A High-Performance Semi-Supervised Learning Method for Text Chunking
 Rie Kubota AndoÝ Tong ZhangÞ
 
IBM T.J. Watson Research Center Yorktown Heights, NY 10598, U.S.A.
 Ýrie1@us.ibm.com Þtongz@us.ibm.com
 
Abstract
 In machine learning, whether one can build a more accurate classifier by using 
unlabeled data (semi-supervised learning) is an important issue. Although a 
number of semi-supervised methods have been proposed, their effectiveness on NLP 
tasks is not always clear. This paper presents a novel semi-supervised method 
that employs a learning paradigm which we call structural learning. The idea is 
to find "what good classifiers are like" by learning from thousands of 
automatically generated auxiliary classification problems on unlabeled data. By 
doing so, the common predictive structure shared by the multiple classification 
problems can be discovered, which can then be used to improve performance on the 
target problem. The method produces performance higher than the previous best 
results on CoNLL'00 syntactic chunking and CoNLL'03 named entity chunking 
(English and German).
 
(Blum and Mitchell, 1998) automatically bootstraps labels, and such labels are 
not necessarily reliable (Pierce and Cardie, 2001). A related idea is to use 
Expectation Maximization (EM) to impute labels. Although useful under some 
circumstances, when a relatively large amount of labeled data is available, the 
procedure often degrades performance (e.g. Merialdo (1994)). A number of 
bootstrapping methods have been proposed for NLP tasks (e.g. Yarowsky (1995), 
Collins and Singer (1999), Riloff and Jones (1999)). But these typically assume 
a very small amount of labeled data and have not been shown to improve 
state-of-the-art performance when a large amount of labeled data is available. 
Our goal has been to develop a general learning framework for reliably using 
unlabeled data to improve performance irrespective of the amount of labeled data 
available. It is exactly this important and difficult problem that we tackle 
here. This paper presents a novel semi-supervised method that employs a learning 
framework called structural learning (Ando and Zhang, 2004), which seeks to 
discover shared predictive structures (i.e. what good classifiers for the task 
are like) through jointly learning multiple classification problems on unlabeled 
data. That is, we systematically create thousands of problems (called auxiliary 
problems) relevant to the target task using unlabeled data, and train 
classifiers from the automatically generated `training data'. We learn the 
commonality (or structure) of such many classifiers relevant to the task, and 
use it to improve performance on the target task. One example of such auxiliary 
problems for chunking tasks is to `mask' a word and predict whether it is 
"people" or not from the context, like language modeling. Another example is to 
predict the pre-
 
1 Introduction
 In supervised learning applications, one can often find a large amount of 
unlabeled data without difficulty, while labeled data are costly to obtain. 
Therefore, a natural question is whether we can use unlabeled data to build a 
more accurate classifier, given the same amount of labeled data. This problem is 
often referred to as semi-supervised learning. Although a number of 
semi-supervised methods have been proposed, their effectiveness on NLP tasks is 
not always clear. For example, co-training
 
Proceedings of the 43rd Annual Meeting of the ACL, pages 1­9, Ann Arbor, June 
2005. c 2005 Association for Computational Linguistics
 
fidiction of some classifier trained for the target task. These auxiliary 
classifiers can be adequately learned since we have very large amounts of 
`training data' for them, which we automatically generate from a very large 
amount of unlabeled data. The contributions of this paper are two-fold. First, 
we present a novel robust semi-supervised method based on a new learning model 
and its application to chunking tasks. Second, we report higher performance than 
the previous best results on syntactic chunking (the CoNLL'00 corpus) and named 
entity chunking (the CoNLL'03 English and German corpora). In particular, our 
results are obtained by using unlabeled data as the only additional resource 
while many of the top systems rely on hand-crafted resources such as large name 
gazetteers or even rulebased post-processing.
 
model complexity. ERM-based methods for discriminative learning are known to be 
effective for NLP tasks such as chunking (e.g. Kudoh and Matsumoto (2001), Zhang 
and Johnson (2003)). 2.2 Linear model for structural learning
 
We present a linear prediction model for structural learning, which extends the 
traditional model to multiple problems. Specifically, we assume that there 
exists a low-dimensional predictive structure shared by multiple prediction 
problems. We seek to discover this structure through joint empirical risk 
minimization over the multiple problems. Consider Ñ problems indexed by ¾ ½ Ñ, 
each with Ò samples ´ µ indexed by ¾ ½ Ò . In our joint linear model, a 
predictor for problem takes the following form
 
2 A Model for Learning Structures
 This work uses a linear formulation of structural learning. We first briefly 
review a standard linear prediction model and then extend it for structural 
learning. We sketch an optimization algorithm using SVD and compare it to 
related methods. 2.1 Standard linear prediction model
 
In the standard formulation of supervised learning, we seek a predictor that 
maps an input vector ¾ to the corresponding output Ý ¾ . Linear prediction 
models are based on real-valued predictors of Ì the form ´Üµ where is called a 
weight vector. For binary problems, the sign of the linear prediction gives the 
class label. For -way classification (with ¾), a typical method is winner takes 
all, where we train one predictor per class and choose the class with the 
highest output value. A frequently used method for finding an accurate predictor 
is regularized empirical risk minimization (ERM), which minimizes an empirical 
loss of the predictor (with regularization) on the Ò training examples ´ µ:
 
where we use to denote the identity matrix. Matrix ¢ (whose rows are 
orthonormal) is the common structure parameter shared by all the problems; and 
are weight vectors specific to each prediction problem . The idea of this model 
is to discover a common low-dimensional predictive structure (shared by the Ñ 
problems) parameterized by the projection matrix ¢. In this setting, the goal of 
structural learning may also be regarded as learning a good feature map ¢Ü -- a 
low-dimensional feature vector parameterized by ¢. In joint ERM, we seek ¢ (and 
weight vectors) that minimizes the empirical risk summed over all the problems:
 
It can be shown that using joint ERM, we can reliably estimate the optimal joint 
parameter ¢ as long as Ñ is large (even when each Ò is small). This is the key 
reason why structural learning is effective. A formal PAC-style analysis can be 
found in (Ando and Zhang, 2004). 2.3 Alternating structure optimization (ASO)
 
Ä´¡µ is a loss function to quantify the difference
 
between the prediction ´ µ and the true output , and Ö´¡µ is a regularization 
term to control the
 
The optimization problem (2) has a simple solution using SVD when we choose 
square regularization:
 
ter is given. For clarity, let be a weight vector · ¢Ì Then, for problem such 
that: (2) becomes the minimization of the joint empirical risk written as:
 
Input: training data ( Ñ) Parameters: dimension and regularization param Output: 
matrix with rows Ñµ, and arbitrary Initialize: iterate to Ñ do for With fixed 
and , solve for :
 
This minimization can be approximately solved by the following alternating 
optimization procedure:
 
minimizes the joint empirical risk (3). , and find ´¢ minimizes the joint 
empirical risk (3).
 
that
 
Ì Let endfor Compute the SVD of ½ Ñ Let the rows of be the left singular vectors 
of corresponding to the largest singular values. until converge
 
Figure 1: SVD-based Alternating Structure Optimization
 (SVD-ASO) Algorithm
 
¯ Iterate until a convergence criterion is met.
 In the first step, we train Ñ predictors independently. It is the second step 
that couples all the problems. Its solution is given by the SVD (singular value 
decomposition) of the predictor matrix ½ Ñ : the rows of the optimum ¢ are given 
by the most sigIntuitively, the nificant left singular vectors1 of optimum ¢ 
captures the maximal commonality of the Ñ predictors (each derived from ). These 
Ñ predictors are updated using the new structure matrix ¢ in the next iteration, 
and the process repeats. Figure 1 summarizes the algorithm sketched above, which 
we call the alternating structure optimization (ASO) algorithm. The formal 
derivation can be found in (Ando and Zhang, 2004).
 
Comparison with existing techniques
 
It is important to note that this SVD-based ASO (SVD-ASO) procedure is 
fundamentally different from the usual principle component analysis (PCA), which 
can be regarded as dimension reduction in the data space . By contrast, the 
dimension reduction performed in the SVD-ASO algorithm is on the predictor space 
(a set of predictors). This is possible because we observe multiple predictors 
from multiple learning tasks. If we regard the observed predictors as sample 
points of the predictor distribution in
 1 is computed so that the best low-rank In other words, in the least square 
sense is obtained by approximation of see e.g. Golub and Loan projecting onto 
the row space of (1996) for SVD.
 
the predictor space (corrupted with estimation error, or noise), then SVD-ASO 
can be interpreted as finding the "principle components" (or commonality) of 
these predictors (i.e., "what good predictors are like"). Consequently the 
method directly looks for low-dimensional structures with the highest predictive 
power. By contrast, the principle components of input data in the data space 
(which PCA seeks) may not necessarily have the highest predictive power. The 
above argument also applies to the feature generation from unlabeled data using 
LSI (e.g. Ando (2004)). Similarly, Miller et al. (2004) used word-cluster 
memberships induced from an unannotated corpus as features for named entity 
chunking. Our work is related but more general, because we can explore 
additional information from unlabeled data using many different auxiliary 
problems. Since Miller et al. (2004)'s experiments used a proprietary corpus, 
direct performance comparison is not possible. However, our preliminary 
implementation of the word clustering approach did not provide any improvement 
on our tasks. As we will see, our starting performance is already high. 
Therefore the additional information discovered by SVD-ASO appears crucial to 
achieve appreciable improvements.
 
3 Semi-supervised Learning Method
 For semi-supervised learning, the idea is to create many auxiliary prediction 
problems (relevant to the task) from unlabeled data so that we can learn the
 
fishared structure ¢ (useful for the task) using the ASO algorithm. In 
particular, we want to create auxiliary problems with the following properties:
 
we need to automatically generate various "labeled" data for the auxiliary 
problems from unlabeled data. auxiliary problems should be related to the target 
problem. That is, they should share a certain predictive structure.
 
The final classifier for the target task is in the form of (1), a linear 
predictor for structural learning. We fix ¢ (learned from unlabeled data through 
auxiliary problems) and optimize weight vectors and on the given labeled data. 
We summarize this semisupervised learning procedure below.
 
Ex 3.1 Predict words. Create auxiliary problems by regarding the word at each 
position as an auxiliary label, which we want to predict from the context. For 
instance, predict whether a word is "Smith" or not from its context. This 
problem is relevant to, for instance, named entity chunking since knowing a word 
is "Smith" helps to predict whether it is part of a name. One binary 
classification problem can be created for each possible word value (e.g., "IBM", 
"he", "get", ¡ ¡ ¡ ). Hence, many auxiliary problems can be obtained using this 
idea. More generally, given a feature representation of the input data, we may 
mask some features as unobserved, and learn classifiers to predict these 
`masked' features based on other features that are not masked. The 
automatic-labeling requirement is satisfied since the auxiliary labels are 
observable to us. To create relevant problems, we should choose to (mask and) 
predict features that have good correlation to the target classes, such as words 
on text tagging/chunking tasks. 3.1.2 Partially-supervised strategy
 
Auxiliary problem creation
 
The idea is to discover useful features (which do not necessarily appear in the 
labeled data) from the unlabeled data through learning auxiliary problems. 
Clearly, auxiliary problems more closely related to the target problem will be 
more beneficial. However, even if some problems are less relevant, they will not 
degrade performance severely since they merely result in some irrelevant 
features (originated from irrelevant ¢-components), which ERM learners can cope 
with. On the other hand, potential gains from relevant auxiliary problems can be 
significant. In this sense, our method is robust. We present two general 
strategies for generating useful auxiliary problems: one in a completely 
unsupervised fashion, and the other in a partiallysupervised fashion. 3.1.1 
Unsupervised strategy In the first strategy, we regard some observable as 
auxiliary class substructures of the input data labels, and try to predict these 
labels using other parts of the input data.
 
The second strategy is motivated by co-training. We use two (or more) distinct 
feature maps: ½ and ¾ . First, we train a classifier ½ for the target task, 
using the feature map ½ and the labeled data. The auxiliary tasks are to predict 
the behavior of this classifier ½ (such as predicted labels) on the unlabeled 
data, by using the other feature map ¾ . Note that unlike co-training, we only 
use the classifier as a means of creating auxiliary problems that meet the 
relevancy requirement, instead of using it to bootstrap labels.
 
Ex 3.2 Predict the top- choices of the classifier. Predict the combination of (a 
few) classes to which ½ assigns the highest output (confidence) values. For 
instance, predict whether ½ assigns the highest confidence values to CLASS1 and 
CLASS2 in this order. By setting ½, the auxiliary task is simply to predict the 
label prediction of classifier ½ . By set½, fine-grained distinctions (related 
to inting trinsic sub-classes of target classes) can be learned. From a 
-way 
classification problem, 
 ´
 µ binary prediction problems can be created.
 
fi4 Algorithms Used in Experiments
 Using auxiliary problems introduced above, we study the performance of our 
semi-supervised learning method on named entity chunking and syntactic chunking. 
This section describes the algorithmic aspects of the experimental framework. 
The taskspecific setup is described in Sections 5 and 6. 4.1 Extension of the 
basic SVD-ASO algorithm
 
In our experiments, we use an extension of SVDASO. In NLP applications, features 
have natural grouping according to their types/origins such as `current words', 
`parts-of-speech on the right', and so forth. It is desirable to perform a 
localized optimization for each of such natural feature groups. Hence, we 
associate each feature group with a submatrix of structure matrix ¢. The 
optimization algorithm for this extension is essentially the same as SVD-ASO in 
Figure 1, but with the SVD step performed separately for each group. See (Ando 
and Zhang, 2004) for the precise formulation. In addition, we regularize only 
those components of which correspond to the non-negative part of . The 
motivation is that positive weights are usually directly related to the target 
concept, while negative ones often yield much less specific information 
representing `the others'. The resulting extension, in effect, only uses the 
positive components of in the SVD computation.
 
(Zhang, 2004). As we will show in Section 7.3, our formulation (rowis relatively 
insensitive to the change in dimension of the structure matrix). We fix (for 
each feature group) to 50, and use it in all settings. The most time-consuming 
process is the training of Ñ auxiliary predictors on the unlabeled data in 
Figure 1). Fixing the number of (computing iterations to a constant, it runs in 
linear to Ñ and the number of unlabeled instances and takes hours in our 
settings that use more than 20 million unlabeled instances.
 
Baseline algorithms
 
Supervised classifier For comparison, we train a classifier using the same 
features and algorithm, but in effect). without unlabeled data (¢
 
Chunking algorithm, loss function, training algorithm, and parameter settings
 
Co-training We test co-training since our idea of partially-supervised auxiliary 
problems is motivated by co-training. Our implementation follows the original 
work (Blum and Mitchell, 1998). The two (or more) classifiers (with distinct 
feature maps) are trained with labeled data. We maintain a pool of Õ unlabeled 
instances by random selection. The classifier proposes labels for the instances 
in this pool. We choose × instances for each classifier with high confidence 
while preserving the class distribution observed in the initial labeled data, 
and add them to the labeled data. The process is then repeated. We explore 
Õ=50K, 100K, ×=50,100,500,1K, and commonly-used feature splits: `current vs. 
context' and `current+left-context vs. current+right-context'. Self-training 
Single-view bootstrapping is sometimes called self-training. We test the basic 
selftraining2 , which replaces multiple classifiers in the co-training procedure 
with a single classifier that employs all the features. co/self-training oracle 
performance To avoid the issue of parameter selection for the co- and 
selftraining, we report their best possible oracle performance, which is the 
best F-measure number among all the co- and self-training parameter settings 
including the choice of the number of iterations.
 2 We also tested "self-training with bagging", which Ng and Cardie (2003) used 
for co-reference resolution. We omit results since it did not produce better 
performance than the supervised baseline.
 
As is commonly done, we encode chunk information into word tags to cast the 
chunking problem to that of sequential word tagging. We perform Viterbistyle 
decoding to choose the word tag sequence that maximizes the sum of tagging 
confidence values. In all settings (including baseline methods), the loss 
function is a modification of the Huber's roÑ Ü´¼ ½ bust loss for regression: 
Ä´Ô Ýµ ÔÝµ¾ if ÔÝ ½; and ÔÝ otherwise; with square regularization ( ½¼ ). One 
may select other loss functions such as SVM or logistic regression. The specific 
choice is not important for the purpose of this paper. The training algorithm is 
stochastic gradient descent, which is argued to perform well for regularized 
convex ERM learning formulations
 
words, parts-of-speech (POS), character types, 4 characters at the 
beginning/ending in a 5-word window. words in a 3-syntactic chunk window. labels 
assigned to two words on the left. bi-grams of the current word and the label on 
the left. labels assigned to previous occurrences of the current word.
 
Figure 2: Feature types for named entity chunking. POS and
 syntactic chunk information is provided by the organizer.
 
# of aux. problems 1000 1000 1000 72 72 72 72
 
Auxiliary labels previous words current words next words ½ 's top-2 choices ¾ 's 
top-2 choices ¿ 's top-2 choices 's top-2 choices
 
Features used for learning aux problems all but previous words all but current 
words all but next words ¾ (all but left context) ½ (left context) (all but 
right context) ¿ (right context)
 
Figure 3: Auxiliary problems used for named entity chunking. 3000 problems 
`mask' words and predict them from the other features on unlabeled data. 288 
problems predict classifier 's predictions on unlabeled data, where is trained 
with labeled data using feature map . There are 72 possible top-2 choices from 9 
classes (beginning/inside of four types of name chunks and `outside').
 
5 Named Entity Chunking Experiments
 We report named entity chunking performance on the CoNLL'03 shared-task3 corpora 
(English and German). We choose this task because the original intention of this 
shared task was to test the effectiveness of semi-supervised learning methods. 
However, it turned out that none of the top performing systems used unlabeled 
data. The likely reason is that the number of labeled data is relatively large ( 
200K), making it hard to benefit from unlabeled data. We show that our ASO-based 
semi-supervised learning method (hereafter, ASO-semi) can produce results 
appreciably better than all of the top systems, by using unlabeled data as the 
only additional resource. In particular, we do not use any gazetteer 
information, which was used in all other systems. The CoNLL corpora are 
annotated with four types of named entities: persons, organizations, locations, 
and miscellaneous names (e.g., "World Cup"). We use the official 
training/development/test splits. Our unlabeled data sets consist of 27 million 
words (English) and 35 million words (German), respectively. They were chosen 
from the same sources ­ Reuters and ECI Multilingual Text Corpus ­ as the 
provided corpora but disjoint from them. 5.1 Features
 
of the classifier" using feature splits `left context vs. the others' and `right 
context vs. the others'. For word-prediction problems, we only consider the 
instances whose current words are either nouns or adjectives since named 
entities mostly consist of these types. Also, we leave out all but at most 1000 
binary prediction problems of each type that have the largest numbers of 
positive examples to ensure that auxiliary predictors can be adequately learned 
with a sufficiently large number of examples. The results we report are obtained 
by using all the problems in Figure 3 unless otherwise specified. 5.3 Named 
entity chunking results
 methods
 
Our feature representation is a slight modification of a simpler configuration 
(without any gazetteer) in (Zhang and Johnson, 2003), as shown in Figure 2. We 
use POS and syntactic chunk information provided by the organizer. 5.2 Auxiliary 
problems
 
test diff. from supervised data F prec. recall F English, small (10K examples) 
training set ASO-semi dev. 81.25 +10.02 +7.00 +8.51 co/self oracle 73.10 +0.32 
+0.39 +0.36 ASO-semi test 78.42 +9.39 +10.73 +10.10 co/self oracle 69.63 +0.60 
+1.95 +1.31 English, all (204K) training examples ASO-semi dev. 93.15 +2.25 
+3.00 +2.62 co/self oracle 90.64 +0.04 +0.20 +0.11 ASO-semi test 89.31 +3.20 
+4.51 +3.86 co/self oracle 85.40 German, all (207K) training examples ASO-semi 
dev. 74.06 +7.04 +10.19 +9.22 co/self oracle 66.47 +4.39 +1.63 ASO-semi test 
75.27 +4.64 +6.59 +5.88 co/self oracle 70.45 +2.59 +1.06
 
Figure 4: Named entity chunking results. No gazetteer. Fmeasure and performance 
improvements over the supervised baseline in precision, recall, and F. For co- 
and self-training (baseline), the oracle performance is shown.
 
As shown in Figure 3, we experiment with auxiliary problems from Ex 3.1 and 3.2: 
"Predict current (or previous or next) words"; and "Predict top-2 choices
 
Figure 4 shows results in comparison with the supervised baseline in six 
configurations, each trained
 
fiwith one of three sets of labeled training examples: a small English set (10K 
examples randomly chosen), the entire English training set (204K), and the 
entire German set (207K), tested on either the development set or test set. 
ASO-semi significantly improves both precision and recall in all the six 
configurations, resulting in improved F-measures over the supervised baseline by 
+2.62% to +10.10%. Co- and self-training, at their oracle performance, improve 
recall but often degrade precision; consequently, their F-measure improvements 
are relatively low: 0.05% to +1.63%. Comparison with top systems As shown in 
Figure 5, ASO-semi achieves higher performance than the top systems on both 
English and German data. Most of the top systems boost performance by external 
hand-crafted resources such as: large gazetteers4 ; a large amount (2 million 
words) of labeled data manually annotated with finer-grained named entities 
(FIJZ03); and rule-based post processing (KSNM03). Hence, we feel that our 
results, obtained by using unlabeled data as the only additional resource, are 
encouraging.
 System ASO-semi FIJZ03 CN03 KSNM03 Eng. 89.31 88.76 88.31 86.31 Ger. 75.27 72.41 
65.67 71.90 Additional resources unlabeled data gazetteers; 2M-word labeled data 
(English) gazetteers (English); (also very elaborated features) rule-based post 
processing
 
uni- and bi-grams of words and POS in a 5-token window. word-POS bi-grams in a 
3-token window. POS tri-grams on the left and right. labels of the two words on 
the left and their bi-grams. bi-grams of the current word and two labels on the 
left.
 
Figure 6: Feature types for syntactic chunking. POS information is provided by 
the organizer. prec. 93.83 94.57 93.76 recall 93.37 94.20 93.56
 
supervised ASO-semi co/self oracle
 
Figure 7: Syntactic chunking results. use the WSJ articles in 1991 (15 million 
words) from the TREC corpus as the unlabeled data. 6.1 Features and auxiliary 
problems
 
Our feature representation is a slight modification of a simpler configuration 
(without linguistic features) in (Zhang et al., 2002), as shown in Figure 6. We 
use the POS information provided by the organizer. The types of auxiliary 
problems are the same as in the named entity experiments. For word predictions, 
we exclude instances of punctuation symbols. 6.2 Syntactic chunking results
 
Figure 5: Named entity chunking. F-measure on the test
 sets. Previous best results: FIJZ03 (Florian et al., 2003), CN03 (Chieu and Ng, 
2003), KSNM03 (Klein et al., 2003).
 
As shown in Figure 7, ASO-semi improves both precision and recall over the 
supervised baseline. It ¿ ± in F-measure, which outperforms achieves the 
supervised baseline by ¼ ±. Co- and selftraining again slightly improve recall 
but slightly degrade precision at their oracle performance, which demonstrates 
that it is not easy to benefit from unlabeled data on this task. Comparison with 
the previous best systems As shown in Figure 8, ASO-semi achieves performance 
higher than the previous best systems. Though the space constraint precludes 
providing the detail, we note that ASO-semi outperforms all of the previous top 
systems in both precision and recall. Unlike named entity chunking, the use of 
external resources on this task is rare. An exception is the use of output from 
a grammar-based full parser as features in ZDJ02+, which our system does not 
use. KM01 and CM03 boost performance by classifier combinations. SP03 trains 
conditional random fields for NP
 
6 Syntactic Chunking Experiments
 Next, we report syntactic chunking performance on the CoNLL'00 shared-task5 
corpus. The training and test data sets consist of the Wall Street Journal 
corpus (WSJ) sections 15­18 (212K words) and section 20, respectively. They are 
annotated with eleven types of syntactic chunks such as noun phrases. We
 4 Whether or not gazetteers are useful depends on their coverage. A number of 
top-performing systems used their own gazetteers in addition to the organizer's 
gazetteers and reported significant performance improvements (e.g., FIJZ03, 
CN03, and ZJ03). 5 http://cnts.uia.ac.be/conll2000/chunking
 
firow# ASO-semi KM01 CM03 SP03 ZDJ02 ZDJ02+ all 94.39 93.91 93.74 ­ 93.57 94.17 
NP 94.70 94.39 94.41 94.38 93.89 94.38 description +unlabeled data SVM 
combination perceptron in two layers conditional random fields generalized 
Winnow +full parser output 4 7 9 11 15 26
 
Figure 8: Syntactic chunking F-measure. Comparison with
 previous best results: KM01 (Kudoh and Matsumoto, 2001), CM03 (Carreras and 
Marquez, 2003), SP03 (Sha and Pereira, 2003), ZDJ02 (Zhang et al., 2002).
 
Features corresponding to significant entries Ltd, Inc, Plc, International, 
Ltd., Association, Group, Inc. Co, Corp, Co., Company, Authority, Corp., 
Services PCT, N/A, Nil, Dec, BLN, Avg, Year-on-year, UNCH New, France, European, 
San, North, Japan, Asian, India Peter, Sir, Charles, Jose, Paul, Lee, Alan, Dan, 
John, James June, May, July, Jan, March, August, September, April
 
Interpretation organizations organizations no names locations persons months
 
(noun phrases) only. ASO-semi produces higher NP chunking performance than the 
others.
 
Figure 10: Interpretation of computed from wordprediction (unsupervised) 
problems for named entity chunking. words beginning with upper-case letters 
(i.e., likely to be names in English). Our method captures the spirit of 
predictive word-clustering but is more general and effective on our tasks. It is 
possible to develop a general theory to show that the auxiliary problems we use 
are helpful under reasonable conditions. The intuition is as follows. Suppose we 
split the features into two parts ½ and ¾ and predict ½ based on ¾. Suppose 
features in ½ are correlated to the class labels (but not necessarily correlated 
among themselves). Then, the auxiliary prediction problems are related to the 
target task, and thus can reveal useful structures of ¾ . Under some conditions, 
it can be shown that features in ¾ with similar predictive performance tend to 
map to similar low-dimensional vectors through ¢. This effect can be empirically 
observed in Figure 10 and will be formally shown elsewhere.
 
7 Empirical Analysis
 7.1 Effectiveness of auxiliary problems
 English named entity German named entity 90 76 89 74 88 72 87 70 86 68 85 1 
supervised set dev w/ "Predict (previous, current, or next) words" w/ "Predict 
top-2 choices" w/ "Predict words" + "Predict top-2 choices"
 
Figure 9: Named entity F-measure produced by using individual types of auxiliary 
problems. Trained with the entire training sets and tested on the test sets.
 
Figure 9 shows F-measure obtained by computing ¢ from individual types of 
auxiliary problems on named entity chunking. Both types ­ "Predict words" and 
"Predict top-2 choices of the classifier" ­ are useful, producing significant 
performance improvements over the supervised baseline. The best performance is 
achieved when ¢ is produced from all of the auxiliary problems. 7.2 
Interpretation of ¢
 
Effect of the ¢ dimension
 20 40 ASO-semi supervised 60 80 100
 
dimension
 
To gain insights into the information obtained from unlabeled data, we examine 
the ¢ entries associated with the feature `current words', computed for the 
English named entity task. Figure 10 shows the features associated with the 
entries of ¢ with the largest values, computed from the 2000 unsupervised 
auxiliary problems: "Predict previous words" and "Predict next words". For 
clarity, the figure only shows
 
Figure 11: F-measure in relation to the row-dimension of English named entity 
chunking, test set.
 
Recall that throughout the experiments, we fix the row-dimension of ¢ (for each 
feature group) to 50. Figure 11 plots F-measure in relation to the rowdimension 
of ¢, which shows that the method is relatively insensitive to the change of 
this parameter, at least in the range which we consider.
 
We presented a novel semi-supervised learning method that learns the most 
predictive lowdimensional feature projection from unlabeled data using the 
structural learning algorithm SVD-ASO. On CoNLL'00 syntactic chunking and 
CoNLL'03 named entity chunking (English and German), the method exceeds the 
previous best systems (including those which rely on hand-crafted resources) by 
using unlabeled data as the only additional resource. The key idea is to create 
auxiliary problems automatically from unlabeled data so that predictive 
structures can be learned from that data. In practice, it is desirable to create 
as many auxiliary problems as possible, as long as there is some reason to 
believe in their relevancy to the task. This is because the risk is relatively 
minor while the potential gain from relevant problems is large. Moreover, the 
auxiliary problems used in our experiments are merely possible examples. One 
advantage of our approach is that one may design a variety of auxiliary problems 
to learn various aspects of the target problem from unlabeled data. Structural 
learning provides a framework for carrying out possible new ideas.
 
Acknowledgments
 Part of the work was supported by ARDA under the NIMD program PNWD-SW-6059.
 
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