (2017) showed that this joint strategy gives SOTA results for Chinese POS tagging by utilizing a BiLSTM-CNN-CRF model (Ma and Hovy, 2016).

In this paper, we present the first empirical study comparing the joint and pipeline strategies for Vietnamese POS tagging from unsegmented text. In addition, we make a comparison between SOTA feature-based and neural network-based models, which, to the best of our knowledge, has not done in any prior work on Vietnamese. On the benchmark Vietnamese treebank (Nguyen et al., 2009), we show that the pipeline strategy produces better scores than the joint strategy. We also show that the highest tagging accuracy is obtained by using a traditional feature-based model rather than neural network-based models.

2 Related work

2.1 Word segmentation

Nguyen et al. (2006), Dinh and Vu (2006) and Tran et al. (2010) considered the Vietnamese word segmentation task as a sequence labeling task, using either a CRF, SVM or MaxEnt model to assign each syllable a segmentation tag such as B or I. In addition, Le et al. (2008), Pham et al. (2009) and Tran et al. (2012) used the maximum matching method (NanYuan and YanBin, 1991) to generate all possible segmentations for each input sentence; then to select the best segmentation, Le et al. (2008) and Tran et al. (2012) applied n-gram language model while Pham et al. (2009) employed POS information from an external POS tagger. Later, Liu and Lin (2014) and Nguyen and Le (2016) proposed approaches based on pointwise prediction, where a binary classifier is trained to identify whether or not there is a word boundary at each point between two syllables. Furthermore, Nguyen et al. (2017b) proposed a rule-based approach which gets the highest results to date in terms of both segmentation accuracy and speed.

2.2 POS tagging

Regarding Vietnamese POS tagging, Dien and Kiem (2003) projected POS annotations from English to Vietnamese via a bilingual corpus of word alignments. As a standard sequence labeling task, previous research has applied the CRF, SVM or MaxEnt model to assign each word a POS tag (Nghiem et al., 2008; Tran et al., 2009; Le-Hong et al., 2010; Nguyen et al., 2010; Tran et al., 2010; Bach et al., 2013). In addition, Nguyen et al. (2011) proposed a rule-based approach to automatically construct transformation rules for POS tagging in the form of a Ripple Down Rules tree (Compton and Jansen, 1990), leading to a development of the RDRPOSTagger (Nguyen et al., 2014a) which was the best system for the POS tagging shared task at the 2013 Vietnamese Language and Speech Processing (VLSP) workshop.

Nguyen et al. (2016a) and Nguyen et al. (2016b) later showed that SOTA accuracies at 94+% in the Vietnamese POS tagging task are obtained by simply retraining existing English POS taggers on Vietnamese data, showing that the MarMoT tagger (Mueller et al., 2013) and the Stanford POS tagger (Toutanova et al., 2003) obtain higher accuracies than RDRPOSTagger. Nguyen et al. (2016a) also showed that a simple lexicon-based approach assigning each word by its most probable POS tag gains a promising accuracy at 91%. Note that both Nguyen et al. (2016a) and Nguyen et al. (2016b) did not experiment with neural network models. Pham et al. (2017) recently applied the BiLSTM-CNN-CRF (Ma and Hovy, 2016) for Vietnamese POS tagging, however, they did not experiment with SOTA feature-based models.

Previously, only Takahashi and Yamamoto (2016) carried out joint word segmentation and POS tagging for Vietnamese, to predicting a combined segmentation and POS tag to each syllable. In particular, Takahashi and Yamamoto (2016) experimented with traditional SVM- and CRF-based toolkits on a dataset of about 7k sentences and reported results of joint prediction only, i.e., they did not compare to the pipeline strategy. The CoNLL 2017 shared task on Universal Dependencies (UD) parsing from raw text (Zeman et al., 2017) provided some results to the pipeline strategy from word segmentation to POS tagging, however, the Vietnamese dataset in the UD project is very small, consisting of 1,400 training sentences. Furthermore, Nguyen et al. (2017a) provided a pre-trained jPTDP model for joint POS tagging and dependency parsing for Vietnamese,222https://drive.google.com/drive/folders/0B5eBgc8jrKtpUmhhSmtFLWdrTzQ which obtains a tagging accuracy at 93.0%, a UAS score at 77.7% and a LAS score at 69.5% when evaluated on the Vietnamese dependency treebank VnDT of 10k sentences (Nguyen et al., 2014b).

3 Experimental methodology

We compare the joint word segmentation and POS tagging strategy to the pipeline strategy on the benchmark Vietnamese treebank (Nguyen et al., 2009) using well-known POS tagging models.

3.1 Joint segmentation and POS tagging

Following Kruengkrai et al. (2009), Takahashi and Yamamoto (2016) and Shao et al. (2017), we formalize the joint word segmentation and POS tagging problem for Vietnamese as a sequence labeling task to assigning a combined segmentation and POS tag to each syllable. For example, given a manually POS-annotated training corpus “Cuộc/Nc điều_tra/V dường_nhÆ°/X không/R tiến_triển/V ./CH” ‘The investigation seems to be making no progress’, we transform this corpus into a syllable-based representation as follows: “Cuộc/B-Nc điều/B-V tra/I-V dường/B-X nhÆ°/I-X không/B-R tiến/B-V triển/I-V ./B-CH”, where segmentation tags B and I denote beginning and inside of a word, respectively, while Nc, V, X, R and CH are POS tags. Then we train sequence labeling models on the syllable-based transformed corpus.

3.2 Dataset

The Vietnamese treebank (Nguyen et al., 2009) is the largest annotated corpus for Vietnamese, providing a set of 27,870 manually POS-annotated sentences for training and development (about 23 words per sentence on average) and a test set of 2120 manually POS-annotated sentences (about 31 words per sentence).333The data was officially used for the Vietnamese POS tagging shared task at the second VLSP 2013 workshop. From the set of 27,870 sentences, we use the first 27k sentences for training and the last 870 sentences for development.

3.3 Models

For both joint and pipeline strategies, we use the following models:

  • RDRPOSTagger (Nguyen et al., 2014a) is a transformation rule-based learning model which obtained the highest accuracy at the VLSP 2013 POS tagging shared task.444http://rdrpostagger.sourceforge.net

  • MarMoT (Mueller et al., 2013) is a generic CRF framework and a SOTA POS and morphological tagger.555http://cistern.cis.lmu.de/marmot

  • BiLSTM-CRF (Huang et al., 2015) is a sequence labeling model which extends the BiLSTM model with a CRF layer.

  • BiLSTM-CRF + CNN-char, i.e. BiLSTM-CNN-CRF, is an extension of the BiLSTM-CRF, using CNN to derive character-based representations (Ma and Hovy, 2016).

  • BiLSTM-CRF + LSTM-char is another extension of the BiLSTM-CRF, using BiLSTM to derive the character-based representations (Lample et al., 2016).

Here, for the pipeline strategy, we train these models to predict POS tags with respect to (w.r.t.) gold word segmentation. In addition, we also retrain the fast and accurate Vietnamese word segmenter RDRsegmenter (Nguyen et al., 2017b) using the training set of 27k sentences.666RDRsegmenter obtains a segmentation speed at 60k words per second, computed on a personal computer of Intel Core i7 2.2 GHz. RDRsegmenter is available at: https://github.com/datquocnguyen/RDRsegmenter

3.4 Implementation details

We use the original pure Java implementations of RDRPOSTagger and MarMoT with default hyper-parameter settings in our experiments. Instead of using implementations independently provided by authors of BiLSTM-CRF, BiLSTM-CRF + CNN-char777https://github.com/XuezheMax/LasagneNLP and BiLSTM-CRF + LSTM-char, we use a reimplementation which is optimized for performance of all these models from Reimers and Gurevych (2017).888https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf

For three BiLSTM-CRF-based models, we use default hyper-parameters provided by Reimers and Gurevych (2017) with the following exceptions: we use a dropout rate at 0.5 (Ma and Hovy, 2016) with the frequency threshold of 5 for unknown word and syllable types. We initialize word and syllable embeddings with 100-dimensional pre-trained embeddings,999Pre-trained word and syllable embeddings are learned by training the Word2Vec Skip-gram model (Mikolov et al., 2013) on a Vietnamese news corpus which is available at: http://mim.hus.vnu.edu.vn/phuonglh/corpus/baomoi.zip then learn them together with other model parameters during training by using Nadam (Dozat, 2016). For training, we run for 100 epochs. We perform a grid search of hyper-parameters to select the number of BiLSTM layers from and the number of LSTM units in each layer from . Early stopping is applied when no performance improvement on the development set is obtained after 5 contiguous epochs. For both pipeline and joint strategies, we find the highest performance on the development set is when using two stacked BiLSTM layers. Table 1 presents the optimal number of LSTM units.

Model Pipeline Joint
BiLSTM-CRF 100 200
     + CNN-char 100 250
     + LSTM-char 150 250
Table 1: Optimal number of LSTM units.

Here the performance is evaluated by F1 score, based on the number of correctly segmented and tagged words (Zhang and Clark, 2008). In the case of gold word segmentation, F1 score for POS tagging is in fact the tagging accuracy.

4 Main results

Table 2 presents POS tagging accuracy and tagging speed of each model on the test set w.r.t. gold word segmentation, in which MarMoT is the most accurate model while RDRPOSTagger is the fastest one. In particular, MarMoT obtains 0.5%+ higher accuracy than the three BiLSTM-based models. This is not surprising as the training set of 27k sentences is relatively small compared to the training data available in other languages such as English or Chinese.

Table 3 presents F1 scores for word segmentation and POS tagging in a real-world application scenario where the gold word-segmentation is not available. Comparing the results in Table 2 to results for the pipeline strategy, we observe a drop of about 2% for all models when using predicted segmentation instead of gold segmentation. Also, Table 3 clearly shows that the pipeline strategy helps produce better results than the joint strategy. In addition, pre-designed features in both RDRPOSTagger and MarMoT are designed to capture word-level information rather than syllable-level information, so it is also not surprising that for the joint strategy RDRPOSTagger is significantly lower while MarMoT is lower than the BiLSTM-CRF model with additional character-based representations.

Model Accuracy Speed
RDRPOSTagger 95.11 180k
MarMoT 95.88 25k
BiLSTM-CRF 95.06 3k
     + CNN-char 95.40 2.5k
     + LSTM-char 95.31 1.5k
Table 2: POS tagging accuracies (in %) on the test set w.r.t. gold word segmentation. “Speed” denotes the tagging speed, i.e. the number of words per second, computed on a personal computer of Intel Core i7 2.2 GHz (model loading time is not taken into account).
Model WSeg PTag

Pipeline

RDRPOSTagger 97.75 93.39
MarMoT 97.75 93.96
BiLSTM-CRF 97.75 93.25
     + CNN-char 97.75 93.55
     + LSTM-char 97.75 93.46

Joint

RDRPOSTagger 93.73 87.53
MarMoT 96.50 92.78
BiLSTM-CRF 96.15 92.43
     + CNN-char 96.66 92.79
     + LSTM-char 96.76 92.95
Table 3: F1 scores (in %) for word segmentation (WSeg) and POS tagging (PTag) from unsegmented text. The pipeline strategy uses RDRsegmenter for word segmentation. In preliminary experiments, where we also train the five models above to predict a segmentation tag B or I for each syllable, we then find that RDRsegmenter obtains better word segmentation score than those five models.

Tables 2 and 3 suggest that for a practical application to Vietnamese where performance accuracy is preferred, we should consider using the pipeline strategy with a traditional SOTA feature-based tagger such as MarMoT. If speed is preferred such as in big data, RDRPOSTagger would be a superior alternative. With the current state of training data available in Vietnamese, future research should focus on incorporating Vietnamese linguistic features into the traditional feature-based sequence taggers.

5 Conclusion

We have presented empirical comparisons between two strategies for Vietnamese POS tagging from unsegmented text and between SOTA feature- and neural network-based models. Experimental results on the benchmark Vietnamese treebank (Nguyen et al., 2009) show that the pipeline strategy produces higher scores of POS tagging from unsegmented text than the joint strategy. In addition, we also show that a traditional feature-based model (i.e. MarMoT) obtains better POS tagging accuracy than neural network-based models. We provide a pre-trained MarMoT model for Vietnamese POS tagging at https://github.com/datquocnguyen/VnMarMoT.

Acknowledgments

This research was partially supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP160102156). This research was also partially supported by NICTA, funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.

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