Abstract

Training speed and accuracy are two major concerns of large-scale natural language processing systems. Typically, we need to make a tradeoff between speed and accuracy. It is trivial to improve the training speed via sacrificing accuracy or to improve the accuracy via sacrificing speed. Nevertheless, it is nontrivial to improve the training speed and the accuracy at the same time, which is the target of this work. To reach this target, we present a new training method, feature-frequency–adaptive on-line training, for fast and accurate training of natural language processing systems. It is based on the core idea that higher frequency features should have a learning rate that decays faster. Theoretical analysis shows that the proposed method is convergent with a fast convergence rate. Experiments are conducted based on well-known benchmark tasks, including named entity recognition, word segmentation, phrase chunking, and sentiment analysis. These tasks consist of three structured classification tasks and one non-structured classification task, with binary features and real-valued features, respectively. Experimental results demonstrate that the proposed method is faster and at the same time more accurate than existing methods, achieving state-of-the-art scores on the tasks with different characteristics.

1. Introduction

Training speed is an important concern of natural language processing (NLP) systems. Large-scale NLP systems are computationally expensive. In many real-world applications, we further need to optimize high-dimensional model parameters. For example, the state-of-the-art word segmentation system uses more than 40 million features (Sun, Wang, and Li 2012). The heavy NLP models together with high-dimensional parameters lead to a challenging problem on model training, which may require week-level training time even with fast computing machines.

Accuracy is another very important concern of NLP systems. Nevertheless, usually it is quite difficult to build a system that has fast training speed and at the same time has high accuracy. Typically we need to make a tradeoff between speed and accuracy, to trade training speed for higher accuracy or vice versa. In this work, we have tried to overcome this problem: to improve the training speed and the model accuracy at the same time.

There are two major approaches for parameter training: batch and on-line. Standard gradient descent methods are normally batch training methods, in which the gradient computed by using all training instances is used to update the parameters of the model. The batch training methods include, for example, steepest gradient descent, conjugate gradient descent (CG), and quasi-Newton methods like limited-memory BFGS (Nocedal and Wright 1999). The true gradient is usually the sum of the gradients from each individual training instance. Therefore, batch gradient descent requires the training method to go through the entire training set before updating parameters. This is why batch training methods are typically slow.

On-line learning methods can significantly accelerate the training speed compared with batch training methods. A representative on-line training method is the stochastic gradient descent method (SGD) and its extensions (e.g., stochastic meta descent) (Bottou 1998; Vishwanathan et al. 2006). The model parameters are updated more frequently compared with batch training, and fewer passes are needed before convergence. For large-scale data sets, on-line training methods can be much faster than batch training methods.

However, we find that the existing on-line training methods are still not good enough for training large-scale NLP systems—probably because those methods are not well-tailored for NLP systems that have massive features. First, the convergence speed of the existing on-line training methods is not fast enough. Our studies show that the existing on-line training methods typically require more than 50 training passes before empirical convergence, which is still slow. For large-scale NLP systems, the training time per pass is typically long and fast convergence speed is crucial. Second, the accuracy of the existing on-line training methods is not good enough. We want to further improve the training accuracy. We try to deal with the two challenges at the same time. Our goal is to develop a new training method for faster and at the same time more accurate natural language processing.

In this article, we present a new on-line training method, adaptive on-line gradient descent based on feature frequency information (ADF),1 for very accurate and fast on-line training of NLP systems. Other than the high training accuracy and fast training speed, we further expect that the proposed training method has good theoretical properties. We want to prove that the proposed method is convergent and has a fast convergence rate.

In the proposed ADF training method, we use a learning rate vector in the on-line updating. This learning rate vector is automatically adapted based on feature frequency information in the training data set. Each model parameter has its own learning rate adapted on feature frequency information. This proposal is based on the simple intuition that a feature with higher frequency in the training process should have a learning rate that decays faster. This is because a higher frequency feature is expected to be well optimized with higher confidence. Thus, a higher frequency feature is expected to have a lower learning rate. We systematically formalize this intuition into a theoretically sound training algorithm, ADF.

The main contributions of this work are as follows:

  • On the methodology side, we propose a general purpose on-line training method, ADF. The ADF method is significantly more accurate than existing on-line and batch training methods, and has faster training speed. Moreover, theoretical analysis demonstrates that the ADF method is convergent with a fast convergence rate.

  • On the application side, for the three well-known tasks, including named entity recognition, word segmentation, and phrase chunking, the proposed simple method achieves equal or even better accuracy than the existing gold-standard systems, which are complicated and use extra resources.

2. Related Work

Our main focus is on structured classification models with high dimensional features. For structured classification, the conditional random fields model is widely used. To illustrate that the proposed method is a general-purpose training method not limited to a specific classification task or model, we also evaluate the proposal for non-structured classification tasks like binary classification. For non-structured classification, the maximum entropy model (Berger, Della Pietra, and Della Pietra 1996; Ratnaparkhi 1996) is widely used. Here, we review the conditional random fields model and the related work of on-line training methods.

2.1 Conditional Random Fields

The conditional random field (CRF) model is a representative structured classification model and it is well known for its high accuracy in real-world applications. The CRF model is proposed for structured classification by solving “the label bias problem” (Lafferty, McCallum, and Pereira 2001). Assuming a feature function that maps a pair of observation sequence and label sequence to a global feature vector , the probability of a label sequence conditioned on the observation sequence is modeled as follows (Lafferty, McCallum, and Pereira 2001):
formula
where is a parameter vector.
Given a training set consisting of n labeled sequences, , for i = 1 … n, parameter estimation is performed by maximizing the objective function,
formula
The first term of this equation represents a conditional log-likelihood of training data. The second term is a regularizer for reducing overfitting. We use an L2 prior, . In what follows, we denote the conditional log-likelihood of each sample as as . The final objective function is as follows:
formula

2.2 On-line Training

The most representative on-line training method is the SGD method (Bottou 1998; Tsuruoka, Tsujii, and Ananiadou 2009; Sun et al. 2013). The SGD method uses a randomly selected small subset of the training sample to approximate the gradient of an objective function. The number of training samples used for this approximation is called the batch size. By using a smaller batch size, one can update the parameters more frequently and speed up the convergence. The extreme case is a batch size of 1, and it gives the maximum frequency of updates, which we adopt in this work. In this case, the model parameters are updated as follows:
formula
where t is the update counter, γt is the learning rate or so-called decaying rate, and is the stochastic loss function based on a training sample . (More details of SGD are described in Bottou [1998], Tsuruoka, Tsujii, and Ananiadou [2009], and Sun et al. [2013].) Following the most recent work of SGD, the exponential decaying rate works the best for natural language processing tasks, and it is adopted in our implementation of the SGD (Tsuruoka, Tsujii, and Ananiadou 2009; Sun et al. 2013).

Other well-known on-line training methods include perceptron training (Freund and Schapire 1999), averaged perceptron training (Collins 2002), more recent development/extensions of stochastic gradient descent (e.g., the second-order stochastic gradient descent training methods like stochastic meta descent) (Vishwanathan et al. 2006; Hsu et al. 2009), and so on. However, the second-order stochastic gradient descent method requires the computation or approximation of the inverse of the Hessian matrix of the objective function, which is typically slow, especially for heavily structured classification models. Usually the convergence speed based on number of training iterations is moderately faster, but the time cost per iteration is slower. Thus the overall time cost is still large.

Compared with the related work on batch and on-line training (Jacobs 1988; Sperduti and Starita 1993; Dredze, Crammer, and Pereira 2008; Duchi, Hazan, and Singer 2010; McMahan and Streeter 2010), our work is fundamentally different. The proposed ADF training method is based on feature frequency adaptation, and to the best of our knowledge there is no prior work on direct feature-frequency–adaptive on-line training. Compared with the confidence-weighted (CW) classification method and its variation AROW (Dredze, Crammer, and Pereira 2008; Crammer, Kulesza, and Dredze 2009), the proposed method is substantially different. While the feature frequency information is implicitly modeled via a complicated Gaussian distribution framework in Dredze, Crammer, and Pereira (2008) and Crammer, Kulesza, and Dredze (2009), the frequency information is explicitly modeled in our proposal via simple learning rate adaptation. Our proposal is more straightforward in capturing feature frequency information, and it has no need to use Gaussian distributions and KL divergence, which are important in the CW and AROW methods. In addition, our proposal is a probabilistic learning method for training probabilistic models such as CRFs, whereas the CW and AROW methods (Dredze, Crammer, and Pereira 2008; Crammer, Kulesza, and Dredze 2009) are non-probabilistic learning methods extended from perceptron-style approaches. Thus, the framework is different. This work is a substantial extension of the conference version (Sun, Wang, and Li 2012). Sun, Wang, and Li (2012) focus on the specific task of word segmentation, whereas this article focuses on the proposed training algorithm.

3. Feature-Frequency–Adaptive On-line Learning

In traditional on-line optimization methods such as SGD, no distinction is made for different parameters in terms of the learning rate, and this may result in slow convergence of the model training. For example, in the on-line training process, suppose the high frequency feature f1 and the low frequency feature f2 are observed in a training sample and their corresponding parameters w1 and w2 are to be updated via the same learning rate γt. Suppose the high frequency feature f1 has been updated 100 times and the low frequency feature f2 has only been updated once. Then, it is possible that the weight w1 is already well optimized and the learning rate γt is too aggressive for updating w1. Updating the weight w1 with the learning rate γt may make w1 be far from the well-optimized value, and it will require corrections in the future updates. This causes fluctuations in the on-line training and results in slow convergence speed. On the other hand, it is possible that the weight w2 is poorly optimized and the same learning rate γt is too conservative for updating w2. This also results in slow convergence speed.

To solve this problem, we propose ADF. In spite of the high accuracy and fast convergence speed, the proposed method is easy to implement. The proposed method with feature-frequency–adaptive learning rates can be seen as a learning method with specific diagonal approximation of the Hessian information based on assumptions of feature frequency information. In this approximation, the diagonal elements of the diagonal matrix correspond to the feature-frequency–adaptive learning rates. According to the aforementioned example and analysis, it assumes that a feature with higher frequency in the training process should have a learning rate that decays faster.

3.1 Algorithm

In the proposed ADF method, we try to use more refined learning rates than traditional SGD training. Instead of using a single learning rate (a scalar) for all weights, we extend the learning rate scalar to a learning rate vector, which has the same dimension as the weight vector . The learning rate vector is automatically adapted based on feature frequency information. By doing so, each weight has its own learning rate, and we will show that this can significantly improve the convergence speed of on-line learning.

In the ADF learning method, the update formula is:
formula
The update term is the gradient term of a randomly sampled instance:
formula
In addition, is a positive vector-valued learning rate and · denotes the component-wise (Hadamard) product of two vectors.

The learning rate vector is automatically adapted based on feature frequency information in the updating process. Intuitively, a feature with higher frequency in the training process has a learning rate that decays faster. This is because a weight with higher frequency is expected to be more adequately trained, hence a lower learning rate is preferable for fast convergence. We assume that a high frequency feature should have a lower learning rate, and a low frequency feature should have a relatively higher learning rate in the training process. We systematically formalize this idea into a theoretically sound training algorithm. The proposed method with feature-frequency–adaptive learning rates can be seen as a learning method with specific diagonal approximation of the inverse of the Hessian matrix based on feature frequency information.

Given a window size q (number of samples in a window), we use a vector to record the feature frequency. The kth entry corresponds to the frequency of the feature k in this window. Given a feature k, we use u to record the normalized frequency:
formula
For each feature, an adaptation factor η is calculated based on the normalized frequency information, as follows:
formula
where α and β are the upper and lower bounds of a scalar, with 0 < β < α < 1. Intuitively, the upper bound α corresponds to the adaptation factor of the lowest frequency features, and the lower bound β corresponds to the adaptation factor of the highest frequency features. The optimal values of α and β can be tuned based on specific real-world tasks, for example, via cross-validation on the training data or using held-out data. In practice, via cross-validation on the training data of different tasks, we found that the following setting is sufficient to produce adequate performance for most of the real-world natural language processing tasks: α around 0.995, and β around 0.6. This indicates that the feature frequency information has similar characteristics across many different natural language processing tasks.
As we can see, a feature with higher frequency corresponds to a smaller scalar via linear approximation. Finally, the learning rate is updated as follows:
formula
With this setting, different features correspond to different adaptation factors based on feature frequency information. Our ADF algorithm is summarized in Figure 1.
Figure 1 

The proposed ADF on-line learning algorithm. In the algorithm, is the training data set; q, c, α, and β are hyper-parameters; q is an integer representing window size; c is for initializing the learning rates; and α and β are the upper and lower bounds of a scalar, with 0 < β < α < 1.

Figure 1 

The proposed ADF on-line learning algorithm. In the algorithm, is the training data set; q, c, α, and β are hyper-parameters; q is an integer representing window size; c is for initializing the learning rates; and α and β are the upper and lower bounds of a scalar, with 0 < β < α < 1.

The ADF training method is efficient because the only additional computation (compared with traditional SGD) is the derivation of the learning rates, which is simple and efficient. As we know, the regularization of SGD can perform efficiently via the optimization based on sparse features (Shalev-Shwartz, Singer, and Srebro 2007). Similarly, the derivation of can also perform efficiently via the optimization based on sparse features. Note that although binary features are common in natural language processing tasks, the ADF algorithm is not limited to binary features and it can be applied to real-valued features.

3.2 Convergence Analysis

We want to show that the proposed ADF learning algorithm has good convergence properties. There are two steps in the convergence analysis. First, we show that the ADF update rule is a contraction mapping. Then, we show that the ADF training is asymptotically convergent, and with a fast convergence rate.

To simplify the discussion, our convergence analysis is based on the convex loss function of traditional classification or regression problems:
formula
where is the feature vector generated from the training sample . is a function in , such as for regression or for binary classification.

To make convergence analysis of the proposed ADF training algorithm, we need to introduce several mathematical definitions. First, we introduce Lipschitz continuity:

Definition 1 (Lipschitz continuity)

A function is Lipschitz continuous with the degree of D if |F(x) − F(y)| ≤ D|xy| for . can be multi-dimensional space, and |xy| is the distance between the points x and y.

Based on the definition of Lipschitz continuity, we give the definition of the Lipschitz constant ||F||Lip as follows:

Definition 2 (Lipschitz constant)
formula
In other words, the Lipschitz constant ||F||Lip is the lower bound of the continuity degree that makes the function F Lipschitz continuous.

Further, based on the definition of Lipschitz constant, we give the definition of contraction mapping as follows:

Definition 3 (Contraction mapping)

A function is a contraction mapping if its Lipschitz constant is smaller than 1: ||F||Lip < 1.

Then, we can show that the traditional SGD update is a contraction mapping.

Lemma 1 (SGD update rule is contraction mapping)

Let γ be a fixed low learning rate in SGD updating. If , the SGD update rule is a contraction mapping in Euclidean space with Lipschitz continuity degree 1 − γ/σ2.

The proof can be extended from the related work on convergence analysis of parallel SGD training (Zinkevich et al. 2010). The stochastic training process is a one-following-one dynamic update process. In this dynamic process, if we use the same update rule F, we have and . It is only necessary to prove that the dynamic update is a contraction mapping restricted by this one-following-one dynamic process. That is, for the proposed ADF update rule, it is only necessary to prove it is a dynamic contraction mapping. We formally define dynamic contraction mapping as follows.

Definition 4 (Dynamic contraction mapping)

Given a function , suppose the function is used in a dynamic one-following-one process: xt + 1 = F(xt) and xt + 2 = F(xt + 1) for . Then, the function F is a dynamic contraction mapping if ∃D < 1, |xt + 2xt + 1| ≤ D|xt + 1xt| for .

We can see that a contraction mapping is also a dynamic contraction mapping, but a dynamic contraction mapping is not necessarily a contraction mapping. We first show that the ADF update rule with a fixed learning rate vector of different learning rates is a dynamic contraction mapping.

Theorem 1 (ADF update rule with fixed learning rates)

Let be a fixed learning rate vector with different learning rates. Let γmax be the maximum learning rate in the learning rate vector : . Then if , the ADF update rule is a dynamic contraction mapping in Euclidean space with Lipschitz continuity degree .

The proof is sketched in Section 5.

Further, we need to prove that the ADF update rule with a decaying learning rate vector is a dynamic contraction mapping, because the real ADF algorithm has a decaying learning rate vector. In the decaying case, the condition that can be easily achieved, because continues to decay with an exponential decaying rate. Even if the is initialized with high values of learning rates, after a number of training passes (denoted as T) is guaranteed to be small enough so that and . Without losing generality, our convergence analysis starts from the pass T and we take as in the following analysis. Thus, we can show that the ADF update rule with a decaying learning rate vector is a dynamic contraction mapping:

Theorem 2 (ADF update rule with decaying learning rates)

Let be a learning rate vector in the ADF learning algorithm, which is decaying over the time t and with different decaying rates based on feature frequency information. Let start from a low enough learning rate vector such that , where γmax is the maximum element in . Then, the ADF update rule with decaying learning rate vector is a dynamic contraction mapping in Euclidean space with Lipschitz continuity degree .

The proof is sketched in Section 5.

Based on the connections between ADF training and contraction mapping, we demonstrate the convergence properties of the ADF training method. First, we prove the convergence of the ADF training.

Theorem 3 (ADF convergence)

ADF training is asymptotically convergent.

The proof is sketched in Section 5.

Further, we analyze the convergence rate of the ADF training. When we have the lowest learning rate , the expectation of the obtained is as follows (Murata 1998; Hsu et al. 2009):
formula
where is the optimal weight vector, and is the Hessian matrix of the objective function. The rate of convergence is governed by the largest eigenvalue of the function . Following Murata (1998) and Hsu et al. (2009), we can derive a bound of rate of convergence, as follows.

Theorem 4 (ADF convergence rate)

Assume φ is the largest eigenvalue of the function . For the proposed ADF training, its convergence rate is bounded by φ, and we have
formula
where λ is the minimum eigenvalue of .

The proof is sketched in Section 5.

The convergence analysis demonstrates that the proposed method with feature-frequency-adaptive learning rates is convergent and the bound of convergence rate is analyzed. It demonstrates that increasing the values of and β leads to a lower bound of the convergence rate. Because the bound of the convergence rate is just an up-bound rather than the actual convergence rate, we still need to conduct automatic tuning of the hyper-parameters, including and β, for optimal convergence rate in practice. The ADF training method has a fast convergence rate because the feature-frequency-adaptive schema can avoid the fluctuations on updating the weights of high frequency features, and it can avoid the insufficient training on updating the weights of low frequency features. In the following sections, we perform experiments to confirm the fast convergence rate of the proposed method.

4. Evaluation

Our main focus is on training heavily structured classification models. We evaluate the proposal on three NLP structured classification tasks: biomedical named entity recognition (Bio-NER), Chinese word segmentation, and noun phrase (NP) chunking. For the structured classification tasks, the ADF training is based on the CRF model (Lafferty, McCallum, and Pereira 2001). Further, to demonstrate that the proposed method is not limited to structured classification tasks, we also perform experiments on a non-structured binary classification task: sentiment-based text classification. For the non-structured classification task, the ADF training is based on the maximum entropy model (Berger, Della Pietra, and Della Pietra 1996; Ratnaparkhi 1996).

4.1 Biomedical Named Entity Recognition (Structured Classification)

The biomedical named entity recognition (Bio-NER) task is from the BIONLP-2004 shared task. The task is to recognize five kinds of biomedical named entities, including DNA, RNA, protein, cell line, and cell type, on the MEDLINE biomedical text mining corpus (Kim et al. 2004). A typical approach to this problem is to cast it as a sequential labeling task with the BIO encoding.

This data set consists of 20,546 training samples (from 2,000 MEDLINE article abstracts, with 472,006 word tokens) and 4,260 test samples. The properties of the data are summarized in Table 1. State-of-the-art systems for this task include Settles (2004), Finkel et al. (2004), Okanohara et al. (2006), Hsu et al. (2009), Sun, Matsuzaki, et al. (2009), and Tsuruoka, Tsujii, and Ananiadou (2009).

Table 1 

Summary of the Bio-NER data set.


#Abstracts
#Sentences
#Words
Train 2,000 20,546 (10/abs) 472,006 (23/sen) 
Test 404 4,260 (11/abs) 96,780 (23/sen) 

#Abstracts
#Sentences
#Words
Train 2,000 20,546 (10/abs) 472,006 (23/sen) 
Test 404 4,260 (11/abs) 96,780 (23/sen) 

Following previous studies for this task (Okanohara et al. 2006; Sun, Matsuzaki, et al. 2009), we use word token–based features, part-of-speech (POS) based features, and orthography pattern–based features (prefix, uppercase/lowercase, etc.), as listed in Table 2. With the traditional implementation of CRF systems (e.g., the HCRF package), the edges features usually contain only the information of yi − 1 and yi, and ignore the information of the observation sequence (i.e., ). The major reason for this simple realization of edge features in traditional CRF implementation is to reduce the dimension of features. To improve the model accuracy, we utilize rich edge features following Sun, Wang, and Li (2012), in which local observation information of is combined in edge features just like the implementation of node features. A detailed introduction to rich edge features can be found in Sun, Wang, and Li (2012). Using the feature templates, we extract a high dimensional feature set, which contains 5.3 ×107 features in total. Following prior studies, the evaluation metric for this task is the balanced F-score defined as 2PR/(P + R), where P is precision and R is recall.

Table 2 

Feature templates used for the Bio-NER task. wi is the current word token on position i. ti is the POS tag on position i. oi is the orthography mode on position i. yi is the classification label on position i. yi − 1yi represents label transition. represents a Cartesian product between two sets.

Word Token–based Features: 
{wi − 2, wi − 1, wi, wi + 1, wi + 2, wi − 1wi, wiwi + 1
×{yi, yi − 1yi
Part-of-Speech (POS)–based Features: 
{ti − 2, ti − 1, ti, ti + 1, ti + 2, ti − 2ti − 1, ti − 1ti, titi + 1, ti + 1ti + 2, ti − 2ti − 1ti, ti − 1titi + 1, titi + 1ti + 2
×{yi, yi − 1yi
Orthography Pattern–based Features: 
{oi − 2, oi − 1, oi, oi + 1, oi + 2, oi − 2oi − 1, oi − 1oi, oioi + 1, oi + 1oi + 2
×{yi, yi − 1yi
Word Token–based Features: 
{wi − 2, wi − 1, wi, wi + 1, wi + 2, wi − 1wi, wiwi + 1
×{yi, yi − 1yi
Part-of-Speech (POS)–based Features: 
{ti − 2, ti − 1, ti, ti + 1, ti + 2, ti − 2ti − 1, ti − 1ti, titi + 1, ti + 1ti + 2, ti − 2ti − 1ti, ti − 1titi + 1, titi + 1ti + 2
×{yi, yi − 1yi
Orthography Pattern–based Features: 
{oi − 2, oi − 1, oi, oi + 1, oi + 2, oi − 2oi − 1, oi − 1oi, oioi + 1, oi + 1oi + 2
×{yi, yi − 1yi

4.2 Chinese Word Segmentation (Structured Classification)

Chinese word segmentation aims to automatically segment character sequences into word sequences. Chinese word segmentation is important because it is the first step for most Chinese language information processing systems. Our experiments are based on the Microsoft Research data provided by The Second International Chinese Word Segmentation Bakeoff. In this data set, there are 8.8×104 word-types, 2.4×106 word-tokens, 5×103 character-types, and 4.1×106 character-tokens. State-of-the-art systems for this task include Tseng et al. (2005), Zhang, Kikui, and Sumita (2006), Zhang and Clark (2007), Gao et al. (2007), Sun, Zhang, et al. (2009), Sun (2010), Zhao et al. (2010), and Zhao and Kit (2011).

The feature engineering follows previous work on word segmentation (Sun, Wang, and Li 2012). Rich edge features are used. For the classification label yi and the label transition yi − 1yi on position i, we use the feature templates as follows (Sun, Wang, and Li 2012):

  • Character unigrams located at positions i − 2, i − 1, i, i + 1, and i + 2.

  • Character bigrams located at positions i − 2, i − 1, i and i + 1.

  • Whether xj and xj + 1 are identical, for j = i − 2, …, i + 1.

  • Whether xj and xj + 2 are identical, for j = i − 3, …, i + 1.

  • The character sequence xj,i if it matches a word , with the constraint i − 6 < j < i. The item xj,i represents the character sequence xjxi. represents the unigram-dictionary collected from the training data.

  • The character sequence xi,k if it matches a word , with the constraint i < k < i + 6.

  • The word bigram candidate [xj,i − 1, xi,k] if it hits a word bigram , and satisfies the aforementioned constraints on j and k. represents the word bigram dictionary collected from the training data.

  • The word bigram candidate [xj,i, xi + 1,k] if it hits a word bigram , and satisfies the aforementioned constraints on j and k.

All feature templates are instantiated with values that occurred in training samples. The extracted feature set is large, and there are 2.4×107 features in total. Our evaluation is based on a closed test, and we do not use extra resources. Following prior studies, the evaluation metric for this task is the balanced F-score.

4.3 Phrase Chunking (Structured Classification)

In the phrase chunking task, the non-recursive cores of noun phrases, called base NPs, are identified. The phrase chunking data is extracted from the data of the CoNLL-2000 shallow-parsing shared task (Sang and Buchholz 2000). The training set consists of 8,936 sentences, and the test set consists of 2,012 sentences. We use the feature templates based on word n-grams and part-of-speech n-grams, and feature templates are shown in Table 3. Rich edge features are used. Using the feature templates, we extract 4.8 ×105 features in total. State-of-the-art systems for this task include Kudo and Matsumoto (2001), Collins (2002), McDonald, Crammer, and Pereira (2005), Vishwanathan et al. (2006; Sun et al. (2008), and Tsuruoka, Tsujii, and Ananiadou (2009). Following prior studies, the evaluation metric for this task is the balanced F-score.

Table 3 

Feature templates used for the phrase chunking task. wi, ti, and yi are defined as before.

Word-Token–based Features: 
{wi − 2, wi − 1, wi, wi + 1, wi + 2, wi − 1wi, wiwi + 1
×{yi, yi − 1yi
Part-of-Speech (POS)–based Features: 
{ti − 1, ti, ti + 1, ti − 2ti − 1, ti − 1ti, titi + 1, ti + 1ti + 2, ti − 2ti − 1ti, ti − 1titi + 1, titi + 1ti + 2
×{yi, yi − 1yi
Word-Token–based Features: 
{wi − 2, wi − 1, wi, wi + 1, wi + 2, wi − 1wi, wiwi + 1
×{yi, yi − 1yi
Part-of-Speech (POS)–based Features: 
{ti − 1, ti, ti + 1, ti − 2ti − 1, ti − 1ti, titi + 1, ti + 1ti + 2, ti − 2ti − 1ti, ti − 1titi + 1, titi + 1ti + 2
×{yi, yi − 1yi

4.4 Sentiment Classification (Non-Structured Classification)

To demonstrate that the proposed method is not limited to structured classification, we select a well-known sentiment classification task for evaluating the proposed method on non-structured classification.

Generally, sentiment classification classifies user review text as a positive or negative opinion. This task (Blitzer, Dredze, and Pereira 2007) consists of four subtasks based on user reviews from Amazon.com. Each subtask is a binary sentiment classification task based on a specific topic. We use the maximum entropy model for classification. We use the same lexical features as those used in Blitzer, Dredze, and Pereira (2007), and the total number of features is 9.4 ×105. Following prior work, the evaluation metric is binary classification accuracy.

4.5 Experimental Setting

As for training, we perform gradient descent with the proposed ADF training method. To compare with existing literature, we choose four popular training methods, a representative batch training method, and three representative on-line training methods. The batch training method is the limited-memory BFGS (LBFGS) method (Nocedal and Wright 1999), which is considered to be one of the best optimizers for log-linear models like CRFs. The on-line training methods include the SGD training method, which we introduced in Section 2.2, the structured perceptron (Perc) training method (Freund and Schapire 1999; Collins 2002), and the averaged perceptron (Avg-Perc) training method (Collins 2002). The structured perceptron method and averaged perceptron method are non-probabilistic training methods that have very fast training speed due to the avoidance of the computation on gradients (Sun, Matsuzaki, and Li 2013). All training methods, including ADF, SGD, Perc, Avg-Perc, and LBFGS, use the same set of features.

We also compared the ADF method with the CW method (Dredze, Crammer, and Pereira 2008) and the AROW method (Crammer, Kulesza, and Dredze 2009). The CW and AROW methods are implemented based on the Confidence Weighted Learning Library.2 Because the current implementation of the CW and AROW methods do not utilize rich edge features, we removed the rich edge features in our systems to make more fair comparisons. That is, we removed rich edge features in the CRF-ADF setting, and this simplified method is denoted as ADF-noRich. The second-order stochastic gradient descent training methods, including the SMD method (Vishwanathan et al. 2006) and the PSA method (Hsu et al. 2009), are not considered in our experiments because we find those methods are quite slow when running on our data sets with high dimensional features.

We find that the settings of q, α, and β in the ADF training method are not sensitive among specific tasks and can be generally set. We simply set q = n/10 (n is the number of training samples). It means that feature frequency information is updated 10 times per iteration. Via cross-validation only on the training data of different tasks, we find that the following setting is sufficient to produce adequate performance for most of the real-world natural language processing tasks: α around 0.995 and β around 0.6. This indicates that the feature frequency information has similar characteristics across many different natural language processing tasks.

Thus, we simply use the following setting for all tasks: q = n/10, α = 0.995, and β = 0.6. This leaves c (the initial value of the learning rates) as the only hyper-parameter that requires careful tuning. We perform automatic tuning for c based on the training data via 4-fold cross-validation, testing with c = 0.005, 0.01, 0.05, 0.1, respectively, and the optimal c is chosen based on the best accuracy of cross-validation. Via this automatic tuning, we find it is proper to set c = 0.005, 0.1, 0.05, 0.005, for the Bio-NER, word segmentation, phrase chunking, and sentiment classification tasks, respectively.

To reduce overfitting, we use an L2 Gaussian weight prior (Chen and Rosenfeld 1999) for the ADF, LBFGS, and SGD training methods. We vary the σ with different values (e.g., 1.0, 2.0, and 5.0) for 4-fold cross validation on the training data of different tasks, and finally set σ = 5.0 for all training methods in the Bio-NER task; σ = 5.0 for all training methods in the word segmentation task; σ = 5.0, 1.0, 1.0 for ADF, SGD, and LBFGS in the phrase chunking task; and σ = 1.0 for all training methods in the sentiment classification task. Experiments are performed on a computer with an Intel(R) Xeon(R) 2.0-GHz CPU.

4.6 Structured Classification Results

4.6.1 Comparisons Based on Empirical Convergence

First, we check the experimental results of different methods on their empirical convergence state. Because the perceptron training method (Perc) does not achieve empirical convergence even with a very large number of training passes, we simply report its results based on a large enough number of training passes (e.g., 200 passes). Experimental results are shown in Table 4.

Table 4 

Results for the Bio-NER, word segmentation, and phrase chunking tasks. The results and the number of passes are decided based on empirical convergence (with score deviation of adjacent five passes less than 0.01). For the non-convergent case, we simply report the results based on a large enough number of training passes. As we can see, the ADF method achieves the best accuracy with the fastest convergence speed.

Bio-NER
Prec
Rec
F-score
Passes
Train-Time (sec)
LBFGS (batch) 67.69 70.20 68.92 400 152,811.34 
SGD (on-line) 70.91 72.69 71.79 91 76,549.21 
Perc (on-line) 65.37 66.95 66.15 200 20,436.69 
Avg-Perc (on-line) 68.76 72.56 70.61 37 3,928.01 
ADF (proposal) 71.71 72.80 72.25 35 27,490.24 
 
Segmentation Prec Rec F-score Passes Train-Time (sec) 
LBFGS (batch) 97.46 96.86 97.16 102 13,550.68 
SGD (on-line) 97.58 97.11 97.34 27 6,811.15 
Perc (on-line) 96.99 96.03 96.50 200 8,382.606 
Avg-Perc (on-line) 97.56 97.05 97.30 16 716.87 
ADF (proposal) 97.67 97.31 97.49 15 4,260.08
 
 
Chunking Prec Rec F-score Passes Train-Time (sec) 
LBFGS (batch) 94.57 94.09 94.33 105 797.04 
SGD (on-line) 94.48 94.04 94.26 56 903.88 
Perc (on-line) 93.66 93.31 93.48 200 543.51 
Avg-Perc (on-line) 94.34 94.04 94.19 12 33.45 
ADF (proposal) 94.66 94.38 94.52 17 282.17 
Bio-NER
Prec
Rec
F-score
Passes
Train-Time (sec)
LBFGS (batch) 67.69 70.20 68.92 400 152,811.34 
SGD (on-line) 70.91 72.69 71.79 91 76,549.21 
Perc (on-line) 65.37 66.95 66.15 200 20,436.69 
Avg-Perc (on-line) 68.76 72.56 70.61 37 3,928.01 
ADF (proposal) 71.71 72.80 72.25 35 27,490.24 
 
Segmentation Prec Rec F-score Passes Train-Time (sec) 
LBFGS (batch) 97.46 96.86 97.16 102 13,550.68 
SGD (on-line) 97.58 97.11 97.34 27 6,811.15 
Perc (on-line) 96.99 96.03 96.50 200 8,382.606 
Avg-Perc (on-line) 97.56 97.05 97.30 16 716.87 
ADF (proposal) 97.67 97.31 97.49 15 4,260.08
 
 
Chunking Prec Rec F-score Passes Train-Time (sec) 
LBFGS (batch) 94.57 94.09 94.33 105 797.04 
SGD (on-line) 94.48 94.04 94.26 56 903.88 
Perc (on-line) 93.66 93.31 93.48 200 543.51 
Avg-Perc (on-line) 94.34 94.04 94.19 12 33.45 
ADF (proposal) 94.66 94.38 94.52 17 282.17 

As we can see, the proposed ADF method is more accurate than other training methods, either the on-line ones or the batch one. It is a bit surprising that the ADF method performs even more accurately than the batch training method (LBFGS). We notice that some previous work also found that on-line training methods could have better performance than batch training methods such as LBFGS (Tsuruoka, Tsujii, and Ananiadou 2009; Schaul, Zhang, and LeCun 2012). The ADF training method can achieve better results probably because the feature-frequency–adaptive training schema can produce more balanced training of features with diversified frequencies. Traditional SGD training may over-train high frequency features and at the same time may have insufficient training of low frequency features. The ADF training method can avoid such problems. It will be interesting to perform further analysis in future work.

We also performed significance tests based on t-tests with a significance level of 0.05. Significance tests demonstrate that the ADF method is significantly more accurate than the existing training methods in most of the comparisons, whether on-line or batch. For the Bio-NER task, the differences between ADF and LBFGS, SGD, Perc, and Avg-Perc are significant. For the word segmentation task, the differences between ADF and LBFGS, SGD, Perc, and Avg-Perc are significant. For the phrase chunking task, the differences between ADF and Perc and Avg-Perc are significant; the differences between ADF and LBFGS and SGD are non-significant.

Moreover, as we can see, the proposed method achieves a convergence state with the least number of training passes, and with the least wall-clock time. In general, the ADF method is about one order of magnitude faster than the LBFGS batch training method and several times faster than the existing on-line training methods.

4.6.2 Comparisons with State-of-the-Art Systems

The three tasks are well-known benchmark tasks with standard data sets. There is a large amount of published research on those three tasks. We compare the proposed method with the state-of-the-art systems. The comparisons are shown in Table 5.

Table 5 

Comparing our results with some representative state-of-the-art systems.

Bio-NER
Method
F-score
(Okanohara et al. 2006Semi-Markov CRF + global features 71.5 
(Hsu et al. 2009CRF + PSA(1) training 69.4 
(Tsuruoka, Tsujii, and Ananiadou 2009CRF + SGD-L1 training 71.6 
Our Method CRF + ADF training 72.3 
 
Segmentation Method F-score 
(Gao et al. 2007Semi-Markov CRF 97.2 
(Sun, Zhang, et al. 2009Latent-variable CRF 97.3 
(Sun 2010Multiple segmenters + voting 96.9 
Our Method CRF + ADF training 97.5 
 
Chunking Method F-score 
(Kudo and Matsumoto 2001Combination of multiple SVM 94.2 
(Vishwanathan et al. 2006CRF + SMD training 93.6 
(Sun et al. 2008Latent-variable CRF 94.3 
Our Method CRF + ADF training 94.5 
Bio-NER
Method
F-score
(Okanohara et al. 2006Semi-Markov CRF + global features 71.5 
(Hsu et al. 2009CRF + PSA(1) training 69.4 
(Tsuruoka, Tsujii, and Ananiadou 2009CRF + SGD-L1 training 71.6 
Our Method CRF + ADF training 72.3 
 
Segmentation Method F-score 
(Gao et al. 2007Semi-Markov CRF 97.2 
(Sun, Zhang, et al. 2009Latent-variable CRF 97.3 
(Sun 2010Multiple segmenters + voting 96.9 
Our Method CRF + ADF training 97.5 
 
Chunking Method F-score 
(Kudo and Matsumoto 2001Combination of multiple SVM 94.2 
(Vishwanathan et al. 2006CRF + SMD training 93.6 
(Sun et al. 2008Latent-variable CRF 94.3 
Our Method CRF + ADF training 94.5 

As we can see, our system is competitive with the best systems for the Bio-NER, word segmentation, and NP-chunking tasks. Many of the state-of-the-art systems use extra resources (e.g., linguistic knowledge) or complicated systems (e.g., voting over multiple models). Thus, it is impressive that our single model–based system without extra resources achieves good performance. This indicates that the proposed ADF training method can train model parameters with good generality on the test data.

4.6.3 Training Curves

To study the detailed training process and convergence speed, we show the training curves in Figures 2,34. Figure 2 focuses on the comparisons between the ADF method and the existing on-line training methods. As we can see, the ADF method converges faster than other on-line training methods in terms of both training passes and wall-clock time. The ADF method has roughly the same training speed per pass compared with traditional SGD training.

Figure 2 

Comparisons among the ADF method and other on-line training methods. (Top Row) Comparisons based on training passes. As we can see, the ADF method has the best accuracy and with the fastest convergence speed based on training passes. (Bottom Row) Comparisons based on wall-clock time.

Figure 2 

Comparisons among the ADF method and other on-line training methods. (Top Row) Comparisons based on training passes. As we can see, the ADF method has the best accuracy and with the fastest convergence speed based on training passes. (Bottom Row) Comparisons based on wall-clock time.

Figure 3 

(Top Row) Comparing ADF and ADF-noRich with CW and AROW methods. As we can see, both the ADF and ADF-noRich methods work better than the CW and AROW methods. (Bottom Row) Comparing different methods with mini-batch = 10 in the stochastic learning setting.

Figure 3 

(Top Row) Comparing ADF and ADF-noRich with CW and AROW methods. As we can see, both the ADF and ADF-noRich methods work better than the CW and AROW methods. (Bottom Row) Comparing different methods with mini-batch = 10 in the stochastic learning setting.

Figure 4 

Comparisons between the ADF method and the batch training method LBFGS. (Top Row) Comparisons based on training passes. As we can see, the ADF method converges much faster than the LBFGS method, and with better accuracy on the convergence state. (Bottom Row) Comparisons based on wall-clock time.

Figure 4 

Comparisons between the ADF method and the batch training method LBFGS. (Top Row) Comparisons based on training passes. As we can see, the ADF method converges much faster than the LBFGS method, and with better accuracy on the convergence state. (Bottom Row) Comparisons based on wall-clock time.

Figure 3 (Top Row) focuses on comparing the ADF method with the CW method (Dredze, Crammer, and Pereira 2008) and the AROW method (Crammer, Kulesza, and Dredze 2009). Comparisons are based on similar features. As discussed before, the ADF-noRich method is a simplified system, with rich edge features removed from the CRF-ADF system. As we can see, the proposed ADF method, whether with or without rich edge features, outperforms the CW and AROW methods. Figure 3 (Bottom Row) focuses on the comparisons with different mini-batch (the training samples in each stochastic update) sizes. Representative results with a mini-batch size of 10 are shown. In general, we find larger mini-batch sizes will slow down the convergence speed. Results demonstrate that, compared with the SGD training method, the ADF training method is less sensitive to mini-batch sizes.

Figure 4 focuses on the comparisons between the ADF method and the batch training method LBFGS. As we can see, the ADF method converges at least one order magnitude faster than the LBFGS training in terms of both training passes and wall-clock time. For the LBFGS training, we need to determine the LBFGS memory parameter m, which controls the number of prior gradients used to approximate the Hessian information. A larger value of m will potentially lead to more accurate estimation of the Hessian information, but at the same time will consume significantly more memory. Roughly, the LBFGS training consumes m times more memory than the ADF on-line training method. For most tasks, the default setting of m = 10 is reasonable. We set m = 10 for the word segmentation and phrase chunking tasks, and m = 6 for the Bio-NER task due to the shortage of memory for m > 6 cases in this task.

4.6.4 One-Pass Learning Results

Many real-world data sets can only observe the training data in one pass. For example, some Web-based on-line data streams can only appear once so that the model parameter learning should be finished in one-pass learning (see Zinkevich et al. 2010). Hence, it is important to test the performance in the one-pass learning scenario.

In the one-pass learning scenario, the feature frequency information is computed “on the fly” during on-line training. As shown in Section 3.1, we only need to have a real-valued vector to record the cumulative feature frequency information, which is updated when observing training instances one by one. Then, the learning rate vector is updated based on the only and there is no need to observe the training instances again. This is the same algorithm introduced in Section 3.1 and no change is required for the one-pass learning scenario. Figure 5 shows the comparisons between the ADF method and baselines on one-pass learning. As we can see, the ADF method consistently outperforms the baselines. This also reflects the fast convergence speed of the ADF training method.

Figure 5 

Comparisons among different methods based on one-pass learning. As we can see, the ADF method has the best accuracy on one-pass learning.

Figure 5 

Comparisons among different methods based on one-pass learning. As we can see, the ADF method has the best accuracy on one-pass learning.

4.7 Non-Structured Classification Results

In previous experiments, we showed that the proposed method outperforms existing baselines on structured classification. Nevertheless, we want to show that the ADF method also has good performance on non-structured classification. In addition, this task is based on real-valued features instead of binary features.

Experimental results of different training methods on the convergence state are shown in Table 6. As we can see, the proposed method outperforms all of the on-line and batch baselines in terms of binary classification accuracy. Here again we observe that the ADF and SGD methods outperform the LBFGS baseline.

Table 6 

Results on sentiment classification (non-structured binary classification).


Accuracy
Passes
Train-Time (sec)
LBFGS (batch) 87.00 86 72.20 
SGD (on-line) 87.13 44 55.88 
Perc (on-line) 84.55 25 5.82 
Avg-Perc (on-line) 85.04 46 12.22 
ADF (proposal) 87.89 30 57.12 

Accuracy
Passes
Train-Time (sec)
LBFGS (batch) 87.00 86 72.20 
SGD (on-line) 87.13 44 55.88 
Perc (on-line) 84.55 25 5.82 
Avg-Perc (on-line) 85.04 46 12.22 
ADF (proposal) 87.89 30 57.12 

The training curves are shown in Figure 6. As we can see, the ADF method converges quickly. Because this data set is relatively small and the feature dimension is much smaller than previous tasks, we find the baseline training methods also have fast convergence speed. The comparisons on one-pass learning are shown in Figure 7. Just as for the experiments for structured classification tasks, the ADF method outperforms the baseline methods on one-pass learning, with more than 12.7% error rate reduction.

Figure 6 

F-score curves on sentiment classification. (Top Row) Comparisons among the ADF method and on-line training baselines, based on training passes and wall-clock time, respectively. (Bottom Row) Comparisons between the ADF method and the batch training method LBFGS, based on training passes and wall-clock time, respectively. As we can see, the ADF method outperforms both the on-line training baselines and the batch training baseline, with better accuracy and faster convergence speed.

Figure 6 

F-score curves on sentiment classification. (Top Row) Comparisons among the ADF method and on-line training baselines, based on training passes and wall-clock time, respectively. (Bottom Row) Comparisons between the ADF method and the batch training method LBFGS, based on training passes and wall-clock time, respectively. As we can see, the ADF method outperforms both the on-line training baselines and the batch training baseline, with better accuracy and faster convergence speed.

Figure 7 

One-pass learning results on sentiment classification.

Figure 7 

One-pass learning results on sentiment classification.

5. Proofs

This section gives proofs of Theorems 1–4.

Proof of Theorem 1 Following Equation (5), the ADF update rule is = . For ,
formula
where ai and bi are the ith elements of the vector and , respectively. FSGD is the SGD update rule with the fixed learning rate γmax such that γmax := sup{γiwhereγi}. In other words, for the SGD update rule FSGD, the fixed learning rate γmax is derived from the ADF update rule. According to Lemma 1, the SGD update rule FSGD is a contraction mapping in Euclidean space with Lipschitz continuity degree , given the condition that . Hence, it goes to
formula
Combining Equations (6) and (7), it goes to
formula
Thus, according to the definition of dynamic contraction mapping, the ADF update rule is a dynamic contraction mapping in Euclidean space with Lipschitz continuity degree .
Proof of Theorem 2 As presented in Equation (5), the ADF update rule is := . For ,
formula
where ai is the ith element of the vector . bi and FSGD are the same as before. Similar to the analysis of Theorem 1, the third step of Equation (8) is valid because γmax is the maximum learning rate at the beginning and all learning rates are decreasing when t is increasing. The proof can be easily derived following the same steps in the proof of Theorem 1. To avoid redundancy, we do not repeat the derivation.
Proof of Theorem 3 Let M be the accumulative change of the ADF weight vector :
formula
To prove the convergence of the ADF, we need to prove the sequence Mt converges as t → ∞. Following Theorem 2, we have the following formula for the ADF training:
formula
where γmax is the maximum learning rate at the beginning. Let and , then we have:
formula
When t→ ∞, d0/(1 − qt)/(1 − q) goes to d0/(1 − q) because q < 1. Hence, we have:
formula
Thus, Mt is upper-bounded. Because we know that Mt is a monotonically increasing function when t→ ∞, it follows that Mt converges when t→ ∞. This completes the proof.
Proof of Theorem 4 First, we have
formula
Then, we have
formula
This is because given 0 ≤ aj < 1. Finally, because when t → ∞, we have
formula
This completes the proof.

6. Conclusions

In this work we tried to simultaneously improve the training speed and model accuracy of natural language processing systems. We proposed the ADF on-line training method, based on the core idea that high frequency features should result in a learning rate that decays faster. We demonstrated that the ADF on-line training method is convergent and has good theoretical properties. Based on empirical experiments, we can state the following conclusions. First, the ADF method achieved the major target of this work: faster training speed and higher accuracy at the same time. Second, the ADF method was robust: It had good performance on several structured and non-structured classification tasks with very different characteristics. Third, the ADF method worked well on both binary features and real-valued features. Fourth, the ADF method outperformed existing methods in a one-pass learning setting. Finally, our method achieved state-of-the-art performance on several well-known benchmark tasks. To the best of our knowledge, our simple method achieved a much better F-score than the existing best reports on the biomedical named entity recognition task.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (no. 61300063, no. 61370117), the Doctoral Fund of Ministry of Education of China (no. 20130001120004), a Hong Kong Polytechnic University internal grant (4-ZZD5), a Hong Kong RGC Project (no. PolyU 5230/08E), the National High Technology Research and Development Program of China (863 Program, no. 2012AA011101), and the Major National Social Science Fund of China (no. 12&ZD227). This work is a substantial extension of the conference version presented at ACL 2012 (Sun, Wang, and Li 2012).

Notes

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Author notes

*

Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, Beijing, China, and School of EECS, Peking University, Beijing, China. E-mail: xusun@pku.edu.cn.

**

Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong. E-mail: cswjli@comp.polyu.edu.hk.

Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, Beijing, China, and School of EECS, Peking University, Beijing, China. E-mail: wanghf@pku.edu.cn.

Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong. E-mail: csluqin@comp.polyu.edu.hk.