Venue: NAACL 2019

# Neural Chinese Address Parsing

 Hao Li & Wei Lu

StatNLP Research Group

Singapore University of Technology and Design

hao_li@mymail.sutd.edu.sg chengchen.xpj@alibaba-inc.com

luwei@sutd.edu.sg linyan.ll1@alibaba-inc.com

Pengjun Xie & Linlin Li

DAMO Academy

Alibaba Group

changchen.xpj@alibaba-inc.com

linyan.lll@alibaba-inc.com

###### Abstract

This paper introduces a new task - Chinese address parsing - the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15,000 Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at [http://statnlp.org/research/sp](http://statnlp.org/research/sp).

## 1 Introduction

Addresses play an important role in modern society. They are typically used as identifiers to locations and entities in the world that can be used to facilitate various social activities, such as business correspondences, meetings and events. Recent research efforts show that systems that perform _address parsing_, the task of automatically parsing addresses into semantically meaningful structures, can be useful for tasks such as building e-commerce or product recommendation systems Jia et al. (2017); Avvenuti et al. (2018). Due to historical reasons, the English addresses come with a standardized format, mostly written in order from most specific to most general. Meaningful chunks in an English address are also separated by punctuation or the new-line symbols. Such characteristics make parsing English addresses a relatively easy task.

However, addresses written in eastern Asian languages such as Chinese present several unique challenges. Unlike English addresses, Chinese addresses are typically written in the form of a consecutive sequence of Chinese characters (possibly intermixed with digits and English letters). Figure 1 presents two example Chinese addresses and their desired output structures after parsing - chunks annotated with their labels indicating semantics (such as province, road, etc). The Chinese addressing system is also different from that of English. Though it is generally believed that the system uses the opposite ordering - starting from most general (e.g., province) and ending with most specific (e.g., room no.), in practice it can be observed that the format is far less rigorous than expected. The lack of rigor also leads to other issues - the addresses may come with incomplete, redundant or even inaccurate information, as we can see from the second example listed in Figure 1. Such unique challenges make the design of an effective Chinese address parser non-trivial.

Figure 1: Two example Chinese addresses and the expected structures after parsing. Each chunk is underlined with its corresponding label in blue.



Parsing a Chinese address into semantically meaningful structures can be regarded as a special type of chunking task (Abney, 1991), where we need to perform address-specific Chinese word segmentation (Xue, 2003; Peng et al., 2004; Zhao et al., 2006) while assigning a semantic label to each chunk. However, existing models designed for chunking may not be readily applicable in this task. Our observations show that there are a few characteristics associated with the task. We found that while generally there exists certain ordering information among the chunks of different labels in the addresses, such ordering information is better preserved among the chunks that appear at the beginning of the addresses. For the chunks appearing towards the end of the addresses, chunks of different types often appear in more flexible order.

On top of the above observations, we propose a specific model based on neural networks for the task of Chinese address parsing. The model is able to encode the regular patterns among chunks that appear at the beginning of a Chinese address, while flexibly capturing the irregular patterns and rich dependencies among the chunks of different types that appear towards the end of the address. This is achieved by designing a novel structured representation integrating both a linear structure and a latent-variable tree structure.

Our main contributions in this work can be summarized as follows:

* We create and publicly release a new corpus consisting of 15K Chinese address entries fully annotated with chunk boundaries and address labels. To the best of our knowledge, this is the first and largest annotated Chinese address corpus.
* We introduce a novel neural approach to Chinese address parsing with latent variables to flexibly capture both prior ordering information and rich dependencies among labels.
* Through extensive experiments, we demonstrate the effectiveness of our approach. The experimental results show that our approach outperforms several baselines significantly.

## 2 Data

In this work, we created a _Chinese Address_ corpus. To do so, we crawled a large number of publicly available addresses from the Chinese websites including online business directory websites (e.g., b2b.huangye88.com), social media websites (e.g., www.dianping.com), and an online API service translating a geo-location to a Chinese address (lbs.amap.com). In order to protect privacy, we discarded sensitive addresses (such as those involving military locations) and randomly altered the digits in the collected addresses.

Due to the lack of Chinese address standard format as well as complicated and different writing preferences in different regions (e.g., people living in southern China prefer the word "\(\hat{\mathcal{H}}\)" as the suffix of the name of a lane or sub-road over the word "\(\hat{\mathcal{H}}\)" which is widely used in northern China), we create an annotation guideline1 by summarizing different writing preferences. We proposed 21 chunk labels listed in Table 1. The meaning

\begin{table}
\begin{tabular}{l c r r r l l r l} Label & Order ID & UniqueF & Train & Dev & Test & Interpretation & Example \\ \hline country & 20 & 2 & 41 & 15 & 13 & _name of a country_ & \(\phi\) [China] \\ PROWINC & 19 & 65 & 3,794 & 1,317 & 1,265 & _name of a province_ & \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(G\) Zhejiang Province) \\ City & 18 & 377 & 4,824 & 1,662 & 1,613 & _name of a city_ & \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\)? (Beijing) \\ DISTECT & 17 & 1137 & 5,881 & 2,027 & 1,921 & _name of a district in a city_ & \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\) (Chawong District) \\ DEVEZONE & 16 & 297 & 330 & 118 & 107 & _name of an economic development zone_ & \(\tau\):: \# \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\) (Acquisition Development Zone) \\ TOWN & 15 & 2,382 & 3,972 & 1,300 & 1,308 & _name of a town or a boweled_ & \(\theta\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\) (Quasi Street) \\ COMPUNITY & 14 & 1,867 & 1,279 & 415 & 416 & _name of a community or a village_ & \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}}\) (Angulation Community) \\ ROAD & 13 & 5,037 & 5,410 & 1,788 & 1,801 & _name of a road_ & \(\vartheta\):: \& \(\hat{\mathcal{H}}\):: \& \(\hat{\mathcal{H}

 

of most labels can be inferred from their names. We hire 3 annotators to annotate chunk boundaries and chunk labels for each Chinese address following the annotation guideline. In order to maintain high annotation quality, we also hire 2 additional quality controllers to sample 20 sentences from each batch of 1,000 annotated sentences for human evaluation. Re-annotation for that batch will be performed should the accuracy of human evaluation fall below \(95\%\).

We randomly split the annotated data into 3 portions following the ratio of \(60\%\), \(20\%\), and \(20\%\), yielding training, development, and test sets. The complete statistics of our data can be found in Table 1. From the table we can observe that the chunk label poi (point of interest) occurs most frequently. Indeed, such a label has a high level of importance. This is because location-based information can be extracted from such chunks, which is crucial for recommendation services Gao et al. (2015); Xie et al. (2016). In addition, we report the number of distinctive chunks (unique#) that appear in the data for each label, from which we can see our corpus has a good coverage on province, city, and district2.

Footnote 2: We found these numbers are comparable with statistics on www.stats.gov.cn.

We empirically assign each label a order ID indicating its level of specificity. For example, the label country is used for describing a country, and is the most general concept. It is thus assigned the order ID 20, which is the highest among all labels. As another example, the label person gets assigned an order ID 3, as it is used to describe one of the most specific concepts. Such order ID information will be useful later when designing our models for Chinese address parsing.

## 3 Approach

Our objective is to design a model for parsing Chinese addresses into semantically meaningful structures in the form of consecutive chunks, where each chunk is assigned a label as described in the previous section. As we have mentioned before, we believe there exist Chinese address-specific characteristics associated with address texts that can be exploited in designing a parsing model. Specifically, we argue there are two types of structured information within Chinese addresses that can be exploited when designing our parser - the _latent tree structures_ and the _regular chain structures_. The former is used for capturing rich dependencies among chunks that appear towards the end of each address. The latter is used for capturing the structural patterns associated with chunks appearing at the beginning of each address.

### Latent Tree Structures

We focus our discussions on the latent tree structures first. Given a consecutive sequence of labeled chunks, we can construct a binary tree structure whose yield exactly corresponds to the sequence of labeled chunks. We build the latent tree structures to capture complex dependencies based on the observation that chunks appearing towards the end of a given address do not follow a rigorous order. For instance, as we can see in the second example in Figure 1, chunks towards the end of the address consist of some labels related to numbers as well as the label redundant. These labels are either optional or do not follow some regular patterns in terms of order, which makes capturing dependencies among labels challenging.

We first introduce _auxiliary labels_ based on the set of _original labels_ we defined in the previous section. Such auxiliary labels are assigned to the internal nodes within a parse tree. Specifically, for each original label x, we introduce the auxiliary label \(\overline{\text{x}}\). For example, the auxiliary label for road would be ROAD.

We now illustrate how a latent tree is constructed from a sequence of labeled chunks. These chunks will be regarded as a sequence of leaf nodes, each of which contains the corresponding chunk boundary and chunk type information. To simplify the construction process, we focus on building a specific type of binary trees with each non-leaf node containing at least 1 leaf node as one of its child.3 We start the process by selecting any chunk first as one leaf node. Next we take a chunk that is either on the left or on the right of the selected chunk as its binary sibling node, and create a parent node by assigning the two selected leaf nodes as child nodes. To determine the label of the newly created parent node, we choose the auxiliary label based on the label with a higher order ID between the two of the child nodes. The newly created parent node will replace the 2 child nodes in the sequence and now the parent node becomes a selected node. We repeat this construction pro 

cess until there is only 1 node left in the sequence. Note that the construction process makes use of the label order information.

Figure 2 shows an example tree that the gold chunks correspond to. From the example we can see that the non-leaf node label \(\overline{\textsc{poi}}\) that appears twice has connections to other non-leaf node labels such as \(\overline{\textsc{roadno}}\) and \(\overline{\textsc{poi}}\). Such tree structures will allow us to capture rich Chinese address-specific structural information among labels. Since there are many latent trees corresponding to the given address consisting of consecutive labeled chunks, the model is facilitated to learn such complicated patterns, which is potentially beneficial for the address parsing task.

### Regular Chain Structures

The latent tree structures allow complex dependencies between different chunks to be captured within a Chinese address. Such dependencies would be helpful when there exist irregular patterns within an address. However, if we believe there are regular patterns among the labeled chunks, using an alternative assumption on the dependencies to properly capture such patterns may be more desirable. For instance, the first example in Figure 1 illustrates a common regular pattern at the beginning of the address, which is the order of (province, city, district). This motivates us to employ an alternative representation for capturing dependencies within chunks that appear at the beginning of the addresses, which are believed to exhibit more regular patterns.

Specifically, we employ a chain structure to capture the dependencies between adjacent labeled chunks. For example, given a sequence of chunks, we may always consider a right-branching tree structure to connect all these chunks. The resulting structure will be able to capture first-order dependencies between adjacent labeled chunks, which allows the regular orders among the labels to be learned. For example, consider the first two chunks that appear within the address as illustrated in Figure 2. The first two chunks form a right-branching tree structure. The construction process for such chain structures is similar to that of the latent trees, except that there is a single fixed (right-branching tree) structure for given labeled chunks.

Based on the observation that regular patterns appear mostly at the beginning of an address, we define the space \(\mathcal{H}(x,y,sp)\) that consists of all latent tree structures that are consistent with the input character sequence \(x\), the gold labeled chunks \(y\) and \(sp\) which determines the split point. Formally, we define the split point of a given address as specified by \(sp\) as the left boundary of the rightmost chunk whose label order ID is larger than or equal to \(sp\). The split point divides the chunks into two groups - those appearing on the left of \(sp\) will form a chain structure while those on the right will form a tree structure where the correct construction is latent. Both structures are then merged to form a single representation, which is used for building our address parsing model.

Notice that when \(sp\) is set to \(-1\) (denoted as \(sp=\texttt{last}\)), the split point is on the right of the last chunk. In this case the latent structured space \(\mathcal{H}(x,y,sp)\) consists of only one single right-branching tree. On the other hand, when \(sp\) is set to its maximal value \(20\), the label order ID of country (denoted as \(sp=\texttt{country}\)), the latent structured space does not contain any structure that involves a partial regular chain component. Different values of \(sp\) leads to different interpolations between the two types of structural assumptions, resulting in different variants of our models. We will discuss the effect of different \(sp\) values in the experiments section.

### Chunk Representation

A parse tree corresponds to a collection of labeled chunks as leaves. We adopt a bi-directional LSTM over a given input to compute the span-level representation. At each position \(i\) in the orig

Figure 2: An example latent tree for given gold chunks where \(sp=\texttt{poi}\). The English translation is listed below each chunk. Leaf nodes are in gray, and the internal nodes are in pink (labeled with white auxiliary labels). The tree structure within the triangle is latent – we show one of the many possible structures for illustration only.

 

inal input consisting of a sequence of characters, we use \({\bf f}_{i}\) and \({\bf b}_{i}\) to denote the outputs of forward LSTM and backward LSTM respectively. We use \({\bf c}_{i,j}=[{\bf f}_{j}-{\bf f}_{i};{\bf b}_{i}-{\bf b}_{j}]\) to denote the vector representation of the span covering characters from position \(i\) to position \(j\)Wang and Chang (2016). Motivated by Stern et al. (2017), we define the label score as follows:

\[{\bf s}(i,j)=F({\bf c}_{i,j})\]

where \(F\) is a 2-layer feed-forward neural network with output dimension being the number of chunk labels. In addition, we denote the score of the span with a specific label \(l\) as the value of the \(l\)-th element in the vector \({\bf s}(i,j)\):

\[s(i,j,l)=[{\bf s}(i,j)]_{l} \tag{1}\]

### Model

Inspired by Stern et al. (2017), we build a chart-based parsing model. Unlike that work, however, our model involves latent structures as mentioned in Section 3.1. For a given sequence of labeled chunks, our model considers all possible constituent trees whose yield are exactly the labeled chunks.

Consider a tree \({\bf t}\) that can be represented by a set of labeled spans, where each span is uniquely defined by the boundary \((i,j)\) and the label \(l\):

\[{\bf t}:=\{(i_{n},j_{n},l_{n}):n=1,\ldots,|{\bf t}|\}. \tag{2}\]

The score of the tree \({\bf t}\) can be defined as follows:

\[\mathcal{S}({\bf t})=\sum_{(i,j,l)\in{\bf t}}[s(i,j,l)] \tag{3}\]

Similar to Stern et al. (2017), we use a CKY-style algorithm to calculate the score of the optimal sub-tree that spans the interval \((i,j)\) recursive using the following formula:

\[\begin{split}\pi(i,j)&=\max_{l}[s(i,j,l)]+\\ &\max_{k}\Big{\{}\max_{l}[s(i,k,l)]+\pi(k,j),\\ &\qquad\quad\pi(i,k)+\max_{l}[s(k,j,l)]\Big{\}}\end{split} \tag{4}\]

The base case is when the text span \((i,j)\) corresponds to a leaf node (a chunk) in the tree; in this case we have: \(\pi(i,j)=\max_{l}[s(i,j,l)]\).

### Training and Decoding

Inspired by the structural support vector machines with latent variables Yu and Joachims (2009), we employ a (per instance) hinge loss during training:

\[L=\max_{{\bf t}\in\mathcal{H}(x)}[0,\boldsymbol{\Delta}({\bf t},{\bf t}^{*}) +\mathcal{S}({\bf t})-\mathcal{S}({\bf t}^{*})] \tag{5}\]

where \(\mathcal{H}(x)\) refers to the set of all possible trees for the given input \(x\), and \({\bf t}^{*}\) denotes the best tree in the latent space \(\mathcal{H}(x,y,sp)\):

\[{\bf t}^{*}=\max_{{\bf t}^{\prime}\in\mathcal{H}(x,y,c)}\mathcal{S}({\bf t}^{ \prime}) \tag{6}\]

Here \(\boldsymbol{\Delta}({\bf t},{\bf t}^{*})\) represents the Hamming loss on labeled spans, measuring the similarity between the predicted tree and the best latent tree that corresponds to the gold chunks.

During decoding, we aim to obtain the best tree as the prediction \(\mathbf{\hat{t}}\) for a new address \(x^{\prime}\) among all the possible trees:

\[\mathbf{\hat{t}}=\operatorname*{arg\,max}_{{\bf t}\in\mathcal{H}(x^{\prime})}[ \mathcal{S}({\bf t})] \tag{7}\]

The yield of the predicted tree \(\mathbf{\hat{t}}\) gives us the list of labeled chunks.4

Footnote 4: In some cases, it is possible to predict a tree with one or more leaf chunks labeled with auxiliary labels (e.g., ROAD). We have a post-processing step that converts such labels into their corresponding original labels (e.g., ROAD).

## 4 Experimental Setup

We call our model Address Parser with Latent Trees (**APLT**). We conducted experiments based on different settings of the \(sp\) values, leading to many model variants. We describe baselines, model hyperparameters as well as evaluation metrics in this section.

BaselinesTo understand the effectiveness of our models, we build the following baselines:

* \(\ell\textbf{CRF}\) is the standard first-order linear CRF model Lafferty et al. (2001) with discrete features for sequence labeling tasks.
* \(s\textbf{CRF}\) is based on the standard semi-Markov CRF Sarawagi and Cohen (2004) with discrete features5. Footnote 5: See the supplementary material for details on the features for \(\ell\textbf{CRF}\) and \(s\textbf{CRF}\). For \(s\textbf{CRF}\) (and \(\textbf{LSTM-$s\textbf{CRF}$}\)), maximal chunk length is set to 36, which is the length of the longest chunk appearing in the training set.

 

* **LSTM-\(\ell\)CRF** is proposed by Lample et al. (2016) which is the state-of-the-art for many sequence labeling tasks
* **LSTM-\(s\)CRF** is based on segmental recurrent neural network Kong et al. (2016) which is the neural network version of semi-Markov CRF Sarawagi and Cohen (2004).
* **TP** is a transition-based parser for chunking based on Lample et al. (2016), which makes use of the _stack LSTM_Dyer et al. (2015) to encode the representation of the stack.

HyperparametersWe conducted all the experiments based on our _Chinese Address_ corpus. We pre-trained Chinese character embeddings based on the Chinese Gigaword corpus Graff and Chen (2005), using the skip-gram model with hierarchical softmax implemented within the _word2vec toolkit Mikolov et al. (2013) where we set the sample rate to \(10^{-5}\) and embedding size to 100.

We use a 2-layer LSTM (for both directions) with a hidden dimension of 200. For optimization, we adopt the Adam Kingma and Ba (2014) optimizer to optimize the model with batch size 1 and dropout rate \(0.4\). We randomly replace the low frequency words with the _UNK_ token and normalize all numbers by replacing each digit (including Chinese characters representing numbers from 0-9) to 0. We train our model for a maximal of 30 epochs and select the model parameters based on the \(F_{1}\) score after each epoch on the development set. The selected model is then applied to the test set for evaluation. Our model, as well as the baseline neural models, are implemented using DyNet Neubig et al. (2017). All the neural weights are initialized following the default initialization method used in DyNet.

Evaluation MetricsWe use the standard evaluation metrics from the CoNLL-2000 shared task Tjong Kim Sang and Buchholz (2000), reporting precision (\(P\).), recall (\(R\).) and \(F_{1}\) percentage scores.

## 5 Result and Discussion

### Main Results

We present our main results in Table 2, where we report the overall performance as well as specific results on the poi label. For our model, we report results for \(sp\text{=}20,-1\) as two special cases - the former learns latent tree structures only and the latter assumes a single right-branching tree. We also report results for \(sp\text{=}7\) which is selected based on the optimal results on the development set.

Among all the baselines, **LSTM-\(\ell\)CRF** performs better than **LSTM** and **TP**, which is consistent with the finding reported in Lample et al. (2016). The two models **LSTM-\(\ell\)CRF** and **LSTM-\(s\)CRF** both achieve similar results, which is also consistent with the finding reported in Liu et al. (2016). The two non-neural models \(\ell\)CRF and \(s\)CRF perform substantially worse than their neural counterparts, which we believe is mainly due to the use of only handcrafted features in such systems. All these baseline models are capable of encoding transition patterns between neighboring chunks, which can partially capture certain structural information. However, certain Chinese address-specific structural information is not explicitly captured in such models.

Our model **APLT** (\(sp\text{=}7\)) achieves the best overall results, as well as the best results when evaluated on poi only. Compared with the strongest baselines **LSTM-\(\ell\)CRF** and **LSTM-\(s\)CRF**, **APLT** (\(sp\text{=}7\)) outperforms them significantly by more than \(1~{}F_{1}\) point overall \((p<10^{-5})\)6. Furthermore, the **APLT** (\(sp\text{=}7\)) model obtains the best \(F_{1}\) scores among all the models on poi. Note that our **APLT** model is able to learn richer dependencies among labels including label order information, regular patterns and irregular patterns among labels. Overall, the model **APLT** (\(sp\text{=}7\)) also outperforms both **APLT** (\(sp\text{=}-1\)) \((p<0.05)\) and **APLT** (\(sp\text{=}20\)) \((p<0.005)\) significantly. Such a result implies the importance of capturing the various Chinese address-specific structural information mentioned above within our model.

Footnote 6: We perform the bootstrap resampling significant test.

To understand the results better, we conduct detailed analysis of our results. Table 3 shows the \(F_{1}\) scores of each label as well as the percentage of each label in t

\begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{Model} & \multicolumn{3}{c}{Poi} & \multicolumn{3}{c}{overall} \\  & \(P\) & \(R\). & \(F_{1}\) & \(P\). & \(R\). & \(F_{1}\) \\ \hline \(\epsilon\)**CRF** & 69.76 & 72.68 & 71.19 & 87.78 & 85.33 & 86.53 \\ \(\kappa\)**CRF** & 74.95 & 77.14 & 76.03 & 88.64 & 87.36 & 87.99 \\ **LSTM** & 70.11 & 76.90 & 73.35 & 85.63 & 88.11 & 86.85 \\ **LSTM-\(\langle\textbf{CRF}\rangle\)** & 77.94 & 75.26 & 76.76 & 88.83 & 88.88 & 88.86 \\ **LSTM-\(\langle\textbf{CRF}\rangle\)** & 77.07 & 78.44 & 77.82 & 89.21 & 88.52 & 88.86 \\ **TP** & 77.61 & 76.57 & 76.63 & 88.80 & 88.75 & 88.77 \\ \(\textbf{APLT}\Rightarrow p\to 20\) (country) & **80.36** & 78.36 & 79.40 & 90.18 & 88.64 & 89.37 \\ \(\textbf{APLT}\Rightarrow p\rightarrow\ 1\) (LAST) & 79.64 & 78.89 & 79.26 & 90.06 & 89.07 & 89.56 \\ \(\textbf{APLT}\Rightarrow p\to 7\) (housesno) & 79.75 & **79.26** & **79.51** & **90.65** & **89.21** & **89.93** \\ \hline \end{tabular}
\end{table}
Table 2: Main results.

 

models **LSTM-\(\ell\)CRF**, **LSTM-\(s\)CRF**, **APLT** (_sp_=\(-1\)) and **APLT** (_sp_=\(7\)). Note that the results for the top 4 labels poi, district, road and city, which take up \(45\%\) of total chunks, all get improved when using our **APLT** models. Moreover, it achieves better or comparable \(F1\) scores on 15 labels in the table among the total 21 labels, especially on poi, district, redundant, community and person with at least \(1\) point improvement in \(F_{1}\). Interestingly, our models perform worse than **LSTM-\(\ell\)CRF** on labels such as roadno, roomo, and floorno, which are mostly related to numbers. We note that, however, chunks with such labels do not constitute a large proportion of all chunks. Results suggest that our models somehow learned to focus on optimization performance for chunks with more prominent labels such as poi and district.

### Effectiveness of Structural Information

In order to investigate how tree structures affect the final performance, we also conducted experiments with different values for _sp_, which is used for determining the split point. Figure 3 shows the moving-averaged \(F_{1}\) scores on the test set obtained when choosing _sp_ around specific values (a similar distribution can be observed on the development set). From the bottom (country,20) to the top (last,-1) along y axis, the lower the _sp_ is, the more constraints are applied to the latent space \(\mathcal{H}(x,y,sp)\). Note that when _sp_=\(-1\) (last), the gold input only corresponds to a single right-branching tree. We exclude the following labels: redundant, assist and otherinfo, because we found these labels may appear at any place within a given address, which make them unsuitable for determining the split point.

From Figure 3 we can observe that the \(F_{1}\) score generally increases as we decrease _sp_, starting from country(with order ID 20). The performance reaches the maximum when the _sp_ is set to a value within the range [subpoi, cellnol]. This observation implies that there does exist ordering information among labels, and introducing more constraints on the latent space will have the benefit of modeling the regular patterns around the beginning part of a given address. After reaching the best value, as we further decrease _sp_, the performance drops slightly and oscillates around the range [floorno, last]. From here we can observe that the latent trees are able to help capture irregular patterns within labels that appear towards the end of the address. Overall, these results suggest the importance of designing a model like ours that is capable of capturing Chinese addressing characteristics.

### Error Analysis

We conduct error analysis on two strongest baselines **LSTM-\(\ell\)CRF** and **LSTM-\(s\)CRF** as well as two best-performing **APLT** models respectively. We examined the list of top-10 labels with most errors for each model, and found most of the errors come from labels such as poi, subpoi and redundant - this implies they are the most challenging labels for this task. We also found labels such as roommo appear in the list for **APLT** models, but not for the **LSTM-\(\ell\)CRF** model, showing that **APLT** models are still not good at handling numbers as we discussed above.

There are two major types of errors. The type

Figure 3: Effect of _sp_.

 

I error refers to the case where the boundary of a chunk is predicted correctly but not its label. The type-II error is the case where even the boundary of a predicted chunk is incorrect. We found that **APLT** (\(sp{=}{-1}\)) and **APLT** (\(sp{=}7\)) produce less type-I errors (\(45.04\%\) and \(42.95\%\) respectively) than **LSTM**-\(\ell\)**CRF** and **LSTM**-\(s\)**CRF** (\(49.87\%\) and \(47.26\%\) respectively). Moreover, we find that **APLT** (\(sp{=}7\)) model produces the least number of type-I errors as well as type-II errors.

Looking into the type-I errors of both two **APLT** models, we find chunks with label poi are often incorrectly labeled as community, which is a major source of errors (\(9\%\) of total errors). As a typical example, we show a partial prediction in Figure 4, where our model fails to recognize "##"(Houhu Village Residence) as a poi. Here the character "##"(Village) is a common suffix for the name of either a village or a residence, hence the confusion.

The second example in Figure 4 demonstrates another typical kind of errors produced by our models around the poi labels. Here, "# 

that we used, which are capable of capturing complex dependencies among chunks.

## 6 Related Work

While the Chinese address parsing task is new, it is related to the following traditional tasks within the field of natural language processing (NLP) - chunking, named entity recognition, word segmentation and parsing. We briefly survey research efforts which are most related to our task below.

Chunking as a fundamental task in NLP has been investigated for decades Abney (1991). Chunking for Chinese can typically be regarded as a sequence labeling problem solvable by models such as conditional random fields Chen et al. (2006); Tan et al. (2005); Zhou et al. (2012), hidden Markov models Li et al. (2003), support vector machines Tan et al. (2004) and the maximum entropy model Wu et al. (2005). Our task can also be regarded as a chunking task where we need to assign an address-specific label to each chunk.

Named entity recognition (NER) is another fundamental task close to chunking within the field of NLP, which focuses on the extraction of semantically meaningful entities from the text. The state-of-the-art approach by Lample et al. (2016) employs a LSTM-CRF model Ma and Hovy (2016) proposed a LSTM-CNNs-CRF model that utilizes convolutional neural networks (CNNs) to extract character-level features besides word-level features. Zhai et al. (2017) suggested a neural chunking model based on pointer networks Vinyals et al. (2015) to resolve the issue of being difficult to use chunk-level features such as the length of the chunk for segmentation. Zhang and Yang (2018) tackled the problem of Chinese NER by deploying a lattice LSTM leveraging lexicons.

Another task closely related to our task is the Chinese word segmentation task which at least dates back to the 1990s Sproat et al. (1994). The segmentation task is typically casted as a character-based sequence labeling problem Xue (2003) which can be solved by CRF based models Peng et al. (2004); Zhao et al. (2006), their latent-variable variants Sun et al. (2009), or maxmargin based models Zhang and Clark (2007). Recently, Zhang et al. (2016) proposed a neural transition-based segmentation approach by encoding both words and characters as well as the history action sequence. Yang et al. (2017) suggested to perform segmentation with a neural transition-based method with rich pre-training.

Constituent parsing is another line of work that is related to our task. The state-of-the-art approaches to parsing include transition-based models Dyer et al. (2016) and chart-based models Stern et al. (2017); Kitaev and Klein (2018).Our model is motivated by the latter approaches, where we additionally introduce latent variables for capturing complex dependencies among chunks.

## 7 Conclusion

In this work, we introduce a new task - _Chinese address parsing_, which is to segment a given Chinese address text into chunks while assigning each chunk a semantically meaningful label. We create and publish a _Chinese address_ corpus that consists of 15K fully labeled Chinese addresses. We identify interesting characteristics associated with the task and design a novel neural parsing model with latent variables for this task, which is able to capture Chinese address-specific structural information. We conduct extensive experiments and compare our approach with strong baselines through detailed analysis. We show that our proposed model outperforms baseline approaches significantly, due to its ability in capturing rich structural information present in the Chinese addresses.

Future work includes leveraging external knowledge bases to disambiguate chunks and entities that appear within Chinese addresses, as well as designing algorithms that are able to capture longer-range dependencies among chunks using alternative structures.

## Acknowledgments

We would like to thank the anonymous reviewers for their constructive comments on this work. This work is done under a collaborative agreement between SutTD and Alibaba on an Alibaba Innovative Research (AIR) Program funded by Alibaba, where Alibaba provided data. We appreciate Alibaba's generosity in the agreement that makes it possible for us to make all data and code in this research publicly available upon acceptance of this paper. This work is also partially supported by SUTD project PIE-SGP-AI-2018-01.

\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LSTM** & **LSTM** & **APLT** & **APLT** \\ _t_**CRF** & _s_**CRF** & _spa_=1 & _spa_? \\ \hline
80.17 & 79.92 & 80.94 & 80.94 \\ \hline \end{tabular}
\end{table}
Table 5: Accuracy on test data for the new chunks.

 



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