Learning Verb Subcategorization from Corpora: Counting Frame Subsets

Daniel Zeman
Ústav formální a aplikované lingvistiky
Univerzita Karlova
Malostranské náměstí 25, 11800 Praha 1, Czechia
zeman@ufal.ms.mff.cuni.cz

Anoop Sarkar
Department of Computer and Information Science
University of Pennsylvania
200 South 33rd Street, Philadelphia, PA 19104, USA
anoop@linc.cis.upenn.edu

Abstract
We present some novel machine learning techniques for the identification of subcategorization information for verbs in Czech. We compare three different statistical techniques applied to this problem. We show how the learning algorithm can be used to discover previously unknown subcategorization frames from the Czech Prague Dependency Treebank. The algorithm can then be used to label dependents of a verb in the Czech treebank as either arguments or adjuncts. Using our techniques, we are able to achieve 88 % accuracy on unseen parsed text.
1.	
1.	Introduction
The subcategorization of verbs is an essential issue in parsing, helping us to attach the right arguments to the verb. Subcategorization is also important for the recovery of the correct predicate-argument relations by a parser Carroll and Minnen (1998) and Carroll and Rooth (1998) give several reasons why subcategorization information is important for a natural language parser. Machine-readable dictionaries are not comprehensive enough to provide this lexical information (Manning 1993, Briscoe 1997). Furthermore, such dictionaries are available only for very few languages. We need some general method for the automatic extraction of subcategorization information from text corpora.
Several techniques and results have been reported on learning subcategorization frames (SFs) from text corpora (Webster 1989, Brent 1991, Brent 1993, Brent 1994, Ushioda 1993, Manning 1993, Ersan 1996, Briscoe 1997, Carroll 1998). All of this work deals with English. In this paper we report on techniques that automatically extract SFs for Czech, which is a free word-order language, where verb complements have visible case marking.
Apart from the target language, this work also differs from previous work in other ways. Unlike all other previous work in this area, we do not assume that the set of SFs is known to us in advance. Also in contrast, we work with syntactically annotated data (the Prague Dependency Treebank, PDT (Hajič 1998) where the subcategorization information is not given; although this is less noisy compared to using raw text, we have discovered interesting problems that a user of a raw or tagged corpus is unlikely to face.
We first give a detailed description of the task of uncovering SFs and also point out those properties of Czech that have to be taken into account when searching for SFs. Then we discuss some differences from the other research efforts. We then present the three techniques that we use to learn SFs from the input data.
In the input data, many observed dependents of the verb are adjuncts. To treat this problem effectively, we describe a novel addition to the hypothesis testing technique that uses intersections of observed frames to permit the learning algorithm to better distinguish arguments from adjuncts.
Using our techniques, we are able to achieve 88 % accuracy in distinguishing arguments from adjuncts on unseen parsed text.
2.	Task Description
In this section we describe precisely the proposed task. We also describe the input training material and the output produced by our algorithms.
2.1.	Identifying subcategorization frames
In general, the problem of identifying subcategorization frames is to distinguish between arguments and adjuncts among the constituents modifying a verb. e.g., in “John saw Mary yesterday at the station”, only “John” and “Mary” are required arguments while the other constituents are optional (adjuncts). There is some controversy as to the correct subcategorization of a given verb and linguists often disagree as to what is the right set of SFs for a given verb. A machine learning approach such as the one followed in this paper sidesteps this issue altogether, since the algorithm is left to learn what is an appropriate SF for a verb.
Figure 1 shows a sample input sentence from the PDT annotated with dependencies which is used as training material for the techniques described in this paper. Each node in the tree contains a word, its part-of-speech tag (which includes morphological information) and its location in the sentence. We also use the functional tags, which are part of the PDT annotation. To make future discussion easier we define some terms here. Each daughter of a verb in the tree shown is called a dependent and the set of all dependents for that verb in that tree is called an observed frame (OF). A subcategorization frame (SF) is a subset of the OF. For example the OF for the verb mají (have) in Figure 1 is {N1, N4} and its SF is the same as its OF. After training on such examples, the algorithm takes as input parsed text and labels each daughter of each verb as either an argument or an adjunct. It does this by selecting the most likely SF for that verb given its OF.

Figure 1	
[# ZSB 0]

[však JE 8]
but

[. ZIP 11]

[mají VPP3A 2]
have

[, ZIP 6]

[chybí VPP3A 9]
miss

[studenti N1 1]
students

[zájem N4 5]
interest

[fakultě N3 7]
faculty (dative)

[angličtináři N1 10]
teachers of English

[jazyky N4 4]
languages

[o R4 3]
in

Example input to the algorithm from the Prague Dependency Treebank.
Czech: Studenti mají o jazyky zájem, fakultě však chybí angličtináři.
English: The students are interested in languages but the faculty is missing teachers of English.

2.2.	Relevant properties of the Czech Data
Czech is a “free word-order”' language. This means that the arguments of a verb do not have fixed positions and are not guaranteed to be in a particular configuration with respect to the verb.
The examples in (1) show that while Czech has a relatively free word-order some orders are still marked. The SVO, OVS, and SOV orders in (1)a, (1)b, (1)c respectively, differ in emphasis but have the same predicate-argument structure. The examples (1)d, (1)e can only be interpreted as a question. Such word orders require proper intonation in speech, or a question mark in text.
The example (1)f demonstrates how morphology is important in identifying the arguments of the verb. cf. (1)f with (1)b. The ending –a of Martin is the only difference between the two sentences. It however changes the morphological case of Martin and turns it from subject into object.  Czech has 7 cases that can be distinguished morphologically.
(1)	
a.	Martin otvírá soubor. (Martin opens the file)
b.	Soubor otvírá Martin. ( the file opens Martin)
c.	Martin soubor otvírá.
d.	#Otvírá Martin soubor.
e.	#Otvírá soubor Martin.
f.	Soubor otvírá Martina. (= the file opens Martin)
Almost all the existing techniques for extracting SFs exploit the relatively fixed word-order of English to collect features for their learning algorithms using fixed patterns or rules (see Table 2 for more details). Such a technique is not easily transported into a new language like Czech. Fully parsed training data can help here by supplying all dependents of a verb. The observed frames obtained this way have to be normalized with respect to the word order, e.g. by using an alphabetic ordering.
For extracting SFs, prepositions in Czech have to be handled carefully. In some SFs, a particular preposition is required by the verb, while in other cases it is a class of prepositions such as locative prepositions (e.g. in, on, behind, …) that are required by the verb. In contrast, adjuncts can use a wider variety of prepositions. Prepositions specify the case of their noun phrase complements but sometimes there is a choice of two or three cases with different meanings of the whole prepositional phrase (e.g. na mostě = on the bridge; na most = onto the bridge). In general, verbs select not only for particular prepositions but also indicate the case marking for their noun phrase complements.
2.3.	Argument types
We use the following set of labels as possible arguments for a verb in our corpus.  They are derived from morphological tags and simplified from the original PDT definition (Hajič and Hladká 1998, Hajič 1998); the numeric attributes are the case marks.  For prepositions and clause complementizers, we also save the lemma in parentheses.
•	Noun phrases: N4, N3, N2, N7, N1.
•	Prepositional phrases: R2(bez), R3(k), R4(na), R6(na), R7(s), …
•	Reflexive pronouns se, si: PR4, PR3.
•	Clauses: S, JS(že), JS(zda)
•	Infinitives: VINF.
•	Passive participles: VPAS.
•	Adverbs: DB.
We do not specify SF types since we aim to discover these.
3.	Three methods for identifying subcategorization frames
We describe three methods that take as input a list of verbs and associated observed frames from the training data (see Section 2.1), and learn an association between verbs and possible SFs. We describe three methods that arrive at a numerical score for this association.
However, before we can apply any statistical methods to the training data, there is one aspect of using a treebank as input that has to be dealt with. A correct frame (verb + its arguments) is almost always accompanied by one or more adjuncts in a real sentence. Thus the observed frame will almost always contain noise. The approach offered by Brent and others counts all observed frames and then decides which of them do not associate strongly with a given verb. In our situation this approach will fail for most of the observed frames because we rarely see the correct frames isolated in the training data. e.g., from occurrences of the transitive verb absolvovat (“go through something”) that occurred ten times in the corpus, no occurrence presented the verb-object pair alone. In other words, the correct SF constituted 0\% of the observed situations. Nevertheless, for each observed frame, one of its subsets was the correct frame we sought for. Therefore, we considered all possible subsets of all observed frames. We used a technique which steps through the subsets of each observed frame from larger to smaller ones and records their frequency in data.  Large infrequent subsets are suspected to contain adjuncts, so we replace them by more frequent smaller subsets. Small infrequent subsets may have elided some arguments and are rejected. The details of this process can be grasped by looking at the example shown in Figure 2.

Figure 2	
N4 od do (2)

N4 v na (1)

N4 v (1+1)

N4 od (2)

v na (0)

N4 na (0)

od do (0)

N4 do (0)

N4 po (1)

N4 (2+2+1)

od (0)

do (0)

v (0)

na (0)

po (0)

empty (0)

Computing the subsets of observed frames for the verb absolvovat. The counts for each frame are given within parentheses (). In this example, the frames N4 R2(od) R2(do), N4 R6(v) R6(na), N4 R6(v) and N4 R6(po) have been observed with the verb in the corpus; the other frames are only their subsets. Note that the counts in this figure do not correspond to the real counts for the verb absolvovat in the training corpus.

The methods we present here have a common structure. For each verb, we need to associate a score for the hypothesis that a particular set of dependents of the verb are arguments of that verb. In other words, we need to assign a value to the hypothesis that the observed frame under consideration is the verb's SF. Intuitively, we either want to test for independence of the observed frame and verb distributions in the data, or we want to test how likely is a frame to be observed with a particular verb without being a valid SF. Note that the verbs are not labeled with correct SFs in the training data. We develop these intuitions with the following well-known statistical methods. For further background on these methods the reader is referred to Bickel and Doksum (1977) and Dunning (1993).
3.1.	Likelihood ratio test
Let us take the hypothesis that the distribution of an observed frame f in the training data is independent of the distribution of a verb v. We can phrase this hypothesis as , that is distribution of a frame f given that a verb v is present is the same as the distribution of f given that v is not present (written as v). We use the log likelihood test statistic (Bickel and Doksum 1977, p. 209) as a measure to discover particular frames and verbs that are highly associated in the training data.

where c(.) are counts in the training data. Using the values computed above:

Taking these probabilities to be binomially distributed, the log likelihood statistic (Dunning 1993) is given by:

where,

According to this statistic, the greater the value of –2 log  for a particular pair of observed frame and verb, the more likely that frame is to be valid SF of the verb.
3.2.	T-scores
Another statistic that has been used to discover associated items in data is the t-score. Using the definitions from 3.1 we can compute t-scores using the equation below and use its value to measure the association between a verb and a frame observed with it.

where,

3.3.	Hypothesis testing
Once again assuming that the data is binomially distributed, we can look for frames that co-occur with a verb more often than chance. This is the method used by several earlier papers on SF extraction starting with (Brent 1991, 1993, 1994).
Let us consider probability pf which is the probability that a given verb is observed with a frame but this frame is not a valid SF for this verb. pf is the error probability on identifying a SF for a verb. Let us consider a verb v which does not have as one of its valid SFs the frame f. How likely is it that v will be seen m or more times in the training data with frame f. If v has been seen a total of n times in the data, then  gives us this likelihood.

If  is less than or equal to some small threshold value then it is extremely unlikely that the hypothesis is true, and hence the frame f must be a SF of the verb v. Setting the threshold value to 0.05 gives us a 95 % or better confidence value that the verb v has been observed often enough with a frame f for it to be a valid SF.
Initially, we consider only the observed frames (OFs) from the treebank. There is a chance that some are subsets of some others but now we count only the cases when the OFs were seen themselves. Let's assume the test statistic rejected the frame. Then it is not a real SF but there probably is a subset of it that is a real SF. So we select one of the subsets whose length is one member less: this is the successor of the rejected frame and inherits its frequency. Of course one frame may be successor of several longer frames and it can have its own count as OF. This is how frequencies accumulate and frames become more likely to survive.
An important point is the selection of the successor. We have to select only one of the n possible successors of a frame of length n, otherwise we would break the total frequency of the verb. Suppose there is m rejected frames of length n. This yields m × n possible modifications of the lower level. A self-offering approach would be to choose the one that results in the strongest preference for some frame (lowest entropy of the lower level). However, we eventually discovered (due to a bug in the program) that a random selection resulted in better accuracy (88 % instead of 86 %). The reason remains unknown to us.
The technique described here may sometimes find a subset of a correct SF, discarding one or more of its members. Such frame can still help parsers because they can at least look for the dependents that have survived.
4.	Evaluation
For the evaluation of the methods described above we used the Prague Dependency Treebank (PDT). We used 19,126 sentences of training data from the PDT (about 300K words). In this training set, there were 33,641 verb tokens with 2,993 verb types. There were a total of 28,765 observed frames (see Section 2.1 for explanation of these terms). There were 914 verb types seen 5 or more times.
Since there is no electronic valence dictionary for Czech, we evaluated our filtering technique on a set of 500 test sentences where arguments and adjuncts were distinguished manually. We then compared the accuracy of our output set of items marked as either arguments or adjuncts against this gold standard.
First we describe the baseline methods. Baseline method 1: consider each dependent of a verb an adjunct.  Baseline method 2: use just the longest known observed frame matching the test pattern. If no matching OF is known, use a heuristic to find a partially matching (similar) OF. No statistical filtering is applied.
A comparison between the baseline methods and all three methods that were proposed in this paper is shown in Table 1.
The experiments showed that the method improved accuracy of this distinction from 55 % to 88 %. We were able to classify as many as 914 verbs which is a number outperformed only by Manning, with 10× more data.
Also, our method discovered 137 subcategorization frames from the data. The known upper bound of frames that the algorithm could have found (the total number of the observed frame types) was 450.

	Baseline 1	Baseline 2	Likelihood ratio	T-scores	Hypothesis testing	
Total verb nodes	1027.0	1027.0	1027.0	1027.0	1027.0	
Total complements	2144.0	2144.0	2144.0	2144.0	2144.0	
Nodes with known verbs	1027.0	981.0	981.0	981.0	907.0	
Complements of known verbs	2144.0	2010.0	2010.0	2010.0	1812.0	
Recall	100 %	94 %	94 %	94 %	84 %	
Correct suggestions	1187.5	1573.5	1642.5	1652.9	1596.5	
Precision	55 %	78 %	82 %	82 %	88 %	
True arguments	956.5	910.5	910.5	910.5	834.5	
True adjuncts	1187.5	1099.5	1099.5	1099.5	977.5	
Suggested arguments	0.0	1122.0	974.0	1026.0	674.0	
Suggested adjuncts	2144.0	888.0	1036.0	984.0	1138.0	
Wrong argument suggestions	0.0	324.0	215.5	236.3	27.5	
Wrong adjunct suggestions	956.5	112.5	152.0	120.8	188.0	
Table 1	Comparison between the three methods and the baseline methods. Some counts are not integers because, in the test data, the argument- / adjunctivness was considered a fuzzy value rather than a binary (0 or 1) one. Our recall is the number of known verb complements divided by the total number of complements. Our precision is the number of correct suggestions divided by the number of known verb complements (the number of “questions”).

5.	Comparison with related work
Preliminary work on SF extraction from corpora was done by (Brent 1991, 1993, 1994), (Webster and Marcus 1989), and (Ushioda et al. 1993). (Brent 1993) uses standard hypotheses testing method for filtering frames observed with a verb. Brent applied his method to very few verbs however. (Manning 1993) applies Brent's method to parsed data and obtains a subcategorization dictionary for a larger set of verbs. (Briscoe and Carroll 1997) and (Carroll 1998) differ from earlier work in that a substantially larger set of SF types are considered; (Carroll and Rooth 1998) use an iterative EM algorithm to learn subcategorization as a result of parsing, and, in turn, to improve parsing accuracy by applying the verb SFs obtained. A complete comparison of all the previous approaches with the current work is given in Table 2. While these approaches differ in size and quality of training data, number of SF types (e.g. intransitive verbs, transitive verbs) and number of verbs processed, there are properties that all have in common. They all assume that they know the set of possible SF types in advance. Their task can be viewed as assigning one or more of the (known) SF types to a given verb. In addition, except for (Briscoe and Carroll 1997) and (Carroll and Minnen 1998), only a small number of SF types is considered.
Using a dependency treebank as input to our learning algorithm has both advantages and drawbacks. There are two main advantages of using a treebank:
•	Access to more accurate data. Data is less noisy when compared with tagged or parsed input data. We can expect correct identification of verbs and their dependents.
•	We can explore techniques (as we have done in this paper) that try and learn the set of SFs from the data itself, unlike other approaches where the set of SFs have to be set in advance.
Also, by using a treebank we can use verbs in different contexts which are problematic for previous approaches, e.g. we can use verbs that appear in relative clauses. However, there are two main drawbacks:
•	Treebanks are expensive to build and so the techniques presented here have to work with less data.
•	All the dependents of each verb are visible to the learning algorithm. This is contrasted with previous techniques that rely on finite-state extraction rules, which ignore many dependents of the verb. Thus our technique has to deal with a different kind of noisy data as compared to previous approaches.
We tackle the second problem by using the method of observed frame subsets described in Section 3.3.

Previous work	Data	# SFs	# Verbs tested	Method	Miscue rate ()	Corpus	
(UEGW93)	POS + FS rules	6	33	Heuristics	NA	WSJ
(300K)	
(Bre93)	Raw + FS rules	6	193	Hypothesis testing	Iterative estimation	Brown (1.1M)	
(Man93)	POS + FS rules	19	3104	Hypothesis testing	Hand	NYT
(4.1M)	
(Bre94)	Raw + heuristics	12	126	Hypothesis testing	Non-iterative estimation	CHILDES (32K)	
(EC96)	Fully parsed	16	30	Hypothesis testing	Hand	WSJ
(36M)	
(BC97)	Fully parsed	160	14	Hypothesis testing	Dictionary estimation	Various (70K)	
(CR98)	Unlabeled	9+	3	Inside-outside	NA	BNC (5‑30M)	
Current Work	Fully parsed	Learned 137	914	Subsets + hypothesis testing	Hand	PDT
(300K)	
Table 2	Comparison with previous work on automatic SF extraction from corpora.

6.	Conclusion
We are currently incorporating the SF information produced by the methods described in this paper into a parser for Czech. We hope to duplicate the increase in performance shown by treebank-based parsers for English when they use SF information. Our methods can also be applied to improve the annotations in the original treebank that we use as training data. The automatic addition of subcategorization to the treebank can be exploited to add predicate-argument information to the treebank.
Also, techniques for extracting SF information from data can be used along with other research, which aims to discover relationships between different SFs of a verb (Stevenson and Merlo 1999, Lapata and Brew 1999, Lapata 1999, Stevenson et al. 1999).
The statistical models in this paper were based on the assumption that given a verb, different SFs occur independently. This assumption is used to justify the use of the binomial. Future work perhaps should look towards removing this assumption by modeling the dependence between different SFs for the same verb using a multinomial distribution.
To summarize: we have presented techniques that can be used to learn subcategorization information for verbs. We exploit a dependency treebank to learn this information, and moreover we discover the final set of valid subcategorization frames from the training data. We achieve 88 % accuracy on unseen data.
We have also tried our methods on data that was automatically morphologically tagged, which allowed us to use more data (82K sentences instead of 19K). The performance went up to 89 % (a 1 % improvement).
7.	Acknowledgements
This project was done during first author’s visit to the University of Pennsylvania. We would like to thank dr. Aravind Joshi for the invitation and for arranging the visit.
Many tools used throughout the project are the results of the project No. VS96151 of the Ministry of Education of the Czech Republic. The data (PDT) would not be available unless the grant No. 405/96/K214, of the Grant Agency of the Czech Republic, enabled work on the treebank design. (Both granted to the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague.)
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