Supervised Grammar Induction using Training Data with Limited 
Constituent Information * 
Rebecca Hwa 
Division of Engineering and Applied Sciences 
Harvard University 
Cambridge, MA 02138 USA 
rebecca@eecs.harvard.edu 
Abstract 
Corpus-based grammar induction generally re- 
lies on hand-parsed training data to learn the 
structure of the language. Unfortunately, the 
cost of building large annotated corpora is pro- 
hibitively expensive. This work aims to improve 
the induction strategy when there are few labels 
in the training data. We show that the most in- 
formative linguistic constituents are the higher 
nodes in the parse trees, typically denoting com- 
plex noun phrases and sentential clauses. They 
account for only 20% of all constituents. For in- 
ducing grammars from sparsely labeled training 
data (e.g., only higher-level constituent labels), 
we propose an adaptation strategy, which pro- 
duces grammars that parse almost as well as 
grammars induced from fully labeled corpora. 
Our results suggest that for a partial parser to 
replace human annotators, it must be able to 
automatically extract higher-level constituents 
rather than base noun phrases. 
1 Introduction 
The availability of large hand-parsed corpora 
such as the Penn Treebank Project has made 
high-quality statistical parsers possible. How- 
ever, the parsers risk becoming too tailored to 
these labeled training data that they cannot re- 
liably process sentences from an arbitrary do- 
main. Thus, while a parser trained on the 
• Wall Street Journal corpus can fairly accurately 
parse a new Wall Street Journal article, it may 
not perform as well on a New Yorker article. 
To parse sentences from a new domain, one 
would normally directly induce a new grammar 
* This material is based upon work supported by the Na- 
tional Science Foundation under Grant No. IRI 9712068. 
We thank Stuart Shieber for his guidance, and Lillian 
Lee, Ric Crabbe, and the three anonymous reviewers for 
their comments on the paper. 
from that domain, in which the training pro- 
cess would require hand-parsed sentences from 
the new domain. Because parsing a large cor- 
pus by hand is a labor-intensive task, it would 
be beneficial to minimize the number of labels 
needed to induce the new grammar. 
We propose to adapt a grammar already 
trained on an old domain to the new domain. 
Adaptation can exploit the structural similar- 
ity between the two domains so that fewer la- 
beled data might be needed to update the gram- 
mar to reflect the structure of the new domain. 
This paper presents a quantitative study com- 
paring direct induction and adaptation under 
different training conditions. Our goal is to un- 
derstand the effect of the amounts and types 
of labeled data on the training process for both 
induction strategies. For example, how much 
training data need to be hand-labeled? Must 
the parse trees for each sentence be fully spec- 
ified? Are some linguistic constituents in the 
parse more informative than others? 
To answer these questions, we have performed 
experiments that compare the parsing quali- 
ties of grammars induced under different train- 
ing conditions using both adaptation and di- 
rect induction. We vary the number of labeled 
brackets and the linguistic classes of the labeled 
brackets. The study is conducted on both a sim- 
ple Air Travel Information System (ATIS) cor- 
pus (Hemphill et al., 1990) and the more com- 
plex Wall Street Journal (WSJ) corpus (Marcus 
et al., 1993). 
Our results show that the training examples 
do not need to be fully parsed for either strat- 
egy, but adaptation produces better grammars 
than direct induction under the conditions of 
minimally labeled training data. For instance, 
the most informative brackets, which label con- 
stituents higher up in the parse trees, typically 
73 
identifying complex noun phrases and senten- 
tial clauses, account for only 17% of all con- 
stituents in ATIS and 21% in WSJ. Trained on 
this type of label, the adapted grammars parse 
better than the directly induced grammars and 
almost as well as those trained on fully labeled 
data. Training on ATIS sentences labeled with 
higher-level constituent brackets, a directly in- 
duced grammar parses test sentences with 66% 
accuracy, whereas an adapted grammar parses 
with 91% accuracy, which is only 2% lower than 
the score of a grammar induced from fully la- 
beled training data. Training on WSJ sentences 
labeled with higher-level constituent brackets, 
a directly induced grammar parses with 70% 
accuracy, whereas an adapted grammar parses 
with 72% accuracy, which is 6% lower than the 
score of a grammar induced from fully labeled 
training data. 
That the most informative brackets are 
higher-level constituents and make up only one- 
fifth of all the labels in the corpus has two impli- 
cations. First, it shows that there is potential 
reduction of labor for the human annotators. 
Although the annotator still must process an 
entire sentence mentally, the task of identifying 
higher-level structures such as sentential clauses 
and complex nouns should be less tedious than 
to fully specify the complete parse tree for each 
sentence. Second, one might speculate the pos- 
sibilities of replacing human supervision alto- 
gether with a partial parser that locates con- 
stituent chunks within a sentence. However, 
as our results indicate that the most informa- 
tive constituents are higher-level phrases, the 
parser would have to identify sentential clauses 
and complex noun phrases rather than low-level 
base noun phrases. 
2 Related Work on Grammar 
Induction 
• Grammar induction is the process of inferring 
the structure of a language by learning from ex- 
ample sentences drawn from the language. The 
degree of difficulty in this task depends on three 
factors. First, it depends on the amount of 
supervision provided. Charniak (1996), for in- 
stance, has shown that a grammar can be easily 
constructed when the examples are fully labeled 
parse trees. On the other hand, if the examples 
consist of raw sentences with no extra struc- 
tural information, grammar induction is very 
difficult, even theoretically impossible (Gold, 
1967). One could take a greedy approach such 
as the well-known Inside-Outside re-estimation 
algorithm (Baker, 1979), which induces locally 
optimal grammars by iteratively improving the 
parameters of the grammar so that the entropy 
of the training data is minimized. In practice, 
however, when trained on unmarked data, the 
algorithm tends to converge on poor grammar 
models. For even a moderately complex domain 
such as the ATIS corpus, a grammar trained 
on data with constituent bracketing information 
produces much better parses than one trained 
on completely unmarked raw data (Pereira and 
Schabes, 1992). Part of our work explores the 
in-between case, when only some constituent la- 
bels are available. Section 3 defines the different 
types of annotation we examine. 
Second, as supervision decreases, the learning 
process relies more on search. The success of 
the induction depends on the initial parameters 
of the grammar because a local search strategy 
may converge to a local minimum. For finding 
a good initial parameter set, Lari and Young 
(1990) suggested first estimating the probabili- 
ties with a set of regular grammar rules. Their 
experiments, however, indicated that the main 
benefit from this type of pretraining is one 
of run-time efficiency; the improvement in the 
quality of the induced grammar was minimal. 
Briscoe and Waegner (1992) argued that one 
should first hand-design the grammar to en- 
code some linguistic notions and then use the re- 
estimation procedure to fine-tune the parame- 
ters, substituting the cost of hand-labeled train- 
ing data with that of hand-coded grammar. Our 
idea of grammar adaptation can be seen as a 
form of initialization. It attempts to seed the 
grammar in a favorable search space by first 
training it with data from an existing corpus. 
Section 4 discusses the induction strategies in 
more detail. 
A third factor that affects the learning pro- 
cess is the complexity of the data. In their study 
of parsing the WSJ, Schabes et al. (1993) have 
shown that a grammar trained on the Inside- 
Outside re-estimation algorithm can perform 
quite well on short simple sentences but falters 
as the sentence length increases. To take this 
factor into account, we perform our experiments 
74 
Categories Labeled Sentence 
HighP 
BaseNP 
BaseP 
AllNP 
(I want (to take (the flight with at most one stop))) 
(I) want to take (the flight) with (at most one stop) 
(I) want to take (the flight) with (at most one) stop 
(I) want to take ((the flight) with (at most one stop)) 
NotBaseP (I (want (to (take (the flight (with (at most one stop))))))) 
I ATIS I WSJ 
17% 21% 
27% 29% 
32% 30% 
37% 43% 
68% 70% 
Table 1: The second column shows how the example sentence ((I) (want (to (take ((the flight) 
(with ((at most one) stop))))))) is labeled under each category. The third and fourth columns list 
the percentage break-down of brackets in each category for ATIS and WSJ respectively. 
on both a simple domain (ATIS) and a complex 
one (WSJ). In Section 5, we describe the exper- 
iments and report the results. 
3 Training Data Annotation 
The training sets are annotated in multiple 
ways, falling into two categories. First, we con- 
struct training sets annotated with random sub- 
sets of constituents consisting 0%, 25~0, 50%, 
75% and 100% of the brackets in the fully an- 
notated corpus. Second, we construct sets train- 
ing in which only a certain type of constituent is 
annotated. We study five linguistic categories. 
Table 1 summarizes the annotation differences 
between the five classes and lists the percent- 
age of brackets in each class with respect to 
the total number of constituents 1 for ATIS and 
WSJ. In an AI1NP training set, all and only 
the noun phrases in the sentences are labeled. 
For the BaseNP class, we label only simple 
noun phrases that contain no embedded noun 
phrases. Similarly for a BaseP set, all sim- 
ple phrases made up of only lexical items are 
labeled. Although there is a high intersection 
between the set of BaseP labels and the set of 
BaseNP labels, the two classes are not identical. 
A BaseNP may contain a BaseP. For the exam- 
ple in Table 1, the phrase "at most one stop" 
is a BaseNP that contains a quantifier BaseP 
"at most one." NotBaseP is the complement 
of BaseP. The majority of the constituents in 
a sentence belongs to this category, in which at 
least one of the constituent's sub-constituents is 
not a simple lexical item. Finally, in a HighP 
set, we label only complex phrases that decom- 
1 For computing the percentage of brackets, the outer- 
most bracket around the entire sentence and the brack- 
ets around singleton phrases (e.g., the pronoun "r' as a 
BaseNP) are excluded because they do not contribute to 
the pruning of parses. 
pose into sub-phrases that may be either an- 
other HighP or a BaseP. That is, a HighP con- 
stituent does not directly subsume any lexical 
word. A typical HighP is a sentential clause or a 
complex noun phrase. The example sentence in 
Table 1 contains 3 HighP constituents: a com- 
plex noun phrase made up of a BaseNP and a 
prepositional phrase; a sentential clause with an 
omitted subject NP; and the full sentence. 
4 Induction Strategies 
To induce a grammar from the sparsely brack- 
eted training data previously described, we use 
a variant of the Inside-Outside re-estimation 
algorithm proposed by Pereira and Schabes 
(1992). The inferred grammars are repre- 
sented in the Probabilistic Lexicalized Tree In- 
sertion Grammar (PLTIG) formalism (Schabes 
and Waters, 1993; Hwa, 1998a), which is lexical- 
ized and context-free equivalent. We favor the 
PLTIG representation for two reasons. First, it 
is amenable to the Inside-Outside re-estimation 
algorithm (the equations calculating the inside 
and outside probabilities for PLTIGs can be 
found in Hwa (1998b)). Second, its lexicalized 
representation makes the training process more 
efficient than a traditional PCFG while main- 
taining comparable parsing qualities. 
Two training strategies are considered: di- 
rect induction, in which a grammar is induced 
from scratch, learning from only the sparsely la- 
beled training data; and adaptation, a two-stage 
learning process that first uses direct induction 
to train the grammar on an existing fully la- 
beled corpus before retraining it on the new cor- 
pus. During the retraining phase, the probabil- 
ities of the grammars are re-estimated based on 
the new training data. We expect the adaptive 
method to induce better grammars than direct 
induction when the new corpus is only partially 
75 
annotated because the adapted grammars have 
collected better statistics from the fully labeled 
data of another corpus. 
5 Experiments and Results 
We perform two experiments. The first uses 
ATIS as the corpus from which the different 
types of partially labeled training sets are gener- 
ated. Both induction strategies train from these 
data, but the adaptive strategy pretrains its 
grammars with fully labeled data drawn from 
the WSJ corpus. The trained grammars are 
scored on their parsing abilities on unseen ATIS 
test sets. We use the non-crossing bracket mea- 
surement as the parsing metric. This experi- 
ment will show whether annotations of a partic- 
ular linguistic category may be more useful for 
training grammars than others. It will also in- 
dicate the comparative merits of the two induc- 
tion strategies trained on data annotated with 
these linguistic categories. However, pretrain- 
ing on the much more complex WSJ corpus may 
be too much of an advantage for the adaptive 
strategy. Therefore, we reverse the roles of the 
corpus in the second experiment. The partially 
labeled data are from the WSJ corpus, and the 
adaptive strategy is pretrained on fully labeled 
ATIS data. In both cases, part-of-speech(POS) 
tags are used as the lexical items of the sen- 
tences. Backing off to POS tags is necessary 
because the tags provide a considerable inter- 
section in the vocabulary sets of the two cor- 
pora. 
5.1 Experiment 1: Learning ATIS 
The easier learning task is to induce grammars 
to parse ATIS sentences. The ATIS corpus con- 
sists of 577 short sentences with simple struc- 
tures, and the vocabulary set is made up of 32 
• POS tags, a subset of the 47 tags used for the 
WSJ. Due to the limited size of this corpus, ten 
sets of randomly partitioned train-test-held-out 
triples are generated to ensure the statistical 
significance of our results. We use 80 sentences 
for testing, 90 sentences for held-out data, and 
the rest for training. Before proceeding with 
the main discussion on training from the ATIS, 
we briefly describe the pretraining stage of the 
adaptive strategy. 
5.1.1 Pretraining with WSJ 
The idea behind the adaptive method is simply 
to make use of any existing labeled data. We 
hope that pretraining the grammars on these 
data might place them in a better position to 
learn from the new, sparsely labeled data. In 
the pretraining stage for this experiment, a 
grammar is directly induced from 3600 fully 
labeled WSJ sentences. Without any further 
training on ATIS data, this grammar achieves a 
parsing score of 87.3% on ATIS test sentences. 
The relatively high parsing score suggests that 
pretraining with WSJ has successfully placed 
the grammar in a good position to begin train- 
ing with the ATIS data. 
5.1.2 Partially Supervised Training on 
ATIS 
We now return to the main focus of this experi- 
ment: learning from sparsely annotated ATIS 
training data. To verify whether some con- 
stituent classes are more informative than oth- 
ers, we could compare the parsing scores of the 
grammars trained using different constituent 
class labels. But this evaluation method does 
not take into account that the distribution of 
the constituent classes is not uniform. To nor- 
malize for this inequity, we compare the parsing 
scores to a baseline that characterizes the rela- 
tionship between the performance of the trained 
grammar and the number of bracketed con- 
stituents in the training data. To generate the 
baseline, we create training data in which 0%, 
25%, 50%, 75%, and 100% of the constituent 
brackets are randomly chosen to be included. 
One class of linguistic labels is better than an- 
other if its resulting parsing improvement over 
the baseline is higher than that of the other. 
The test results of the grammars induced 
from these different training data are summa- 
rized in Figure 1. Graph (a) plots the outcome 
of using the direct induction strategy, and graph 
(b) plots the outcome of the adaptive strat- 
egy. In each graph, the baseline of random con- 
stituent brackets is shown as a solid line. Scores 
of grammars trained from constituent type spe- 
cific data sets are plotted as labeled dots. The 
dotted horizontal line in graph (b) indicates the 
ATIS parsing score of the grammar trained on 
WSJ alone. 
Comparing the five constituent types, we see 
that the HighP class is the most informative 
76 
95 
ss 
8 
~ 6s 
e 
~ 55 
5O 
Rand-75% Rand-1 
Rand-2S JNP NotBaseP 
Hi~iIP 
b I i , i I I 
20O 40O 6OO 80O 1000 1200 1400 1600 Number of brackets in the ATIS ~ain~lg data 
(a) 
95 
< 
! 
7s 
• "! 60 65 
'~ ss 
SO 
...... RIP-1 X' hP Rand-25% = _ * Rand-I 
ig o A\]INP - NotBaseP 
......... ~ ........................................................................ WSJ ~W ....... 
i 1 i i i i i 
200 4OO 6OO BOO 1000 1200 1400 1600 Number of brackets in the ATIS training data 
(b) 
Figure 1: Parsing accuracies of (a) directly induced grammars and (b) adapted grammars as a 
function of the number of brackets present in the training corpus. There are 1595 brackets in the 
training corpus all together. 
for the adaptive strategy, resulting in a gram- 
mar that scored better than the baseline. The 
grammars trained on the AllNP annotation per- 
formed as well as the baseline for both strate- 
gies. Grammars trained under all the other 
training conditions scored below the baseline. 
Our results suggest that while an ideal train- 
ing condition would include annotations of both 
higher-level phrases and simple phrases, com- 
plex clauses are more informative. This inter- 
pretation explains the large gap between the 
parsing scores of the directly induced grammar 
and the adapted grammar trained on the same 
HighP data. The directly induced grammar 
performed poorly because it has never seen a 
labeled example of simple phrases. In contrast, 
the adapted grammar was already exposed to 
labeled WSJ simple phrases, so that it success- 
fully adapted to the new corpus from annotated 
examples of higher-level phrases. On the other 
hand, training the adapted grammar on anno- 
tated ATIS simple phrases is not successful even 
though it has seen examples of WSJ higher- 
level phrases. This also explains why gram- 
mars trained on the conglomerate class Not- 
BaseP performed on the same level as those 
trained on the AllNP class. Although the Not- 
BaseP set contains the most brackets, most of 
the brackets are irrelevant to the training pro- 
cess, as they are neither higher-level phrases nor 
simple phrases. 
Our experiment also indicates that induction 
strategies exhibit different learning characteris- 
tics under partially supervised training condi- 
tions. A side by side comparison of Figure 1 
(a) and (b) shows that the adapted grammars 
perform significantly better than the directly 
induced grammars as the level of supervision 
decreases. This supports our hypothesis that 
pretraining on a different corpus can place the 
grammar in a good initial search space for learn- 
ing the new domain. Unfortunately, a good ini- 
tial state does not obviate the need for super- 
vised training. We see from Figure l(b) that 
retraining with unlabeled ATIS sentences actu- 
ally lowers the grammar's parsing accuracy. 
5.2 Experiment 2: Learning WSJ 
In the previous section, we have seen that anno- 
tations of complex clauses are the most helpful 
for inducing ATIS-style grammars. One of the 
goals of this experiment is to verify whether the 
result also holds for the WSJ corpus, which is 
structurally very different from ATIS. The WSJ 
corpus uses 47 POS tags, and its sentences are 
longer and have more embedded clauses. 
As in the previous experiment, we construct 
training sets with annotations of different con- 
stituent types and of different numbers of ran- 
domly chosen labels. Each training set consists 
of 3600 sentences, and 1780 sentences are used 
as held-out data. The trained grammars are 
tested on a set of 2245 sentences. 
Figure 2 (a) and (b) summarize the outcomes 
77 
80 
"i 7s 
70 5 
i " 
55 
'5 50~ I 
";~ 40 
35 
' ' ' Ran~l- 
Rand-25% 
/ e...NP~,.p 
No~,P 
65 ! 
eo~ It 
"6 
i 50 
'Rand-TS~ F~nd-50"/,~____----- 
Rand-25%~ Not~eP 
~ Ba~N~ImP 
'~-,oo~. 
~a-~ ............................................................................................................ 
i i i i i i i i i 35 ° i I i i i i a i i 
~0 1oooo 15ooo 200~0 25ooo 30~0 350c0 4oo0o 45ooo 5ooo I ocxJo 15ooo 2oooo 25ooo 300~0 3s0~0 40ooo 45c~0 Numb4r of brackets in me WSJ uaining data number of brackets in the WSJ training data 
(a) (b) 
Figure 2: Parsing accuracies of (a) directly induced grammars and (b) adapted grammars as a 
function of the number of brackets present in the training corpus. There is a total of 46463 brackets 
in the training corpus. 
of this experiment. Many results of this section 
are similar to the ATIS experiment. Higher- 
level phrases still provide the most information; 
the grammars trained on the HighP labels are 
the only ones that scored as well as the baseline. 
Labels of simple phrases still seem the least in- 
formative; scores of grammars trained on BaseP 
and BaseNP remained far below the baseline. 
Different from the previous experiment, how- 
ever, the AI1NP training sets do not seem to 
provide as much information for this learning 
task. This may be due to the increase in the 
sentence complexity of the WSJ, which further 
de-emphasized the role of the simple phrases. 
Thus, grammars trained on AllNP labels have 
comparable parsing scores to those trained on 
HighP labels. Also, we do not see as big a gap 
between the scores of the two induction strate- 
gies in the HighP case because the adapted 
grammar's advantage of having seen annotated 
ATIS base nouns is reduced. Nonetheless, the 
adapted grammars still perform 2% better than 
the directly induced grammars, and this im- 
provement is statistically significant. 2 
Furthermore, grammars trained on NotBaseP 
do not fall as far below the baseline and have 
higher parsing scores than those trained on 
HighP and AllNP. This suggests that for more 
complex domains, other linguistic constituents 
2A pair-wise t-test comparing the parsing scores of 
the ten test sets for the two strategies shows 99% confi- 
dence in the difference. 
such as verb phrases 3 become more informative. 
A second goal of this experiment is to test the 
adaptive strategy under more stringent condi- 
tions. In the previous experiment, a WSJ-style 
grammar was retrained for the simpler ATIS 
corpus. Now, we reverse the roles of the cor- 
pora to see whether the adaptive strategy still 
offers any advantage over direct induction. 
In the adaptive method's pretraining stage, 
a grammar is induced from 400 fully labeled 
ATIS sentences. Testing this ATIS-style gram- 
mar on the WSJ test set without further train- 
ing renders a parsing accuracy of 40%. The 
low score suggests that fully labeled ATIS data 
does not teach the grammar as much about 
the structure of WSJ. Nonetheless, the adap- 
tive strategy proves to be beneficial for learning 
WSJ from sparsely labeled training sets. The 
adapted grammars out-perform the directly in- 
duced grammars when more than 50% of the 
brackets are missing from the training data. 
The most significant difference is when the 
training data contains no label information at 
all. The adapted grammar parses with 60.1% 
accuracy whereas the directly induced grammar 
parses with 49.8% accuracy. 
SV~e have not experimented with training sets con- 
taining only verb phrases labels (i.e., setting a pair of 
bracket around the head verb and its modifiers). They 
are a subset of the NotBaseP class. 
78 
6 Conclusion and Future Work 
In this study, we have shown that the structure 
of a grammar can be reliably learned without 
having fully specified constituent information 
in the training sentences and that the most in- 
formative constituents of a sentence are higher- 
level phrases, which make up only a small per- 
centage of the total number of constituents. 
Moreover, we observe that grammar adaptation 
works particularly well with this type of sparse 
but informative training data. An adapted 
grammar consistently outperforms a directly in- 
duced grammar even when adapting from a sim- 
pler corpus to a more complex one. 
These results point us to three future di- 
rections. First, that the labels for some con- 
stituents are more informative than others im- 
plies that sentences containing more of these in- 
formative constituents make better training ex- 
amples. It may be beneficial to estimate the 
informational content of potential training (un- 
marked) sentences. The training set should only 
include sentences that are predicted to have 
high information values. Filtering out unhelpful 
sentences from the training set reduces unnec- 
essary work for the human annotators. Second, 
although our experiments show that a sparsely 
labeled training set is more of an obstacle for the 
direct induction approach than for the grammar 
adaptation approach, the direct induction strat- 
egy might also benefit from a two stage learning 
process similar to that used for grammar adap- 
tation. Instead of training on a different corpus 
in each stage, the grammar can be trained on 
a small but fully labeled portion of the corpus 
in its first stage and the sparsely labeled por- 
tion in the second stage. Finally, higher-level 
constituents have proved to be the most infor- 
mative linguistic units. To relieve humans from 
labeling any training data, we should consider 
using partial parsers that can automatically de- 
tect complex nouns and sentential clauses. 

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