---
title: "Augmented Transition Networks as Psychological Models of Sentence Comprehension"
authors:
  - "Ronald M. Kaplan"
doi: ""
url: "https://ijcai.org/proceedings/1971/45"
pdf: "https://ijcai.org/proceedings/1971/0045.pdf"
venue: "Proceedings of the Second International Joint Conference on Artificial Intelligence"
year: 1971
pages: "429-442"
track: ""
keywords:
---

# Augmented Transition Networks as Psychological Models of Sentence Comprehension

Ronald M. Kaplan

*Proceedings of the Second International Joint Conference on Artificial Intelligence*, pages 429-442.

## Abstract

This paper describes the operation of an augmented recursive transition network parser and demonstrates the natural way in which perceptual strategies, based on the results of psycholinguistic experimentation, can be represented in the transition network grammatical notation. Several illustrative networks are given, and it is argued that such grammars are empirically justified and conceptually productive models of the psychological processes of sentence comprehension.

## Full text

## AUGMENTED TRANSITION N~TWORKS AS PSYCHOLOGICAL MODELS OF SENTENCE COMPREHENSION

Ronald M. Kaplan Language Research Foundation and Harvard University Cambridge, Massachusetts U.S.A.

Abstract: This paper describes the operation of an augmented recursive transition network parser and demon­ strates the natural way in which percep­ tual strategies, based on the results of psycholinguistic experimentation, can be represented in the transition network grammatical notation. Several illus­ trative networks are given, and it is argued that such grammars are empirically justified and conceptually productive models of the psychological processes of sentence comprehension.

## I. Introductbn

During the past year a major research effort has been conducted to explore and refine the properties of an augmented recursive transition network parser (26, 27) and to develop a large­ scale English grammar for the system.l Although our primary goal has been to construct a powerful and practical natural language processor for artificial intelligence and information retrieval applications,2 we have also investigated the correspondence between the sentence processing characteristics of the parser and those of human speakers, as revealed by psychological experimentation, obser­ vation, and intuitio~. We have found that the grammatical formalism of the transition network is a convenient and natural notational system for fabricating psychological models of syntactic analysis. In the present paper we des­ cribe some of the psychologically appealing properties of the parser and illustrate how psycholinguistic experi­ mental results can be mapped into simple transition network models. We suggest that building and testing such models can lead to a better understanding of linguistic performance.

It should be clear from the outset that we are not proposing a transition network model as a complete and sufficient representation of all aspects of language behavior. Rather, transi­ tion network models aim only at simula­ ting the syntactic analysis component of performance: given an input string

written in standard orthography, they attempt to discover the syntactic rela­ tionships holding between constituents. We ignore the myriad problems of phonetic decoding and segmentation and semantic and cognitive interpretation, as well as all the psycholinguistic and motivational complexities of speech production. It is in this limited sense that we refer to transition network grammars as sentence comprehension or perceptual models. Of couree, we expect that more complete for­ malizations of language behavior will incorporate such independently developed syntactic analysis models.

In section II of this paper we sketch the linguistic and psycholinguistic back­ ground of our research. Section III describes the organization and operation of the transition network parser and depicts the grammatical notation, and section IV shows the representation in this notation of perceptual strategies induced from psycholinguistic data. In section V we discuss the fruitfulness of this modeling approach, indicating some conceptual issues that are clarified and some empirical predictions that arise

from transition network formulations.

## II. Transformational Grammar and Psycholinguistics

The process by which a native speaker comprehends and produces meaningful sen­ tences in his language is extremely com­ plex and, with our present body of psy­ cholinguistic theory and data, is under­ stood only slightly. This shortcoming of psycholinguistics exists despite the fact that advances in linguistic theory over the last decade have provided a number of crucial insights into the formal struc­ ture of language and linguistic perfor­ mance. To place augmented recursive transition network grammars in the con­ text of previous research, we briefly survey some relevant results of linguis­

tics and psycholinguistics.

A transformational grammar for a given language L formally defines the notion sentence of L by describing a mechanical procedure for enumerating all and only the well-formed sentences of L. With each sentence it also associates a structural description which provides a formal account of the native speaker's competence, the linguistic knowledge which underlies his ability to make judgments about the basic grammatical relations (e.g., subject, predicate, object) and about such sentential properties as relative grammaticality, ambiguity, and synonymy. At present

there is no clear agreement among linguists about the detailed features re­ quired for an adequate grammar, but cer­ tain principles of grammar organization are almost universally accepted: the structural description furnished for a sentence by the grammar must consist of (at least) two levels or syntactic repre­ sentation (P-markers) -- a deep structure and a surface structure -- together with a specification or an ordered sequence or transformations which maps the deep structure or a sentence into appropriate surface structures.

Transformational theorists maintain that their formal model is not intended to give an accurate account of the psycho­ logical processes involved when a human being uses language, either speaking or comprehending. Any correlations observed between actual behavior characteristics and transformational grammars are acci­ dental, signifying merely the fact that psychological and linguistic data are both obtained from the same class or na­ tive speakers (but Chomsky (6) weakens this assertion somewhat when he argues that acquisition data might have a bear­ ing on the evaluation metric selected for grammars). Linguists have been very care­ ful to distinguish the speaker's compe­ tence, which transformational grammars attempt to model, from his performance, the manner in which he utilizes his know­ ledge in processing sentences (6, 10, 19). Thus a transformational grammar might be allowed to generate sentences which are virtually impossible for a speaker to deal with. Most current grammars will generate [5a], assi~ning it the same sub­ ject-verb-object relations as are appar­ ent in [ 5b):

- [5) a. The man the girl the cat the dog bit scratched loved ate ice cream.
- b. The dog bit the cat that scratched the girl who loved the man who ate ice cream.

Very few native speakers would intuit that [5a] is grammatical, yet to prevent its generation, either the grammar must be greatly complicated or other sentences which native speakers do accept must be marked ungrammatical. Linguists resolve this dilemma and preserve the simplicity and generality of their grammars by claim­ ing that native English speakers do have the basic knowledge to process [5a), which is therefore grammatical; speakers have trouble with it because their per­ ceptual mechanisms do not provide the memory space and/or computational rou­ tines required to process it. A trans­ formational grammar is a formal specification of the speaker's competence and has nothin~ to say about psychological func­ tioning.

Despite these disclaimers, psycho­ linguists have been intrigued by trans­ formational theory because it provides the most intricate and compelling expli­ cation to date of a large number of basic linguistic intuitions. Many experiments have been conducted to test the hypothesis that transformational operations will have direct, observable reflexes in psycholo­ gical processing; Fodor and Garrett (8) and Bever (1) present useful reviews or this literature. A major concern of these studies has been to determine whether the perceptual complexity of sentences (the difficulty of comprehending and respon­ ding to them) is directly correlated with derivational complexity (e.g., the number of transformations required to generate them). Fodor and Garrett (8) examine this "derivational theory of complexity" in detail and conclude that the available psycholinguistic data do not offer much support for it and that the connection, if there is one, between transformational grammar and perception is not very direct at all.

Although psycholinguists have vir­ tually abandoned their attempts to find perceptual reflexes of specific grammati­ cal features, several studies have been successful in corroborating the psycho­ logical reality of the deep structure­ surface structure distinction. MacKay and Bever (15) found that subjects respond differently to deep structure and surface structure ambiguities; Wanner (25) showed that the number of deep structure S-nodes underlying a sentence has a direct influ­ ence on the ease of prompted recall from long-term memory; and Bever (1) has rein­ terpreted the results or the click exper­ iments (7) as demonstrating that deep­ structure S-nodes affect the surface seg­ mentation of a stimulus sentence. These experiments suggest that an adequate model of sentence comprehension must incorporate some mechanism for recovering a deep­ structure-like representation of a given stimulus word string. This representation should explicitly denote at least such basic grammatical relationships as actor, verb, and object. More extensive empiri­ cal work should indicate whether deep structure must be even more abstract than this.

There are several other requirements for adequacy that we may impose on poten­ tial models of sentence comprehension, based on some common observations about

## our sentence processing abilities:

- (a) A perceptual model must process strings in essentially temporal or linear order, for this is the order in which sentences are encountered in conversation and reading.
- (b) It must process strings and provide appropriate analyses in an amount of time proportional to that required by human speakers. For example, since perceptual difficulty does not rapidly increase as the length of the sentence increases, the amount of time required by the model should be at most a slowly increasing function of sentence length.
- (c) The model should discover anomalies and ambiguities where real speakers discover them, and for ambiguous sentences the model should return analyses in the same order as speakers do.

Whereas there are many well-known recognition procedures for programming languages and other relatively simple artificial languages, only a few algo­ rithms have been proposed which aim at "transformational" recognition, that is, which attempt to develop appropriate deep structures from natural language surface strings. Some of these algorithms (17, 20, 28) incorporate more or less directly a linguistically motivated transforma­ tional grammar; in light of the empirical shortcomings of the derivational theory of complexity, it is not surprising that these proposals are inadequate perceptual models. A deep structure recovery strategy suggested by Kuno (14) operates independently of a transformational grammar and offers more psychological relevance, but it too has formal limita­ tions (13). A procedure and grammatical notation recently described by Kaplan (11), based on an algorithm by Kay (12), appear to meet many of the formal and practical requirements for deep structure recovery, but at present not enough is known about its operating characteristics to assess its adequacy as a formalism for perceptual models. Augmented recursive transi"tion network grammars, to which we now turn, can satisfy (a)- (c), have other desirable psychological and formal properties, and have the additional advantage of being practical and effi­ cient.

## III. The Augmented Recursive Transition Network

The idea of a transition network parsing procedure for natural language was originally suggested by Thorne, Bratley, and Dewar (23) and was subse­ quently refined in an implementation by Bobrow and Fraser (3). Woods (26, 27) has also presented a transition network parsing system which is more general than either the Thorne et al. or Bobrow­ Fraser systems. The discussion below is based on the Woods version. Since a detailed description is already available we present here only a brief outline of the grammatical formalism and then focus on the manner in which this formalism can be used to express perceptual and linguistic regularities.

At the heart of the augmented recur­ sive transition network is a familiar finite-state grammar (5) consisting of a finite set of nodes (states) connected by labelled directed arcs. An arc represents an allowable transition from the state at its tail to the state at its head, the label indicating the input symbol which must be found in order for the transition to occur. An input string is accepted by the grammar if there is a path of transitions which correspond to the sequence of symbols in the string and which lead from a speci­ fied initial state to one of a set of specified flnal states. Finite state grammars are attractive from the percep­ tual point of view because they process strings strictly from left to right, but they have well-known inadequacies as models for natural languages (4). For example, they have no machinery for expressing statements about hierarchical structure.

This particular weakness can be eliminated by adding a recursive control mechanism to the basic strategy, as follows: all states are given names which are then allowed as labels on arcs in addition to the normal input-symbol labels. When an arc with a state-name is encountered, the name of the state at the head of the arc is pushed (saved on the top of a push-down store), and analysis of the remainder of the input string continues at the state named on the arc. When a final state is reached in this new part of the grammar, a ~ occurs (control is returned to the state removed from the top of the push-down store). A sentence is said to be accepted when a final state, the end of the string, and an empty push-down store are all reached at the same time. Note that with this elaboration of the basic finite-state mechanism, we have produced a formalism that can easily describe context-free languages as well as regular languages with unbounded coordinate structures. The structural description provided for a sentence by this procedure is simply the history of transitions, pushes, and pops required to get through the string.

However, the finite-state transition network with recursion cannot describe cross-serial dependencies, so it is still inadequate for natural languages (21). The necessary additional power is obtained by permitting a sequence of actions and a condition to be specified on each arc. The actions provide a facility for expli­ citly building and naming tree structure~ The names, called registers, function much like symbolic variables in program­ ming languages: they can be used in later actions, perhaps on subsequent arcs, to refer to their associated structures. A register is said to contain the struc­ ture it names, and the actions determine additions and changes to the contents of registers in terms of the current input symbol, the previous contents of regis­ ters, and the results of lower-level computations (pushes). This means that as constituents of a sentence are iden­ tified, they can be held in registers until they are combined into larger con­ stituents in other registers. In this way deep structural descriptions can be fashioned in registers essentially inde­ pendently of the analysis paths through the transition network.

Conditions furnish more sensitive controls on the admissibility of transi­ tions. A condition is a Boolean combi­ nation of predicates involving the cur­ rent input symbol and register contents. An arc cannot be taken if its conditiou evaluates to false (symbolized by NIL), even though the current input symbol satisfies the arc label. This means first, that more elaborate restrictions can be imposed on the current symbol than those conveyed by the arc label, and second, that information about previous states and structures can be passed along in the network to determine future transitions. This makes it pos­ sible for similar sections of separate analysis paths to be merged for awhile and then separated again -- a powerful technique for eliminating reduadancies and simplifying grammars. The condition predicates and the arc actions can be arbitrary functions in LISP notation, although we have developed a small set of primitive operations, described below and in (26, 27), which seem adequate for most situations. In these primitive actions and predicates, atomic arguments denote registers; parenthetic expressions are forms to be evaluated.

In order to be able to refer to the current input symbol in conditions or actions, a special register, named*, has been provided. More properly, this register always contains the constituent that enabled the transition; usually this is the input symbol, but for actions on a push arc (which are usually executed after the return from the lower level), *-corrtains the structural des­ cription of the phrase identified in the lower computation. This phrase is de­ termined when a special type of arc, a pop arc, is taken from a final state at the lower level (final states are dis­ tinguished by the existence of pop arcs ).

The recursive transition network, with all of these additions, is called an augmented recursive transition net­ work; it is easy to show that it has the generative po~er of a Turing machin~ To demonstrate more concretely how the transition network works, we give a sim­ ple example. Figure 1 shows a transition network grammar that will recover deep structures for simple transitive and in­ transitive sentences, such as [6] and [ 7 J:

- (6] The man kicked the ball.
- [7] The ball fell.

The top of the figure shows the organi­ zation of paths in the network. States are represented by circles with the state name inside. The state-names are purely mnemonic, serving to indicate the constituent being analyzed (to the left of the slash) and how much of that con­ stituent has been identified so far. Each arc specifies what will allow the transition and has a number denoting the condition and actions in the table below. We mentioned above three kinds of arcs: ordinary input symbol arcs, push arcs, and pop arcs. To distinguish these arcs from each other and ~rom other arc types, each arc has an explicit type-indicator. Thus, PUSn NP/ specifies that arc 1 is a push arc and that control is to pass to state NP/. POP (SBUILD) indicates that arc 5 is a pop arc, and the structure to be popped (that is, placed in * at the next higher level) is the value of the function SBuiLD. Figure 1 includes two new types: a CAT arc (arc 2) does not require a specific input symbol, but requires that the word be marked in the dictionary as belonging to the specified lexical category. A JUMP arc (arc 4) is a very special arc that allows a transition in the grammar,

Figure 1: A Simple Transition Network Grammar

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000000_5381fcb5dcfe71d691b4183c0587c1989cd54fb4a22a84d5d65f6bfd0b488fca.png)

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000001_6c47240ee4bc761e856dcc8d1108beb14c5dfcda49bb16d1510bd9329d79f4ba.png)

| Arc Condition                      | Actions                             | Arc Condition         | Actions      |
|------------------------------------|-------------------------------------|-----------------------|--------------|
| 1 T 2 (AND (GETF TNS) (SVAGR SUBJ) | (SETR SUBJ *) (SETR TN~ (GETF TNS)) | 4 (INTRANS V) 5 T 6 T | (SETR DET *) |
| (GETF 3 (TRANS V)                  | PNCODE))) (SETR OBJ *)              | 7 T 8 T               | (SETR N *)   |

Figure 2: Arcs Required for Passives

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000002_9d279badb06c386644698d152070b110bf36905919a60e016f2ed7f437c6c9ba.png)

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000003_c9386401a555e0c924967b7a5bae2ab7f7f6b31058de760a510edb7a31f05cf2.png)

| Arc                                                       | Condition                                                                     | Actions   | Arc Condition Actions                                               |
|-----------------------------------------------------------|-------------------------------------------------------------------------------|-----------|---------------------------------------------------------------------|
| 4 (OR (INTRANS (FULLR 5 (FULLR 9 (AND (GETF (PASSIVE (WRD | V) OBJ)) SUBJ) PASTPART) (SETR OBJ SUBJ) *) (SETR SUBJ NIL) BE V)) (SETR V *) | 10 11 12  | (NULLR SUBJ) T (NULLR SUBJ) (SETR SUBJ (BUILDQ (NP (PRO SOMEONE)))) |

with possible actions, without advancing the input string -- it is useful for by­ passing optional grammar elements.

Let us trace the analysis of sentence [6) using this grammar (Figure 3 shows the trace as it is printed out by the program). The starting state is, by convention, the state labelled S/. The only arc leaving S/ is a push for a noun-phrase, so without advancing the input string, we switch to NP/. Since the, the current input symbol, is in the category D~T and since the condition for arc 6 is trivially true, we can take arc 6, executing the action (SETR DET 1 ). SETR is a primitive action that places the structure specified by its second argument (in this case, the current input word, denoted by 1 ) in the register named by its first argument (DET). Thus after following arc 6, the register D~T contaire the, and we continue processing at state NP/DET, looking at the word~· We are permitted to take arc 7, saving~ in the register N, and arrive at the final state NP/N. We take the POP arc, which defines the phrase to be returned. BUILDQ is a primitive action that takes as its first argument a tree fragment with some nodes denoted by the symbol +. These nodes are replaced by the contents of the registers specified as the remain­ ing arguments, in left-to-right order. Thus the value returned by arc 8 will be the structure (NP (DET the)(N man)), which is a labelled bracketing-corres­ ponding to the tree (8):

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000004_d62adcce1bf2a17249062c0835bbb17ce91c7522ea7a4814970dd0614d0611cc.png)

--

--

This structure is returned in the regis­ ter · on arc l, where the action (SETR SUBJ *)places it in the register SUBJ. We move on to state S/SUBJ, looking at the word kick.

Kick satisfies the label on arc 2, so the condition is evaluated, checking the inflectional features in the dic­ tionary entry for kick. The predicate (GETF TNS) verifies that the verb is a tensed form (as opposed to a participle), and SVAGR ascertains that the person­ number code of the verb agrees with the noun-phrase stored in the register SUBJ. Since the condition is true, the transi­ tion is permitted and the action is executed, setting the register TNS to the value of the feature TNS (in this case it would be PAST) and saving the verb in V. At state VP/V, we have a

Figure 3: Trace of an Analysis

Sentence: The man kicked the ball.

STRING · (THE MAN KICKED ThE BALL) ENTERING STATE S/ ABOUT TO PUSh ENTERING STATE NP/ TAKING CAT DET ARC

STRING · (MAN KICK~D ThE bALL) ENTERING STATE NP/DET TAKING CAT N ARC

STRING · (KICKED THE BALL) ENTERING STAT~ NP/N ABOUT TO POP ENTERING STATE S/SUBJ TAKING CAT V ARC

STRING · (THI:: BALL) . ENTERING STATE VP/V STORING ALTARC ALTERNATIVI:: 76869a ABOUT TO PUSh ENTERING STATE NP/ TAKING CAT DET ARC

STRING · (BALL) ENTERING STATE NP/DET TAKING CAT N ARC

STRIHG · NIL ENTERING STATE NP/N ABOUT TO POP ENTERING STATE S/VP ABOUT TO POP SUCCESS 10 ARCS ATTEMPTED 195 CONSESb 1.8869999 SECONDSc PARSINGS:d S NP DET THE N MAN AUX TNS PAST VP V KICK NP DET ThE N BALL

- a. The alternative analysis path starting with arc 4 is saved.
- b. Number of memory words used.
- c. Processing time required.
- d. The recovered deep structure.

choice of two arcs. Arc 3 is a push for an object noun-phrase, which we can take since (TRANS V) is true, that is, since the verb in V (kills) is marked transi­ tive in the dictionary. We execute the push, identify the noun-phrase the ball, and save it in the register OBJ. At S/VP we pop the value of SBUILD, a function which applies a complicated BUILDQ to the registers SUBJ, TNS, V, OBJ, building the tree [9]. Notice that at this point we have exhausted the input string, achieved a final state, and emptied the push-down stack. Thus the sentence [6] is accepted by the grammar, and its deep structure is the structure returned by the final POP.

\_\_\_\_\_\_\_,\_\_\_\_

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000005_91686310e3fcd2512f51fb78ea954740f1d4ddaaee65472ad2d010adae68587b.png)

Sentence [7) is processed in the same way, except that arc 4 is taken instead of arc 3, since fell is marked intransi­ tive. Hence, the-resulting structure does not have the object NP node.

For these two examples and, indeed, for all sentences in the language of this grammar, the structure returned by the final POP directly reflects the history of the analysis -- the surface structure -- but this need not be the case. As a second illustration, we extend the grammar to deal with passive sentences, such as [lQ]:

## [10] The ball was kicked by the man.

We must add one new state, S/BY, a new arc to state VP/V and two new arcs to state S/VP. In addition, we must change the conditions on arcs 4 and 5. Figure 2 shows the new grammar, with new arcs in boldface and with only new and changed conditions and actions. For sentence [10] the new grammar works as follows: the ball is recognized as a noun-phrase and placed in SUBJ. Was passes the condition on arc 2, so PAST is stored in TNS and be is placed in V (as part of the category-checking operation, the inflected form was is replaced in 1 by its root). At thrs-point in the sentence, we do not know if be is a passive marker or a main verb as in [11].

[11] The ball was a sphere.

We make the assumption that it is a main verb, with the understanding that later information might cause us to change our minds and possibly rearrange the struc­ ture we have built. At state VP/V we find that we have indeed made a mistake. We first attempt the arc 9 transition. We are looking at kicked, the past par­ ticiple of a passivizable verb, and be is in V, so we can make the transition: the contents of SUBJ (the ball) are moved to OBJ and SUBJ rs-emptied (a regiater containing NIL is considered void). Then kick replaces be in V, and we re-enter state VP/V, looking at the word 2.1\_.

~is not a verb, so arc 9 is dis­ allowed. Kick is transitive, so we try pushing for-a-noun-phrase, but since 2.1\_ is not a determiner, the push is unsuc­ cessful. Arc 4 has been modified so that it can be taken if the verb is transitive but the object register has already been filled (the predicate FULLR is true just in case the indicated regis­ ter is non-empty), and we can therefore JUMP to S/VP.

At S/VP we cannot take arc 5 because we have no subject, so we try arc 10, a WRD arc. This arc type corresponds to the original finite-state grammar arc­ label, a symbol which must literally match an input word. Arc 10 specifies WRD BY and matches the current word, so the transition is allowed (NULLR is true when FULLR is NIL). At this point in the sentence, the only way we could not have a subject is if we had followed the passive loop. We therefore look for the deep subject of the sentence in a by­ phrase: we take arc 11, put the man in SUBJ, and return to S/VP, from which we pop. The resulting structure is identical to [9] -- we have undone the passive transformation. If the agent phrase had been omitted in [10], we would have taken arc 12 instead of the path through S/BY. Arc 12 is a JUMP that inserts the pronoun someone in SUBJ just in case there is no other way to get a subject.

These simple examples have illus­ trated the notation and underlying organization of the augmented recursive transition network. They have also demonstrated that transition network grammars can perform such transforma­ tional operations as movement, deletion, and insertion in a straightforward manner. We are now ready to examine the way in which transition network grammars can model performance data.

## IV. The Formalization of Perceptual Strategies

Bever (1) has surveyed the results of many psycholinguistic experiments and has inferred from the data that human beings use a small number of perceptual strategies in processin~ sentences. Some of these are corollaries of more general cognitive strategies and have observable reflexes in other areas of perception, while others are peculiar to language performance. As a set, these strategies account in part for the relative percep­ tual complexity of sentences and for some of the patterns of observed perceptual errors. In this section, we show how these strategies can be naturally repre­ sented in transition network grammars.

The dependent variable in a majority of psycholinguistic studies has been the difficulty subjects experience in proces­ sing sentences, as indicated for example by response latencies, recall errors, and the impact of various disturbances on comprehensibility. Thus the ultimate validation of transition network models will depend to a large extent on the correlation between experimentally obser­ ved complexity and complexity as measured in the transition network. There are several ways of defining a complexity metric on the network. We could count the total number of transitions taken in analyzin~ a sentence, the total num­ ber of structure building actions exe­ cuted or even the total number of tree­ nodes built by these actions. We could also use the amount of memory space or computing time required for a sentence in a particular implementation of the tran­ sition network parser (e.g., the number of conses (memory cells) or seconds in­ dicated in Figure 3). Of course, most intuitive measures of complexity are highly intercorrelated and lead to the same predictions, so our choice can be somewhat arbitrary. We will say that the complexity of a sentence is directly proportional to the number of transitions made or attempted during the course of its analysis.

With this definition the complexity of a sentence depends crucially on the order in which the network is searched for a successful path, although its acceptability by the grammar is indepen­ dent of the search-order. Unless spe­ cial mechanisms are invoked, the arcs leaving a state-circle are tried in clockwise order, starting from the top. Thus in Figure 2, arc 5 is attempted be­ fore arcs 10 and 12. If an attempted arc turns out to be permitted, then the remaining, untried arcs leaving the state are held in abeyance on a list of alternatives, and the legal transition is made. If the path taken is subse­ quently blocked, alternatives are re­ moved from the front of the list and tried until another legal path is found. As a result of this depth-first search, an ambiguous sentence will initially provide only one analysis; the other analyses are obtained by simulating blocked paths after successes.

## A. The Relations Between Clauses

Since sentences are frequently com­ posed of more than one clause, the native speaker must have a strategy for deci­ ding how the component clauses of a sen­ tence are related to each other (e.g., which is the main clause, which are re­ lative clauses, and which are subordi­ nate). Bever propounds that "the first N ·· V .. (N) clause ... is the main clause, unless the verb is marked as subordinate'' (1, Strategy B, p. 294), and points out that a sentence is perceptually more complicated whenever the first verb is not the main verb, even if it is marked as subordinate.3 Thus, sentences with preposed subordinate clauses [12b] are, according to this hypothesis, relatively more difficult than their normally or­ dered counterparts [12a]:

- [12] a. The dog bit the cat because the food was gone.
- b. Because the food was gone, the dog bit the cat. (·Bever's [24a-b]).

And in cases where the verb is sub­ ordinate but not marked as such, this strategy can lead to serious perceptual errors. Bever reports that subjects had much more difficulty understanding sen­ tences like [13a], where there is an illusory main verb and sentence (under­ lined), than [l3b], even though both sentences, being center-embedded, are exceedingly difficult:

- [13] a. The editor authors the newspaper hired liked laughed.
- b. The editor the authors the newspaper hired liked laughed. ("'Bever's [27a-L]l

The modifications to our transition network shown in Figure 4 can account for these facts. We have added two arcs at the S level to look for subordinatw clauses; a simple transition sequence (not shown) analyzes and builds the appropriate structure for them. Also, we have expanded states NP/ to allow null determiners, and NP/N to look for rela­ tive clauses. With this grammar, four more arcs, 1, 6, 17, and 7, must be attempted for [l2b) than for [12a). For [12b), first arc 1 is tried, causing a push to NP/ where arcs 6, 17, and 7 are tried and fail. We back up to state S/ and take arc 13, eventually ending up with the appropriate structure (the com­ plete sequence of attempted arcs is 1, 6, 17, 7, 13, SUBORD/ arcs (not shown), 1, 6, 7, 8, 2, 9, 3, 6, 7, 8, 14, 5). Note that we must still attempt arc 14, even though we know the condition will fail, because it is ordered before the pop arc, arc 5. For [12a), our first try at arc 1 takes us straight through to arc 14, where we pick up the subordinate clause, consider arc 14 again, and then pop at arc 5 (sequence · 1, 6, 7, 8, 2, 9, 3, 6, 7, 8, 14, SUBORD/arcs, 14, 5)

![Image](https://ijcai.org/proceedings/1971/fulltext/45/image_000006_d84f686274136b4b0f55e875d34fca0288768270b301f6454346c605ffec9ca5.png)

The difference between [13a) and [l3b) is equally well accounted for. Arc 15 looks for a relative clause on the noun-phrase, given that there is a rela­ tive pronoun following the noun. The arc has two new actions, SENDR and ADDR. Registers are subject to the control of the push-down recursion mechanism, so that when a push is executed, the regis­ ters' contents at the upper level are saved on the stack along with the actions to be executed upon return, and at entry to the lower-level, the registers are all empty. Upon popping, the upper level registers are restored. SENDR is a very special action: it can only appear on a PUSH arc, and it is the only action executed before pushing. It causes structures computed at the upper level to be placed in registers at the lower level. Thus the action (SENOR WH (NPBUILD)) causes the noun-phrase so far identified to be placed in the WH register at state R/, the beginning of the relative clause network (not shown). Based on the inter­ nal structure of the relative clause, the R/ network decides whether the relativized noun-phrase in WH is to be interpreted as the subject or object, analy~es the clause using parts of the S/ and NP/ networks, and returns the appropriate structure . (ADDR REL 1 ) causes this structure to be added on the right of the previous contents of REL,-so that a sequence of relative clauses can be processed by looping through arc 15.

In [13a-b), however, there is no relative pronoun, so we cannot take arc 15. For both sentences, a suc~essful an­ alysis requires that we push to state R/NIL (arc 16), the section of the rela­ tive clause grammar designed to analyze relatives with missing relative pronouns. But before we get to arc 16, we pop via arc 8 to state S/SUBJ. In [13aJ, the in­ put word at this point is authors, a pos­ sible verb, so we can take arc 2 to state VP/V. We continue on until we try to pop at arc 5 without having consumed the in­ put string (the current word is hired), and by the time we have backed up all the way to the appropriate arc 16, we have attempted seventeen arcs erroneously (se­ quence·l, 6, 7, 8, 2, 9, 3, 6, 7, 8, 14, 5, 10, 12, 15, 16, blocked R/NIL arcs, 17, 7, 4, 15, 16, R/NIL arcs, 8, 2, 9, 3, 6, 17, 7, 4, 14, 5). For [13b), since the is not a verb, we are blocked at state S/SUBJ, and we arrive at arc 16 having only attempted three wrong arcs (sequence ·1, 6, 7, 8, 2, 15, .16, R/NIL arcs, 8, 2, 9, 3, 6, 17, 7, 4, 14, 5). Inside the relative clause grammar, the noun phrase aut.hors in [13a] requires an extra transi­ tion at arc 17, so the net difference be­ tween the two sentences is fifteen arcs, not counting the blocked R/NIL arcs i~ [13a], a difference clearly in line with empirical perceptual complexity.

We have thus expanded our simple gram­ mar to accept and provide deep structures for a variety of cpnstructions. Our gram­ mar has the same formal power to describe these structures as a transformational grammar, but we have been able to arrange the analysis path so that complexity in our model corresponds to perceptual com­ plexity, as stated by Bever's Strategy B. We have taken advantage of the fact that, unlike the ordering of transformations, the order of arcs can be freely changed, radically altering the amount of computa­ tion required for particular sentences, without affecting the class of acceptable sentences.

## B. Functional Labels

A major task in sentence comprehen­ sion is the determination of the function­ al relationships of constituents within a single clause, of deciding who the sub­ ject is, what the action is, etc. Bever suggests a simple strategy for assigning functional labels based on the left-to­ right surface order of constituents: "Any Noun-Verb-Noun (NVN) sequence within a po­ tential internal [deep structure] unit in the surface structure corresponds to 'ac­ tor~action-object'" (1, Strategy D, p.298). Bever cites several perceptual studies involving sentences for which this stra­ tegy is misleading, and in all cases, these sentences were more difficult to re­ spond to than control sentences for which strategy D was appropriate.

There is very good evidence that pas­ sive sentences are more difficult to pro­ cess than corresponding actives, in the absence of strong semantic constraints. Given strategy D, this follows from the fact that the surface order of passives is object-action-actor. Similarly, progres­ sives [14a] have been found to be signifi­ cantly easier to comprehend than superfi­ cially identical participial constructions [14b] (18).

[14] a. They are fixing benches.

- b. They are performing monkeys. (·Bever's [31 a-b])

According to strategy D, performing is initially accepted as the main verb, un­ til the spurnous direct object monkeys is encountered; at this point the labels

must be switched around.

Bev·r explains these processing dif­ ficulties in terms of the amount of re­ labeling that is required, given that stra­ tegy D can lead to errors. This translates into the proposition that relative com­ plexity is measured by the degree to which constituents are shifted in registers, since assigning a constituent to a regis­ ter is the transition network analog of functional labeling. Indeed, Figure 2 shows that SUBJ is reset twice more for passives than for actives, while in Fig­ ure 5 participial sentences require one extra register assignment (NMODS). How­ ever, we have defined complexity in terms of the number of arcs attempted, and we now show that this measure can also account

for the experimental results.

Figure 2 contains the arcs necessary for passive sentences. Simple active [6] and passive [10] sentences are treated identically until state VP/V is reached. Arc 9 is attempted for both of them and is taken for the passive, returning to VP/V. 9 is attempted again but fails, and then twelve additional arcs are tried before the successful final pop is executed. Since only six additional arcs are attemp­ ted for the active, the difference in fa­ vor of the relative complexity of the pas­ sive is six. (The difference is seven for the more complicated grammar in Figure 5.)

Figure 5 gives the necessary modifica­ tions for the progressive and participial constructions (in bold face). Arc 18 can be taken only if the current word is a present participle and the previously identified main verb is be. The actions put the new verb in V an~mark TNS as pro­ gressive. Arc 19 simply adds an identi­ fied participle to NMODS, where the func­ tion NPBUILD will find it. The analysis of [14a] is simple: at state VP/V, the current word will be fixing and be will be in V; so that arc 18 can be taken. Since fix is transitive, benches will be identified as the direct object, and the pop at arc 5 will be successful. [14b] involves considerably more effort. At VP/V, arc 18 will also be taken but arc 3 is ruled out with perform in V (see foot note 4). Before returning to arc 3 with be in V, arcs 4, 14, 5, 10, and 12 will be tried, and additional arcs will be at­ tempted in deriving the correct partici­ pial analysis (we assume that be is marked tran~itive). -- Thus the functional-relabelling and the attempted-transitions explanations ac­ count equally well for the experimental observations. At present we have no firm empirical basis for choosing one complex­ ity measure over the other; we must find crucial sentences where the measures make opposing predictions and let the data de­ cide for us. So far, we have been unable to discover such sentences.

## C. Prenominal Adjective Ordering

Another problem concerns the segmen­ tation of superficial sequences of words into structural units. Where does a noun­ phrase begin, for example, and where does it end? That these are not trivial ques­ tions is illustrated by [15a-b], where the role of marks is unclear until the

whole sentence has been processed.

- [15] a. The plastic pencil marks easily.
- b. The plastic pencil marks were ugly. (·Bever's [66a-b])

Of course, no matter what perceptual stra­ tegy is involved in making these decisions, the transition network will continue try­ ing alternative paths until it arrives at the correct segmentation, but an appropri­ ate strategy would make the analysis more efficient. Bever suggests that in recog­ nizing the end of a noun-phrase, native speakers use a strategy which also ac­ counts for the anomalies in such pairs as

(without contrastive stress):

- [16] a. The red plastic box ..·
- b. *The plastic red box ...
- c. The large red box ...

## d. *The red large box ... (·Bever's [67a-d])

He cites the theories of Martin (17) and Vendler (25) which essentially claim that the more "nounlike" an adjective is5, the closer to the noun it must be placed.Thus the anomalies in [16] are accounted for if we assume that plastic is more nounlike than red and red is more nounlike than large-.--Although the notion nounlike is not made very precise, Bever gives heuris­ tic arguments that these assumptions are correct. He then postulates that the end of a noun phrase is signalled by a word which is less nounlike than preceding words (1, Strategy E, p. 323). Since large is less nounlike than red, the ini­ tial noun phrase in [16d] must be the red.

This constraint is difficult to ex­ press in traditional transformational for­ malisms but is quite directly represen­ table in the transition network. It not only makes the transition network more congruent with performance data but also helps to rule out the anomalies in [16]. Assuming that nounlike is well-defined and that all potential nouns (including adjec­ tives) are in category N and have their nounlike-ness marked in the lexicon, the dashed arc in Figure 5 is the necessary addition to the network. Arc 20 is attemp­ ted before the pop from NP/N. If the noun­ like-ness of the current word is greater than or equal to that of the word inN, then the word in N is not the head of the noun-phrase. We add this word to the list of modifiers in NMODS, and place the cur­ rent word in N, as a new candidate for head noun. We continue looping until we find a word that is less nounlike than the head, marking the end of the noun-phrase. This procedure will accept [16a,c] but re­ ject [16b,d] except in constructions along the lines of [17]. In [17] the adjectives are accepted only because they can be ana­ lyzed in separate noun-phrases:

## [17] I like the plastic red boxes are made of.

## V. The Justification of Transition Net­ work Models

In the preceding sections we illus­ trated the simple way in which transition network grammars can express some of Be­ ver's perceptual strategies. The transi­ tion network analyzes strings in essen­ tially linear order, and the grammatical notation is flexible enough so that gram­ mars can be devised to fit wide ranges of performance facts. However, to justify the effort needed to simulate experimental data with network models, we must show that the resulting grammars offer substan­ tial advantages compared to informal ver­ bal interpretations, such as Bever's. In this section we argue that these grammars are both conceptually and empirically pro­ ductive: they lead to new theoretical questions, and they suggest new lines of experimentation, predicting specific out­ comes. To the extent that the predictions of a particular grammar are confirmed, that grammar is validated as a model of the psychological processes involved in sentence comprehension.

The grammar shown in Figure 5, while only a small fragment of a complete Eng­ lish grammar, will suffice to exemplify the empirical implications of transition network models. It has been designed to account for the data underlying the per­ ceptual strategies discussed above, but it also encompasses independent findings. The grammar mirrors the perceptual stra­ tegies just so lon& as a depth-first search procedure is used to discover suc­ cessful analysis paths. This search or­ der implies that for truly ambiguous sen­ tences, one interpretation will be recov­ ered before the other; if required, the second interpretation can be recovered by simulating a failure and continuing the analysis. This is in line with the re­ sults of MacKay and Bever (15).and Foss et al. (9): MacKay and Bever found sub­ jects to be aware that they arrived at one interpretation of an ambiguous sen­ tence first and could even report what the first interpretation was. Foss et al. discovered that subjects tend to-rn­ terpret ambiguous sentences in only one way; if the first interpretation is incom­ patible with the experimental context, they can usually go on to find another interpretation, although additional time is required. The search strategy under­ lying the Figure 5 grammar accounts for these results even though the experiments are not implicated in the perceptual stra­ tegies the grammar was designed to repre­ sent.

For ambiguous sentences within its scope, the grammar clearly predicts which interpretation should predominate. Other things being equal, the first interpreta­ tion will have essentially the same analy­ sis as the less complex of two unamb~gu­ ous sentences with the same surface struc­ ture. Thus in a replication of the Foss et al. experiment, the first analysis of maT should be the progressive, resem­ bling [l~a], while the participial deep structure [l~b] should come out second. Subjects should first arrive at the in­ terpretation paraphrased in [18b], ra­ ther than [18c]:

- [18) a. They are flying planes.
- b. Those people are piloting aircraft .
- c. Those things are planes which are sailing through the air.

The Figure 5 grammar similarly pre­ dicts the outcome for another class of ambiguous sentences, where a word can be analyzed either as the head of the sub­ ject noun-phrase, utilizing the prenomi­ nal adjective arc. or as the intransitive main verb [19-20). With the nounlike-ness markings which permit the ambiguity, the

- [19) The Irish water boils,
- (a) juat like any other water.
- (b) but they still hurt.
- [20] The French bottle smells,
- (a) since it contained vinegar.
- (b) as well as Coca-Cola.

grammar predicts that the interpretations corresponding to the (a) continuation will appear first . Thus again our simple grammar has concrete empirical implica­ tions.

It should be noted that the ambigui­ ties in [19-20] involve a conflict between two of Bever's perceptual strategies. The (a) interpretations follow from the pre­ nominal adjective strategy while the (b) interpretations are consistent with the functional labelling strategy, with water and bottle considered as the first verbs. Bever presents his strategies in isolation from one another, without specifying their interrelationships, but the transition network formalism requires the integration of all strategies into a single system. Potential strategy conflicts are high­ lighted, usually in the form of questions about the relative ordering of two or more arcs leaving a state, and the alter­ native grammar formulations often lead to the discovery of crucial cases that can be studied experimentally. Thus for example, the prenominal adjective and the function­ al labelling strategies could have been expressed in another grammar with the op­ posite precedence relation, so that the (b) interpretations in [19-20) would be ju~ged less complex than the (a) ones. The choice between the Figure 5 grammar and this alternative model depends on the outcome of the ambiguity experiment de­

scribed above.

Besides empirical consequences, ques­ tions of strategy interaction have impor­ tant conceptual ramifications. Bever re­ marks that perceptual strategies express

"generalizations which are not necessari­ ly always true" (1, p. 294, footnote 2), that each isolated strategy will be mis­ leading in some cases. The strategies thus serve as heuristic guidelines to the listener and do not directly reflect his abstract appreciation of the structure of his language. A transition network model, on the other hand, incorporates a set of strategies and clarifies their interac­ tions; the set as an integrated whole is valid if it fails only for sentences which are truly unacceptable. Thus a transition network model is intended to make asser­ tions about the-listener's linguistic knowledge, whereas a set of isolated per­ ceptual strategies is not.

Transition network models raise other conceptual issues: we have already mentioned the question of selecting an appropriate complexity metric for the net­ work, which is related to the problem of determining a small set of primitive, psychologically relevant actions and pre­ dicates. The network formalism also pro­ vides a new vocabulary for discussing the processes of language acquisition. We can imagine that as a child's linguistic abilities develop, a transition network model of his perceptual performance will evolve in stages of increasing elabora­ tion, much as the grammar in Figure 5 grew out of Figure 1. New predicates and actions will appear, new arcs and states will be added, the order of arcs will be adjusted, and old and new arcs will in­ teract to handle new syntactic construc­ tions. It should be possible to demon­ strate small, systematic deformations be­ tween the grammars representing the var­ ious levels of acquisition, and the se­ quence of grammars should have strong im­ plications for models of adult performance. Finally, it is conceivable that detailed investigations of transition network ac­ quisition grammars will lead to an algo­ rithm that simulates the language acqui­ sition process, that takes the kinds of data available to children at the differ­ ent stages and devises appropriate per­ ceptual models.

## VI. Conclusion

The augmented recursive transition network we have described is a natural medium for expressing and explaining a wide variety of facts about the psycho­ logical processes of sentence comprehen­ sion. We have shown how several percep­ tual strategies can be represented, and in the last section we explored some of the empirical and conceptual implications of these formalizations. These considera­

tions illustrate the usefulness of transition network grammars as research tools and support their validity as perceptual models.

Of course, there are several impor­ tant issues we have not touched on: the role and representation of semantic in­ formation in sentence comprehension, the differences between the processes of sen­ tence perception and production, and the correspondences between transition net­ work grammars and conventional transfor­ mational rules. We are currently inves­ tigating these problems. We are coupling the transition network parser to a seman­ tic network so that non-syntactic fea­ tures and context can guide the course of sentence analysis and lead to appropriate semantic interpretations. We are also studying the formal and practical dif­ ficulties in using the transition network notation for writing generative grammars; we hope to find a simple algorithm for mapping adequate perceptual models into equivalent production grammars. And fi­ nally, we are constructing two large transition network grammars, one based primarily un performance data and the other intended to capture generalizations about linguistic competence as transfor­ mational grammars express them. We ex­ pect these grammars to converge, giving a single grammar and one notation for modelling both competence and performance. Reports on these investigations are in preparation.

## Acknowledgements

The research reported in this paper represents a synthesis of work on tran­ sition network grammars done in close collaboration with William Woods, and work on the psychological processes in­ volved in sentence comprehension done with Eric Wanner. Both have read earlier versions of this paper ahd have offered valuable suggestions. I am also indebt­ ed to Philip Stone and Bruce Fraser for many useful comments.

This research was supported in part by the National Aeronautics and Space Administration, through Contract NAS911157 to the Language Research Foundation, Cambridge, and in part by a grant from the Milton Fund of Harvard University to Eric Wanner.

## Footnotes

1. The transition network parser was designed by William Woods. It is pro­ grammed in BBN-LISP and is currently running under the TENEX monitor .system on a PDP-10 computer at Bolt, Beranek
2. and Newman, Incorporated, Cambridge, Massachusetts.
2. The parser is presently being used as the natural-language front end of a system for accessing geological data on the Appollo lunar samples.
3. Relative pronouns are also consider­ ed markers of subordinate clauses, so that Bever's strategy B would predict that relative clauses on subject ·noun­ phrases should add more to perceptual complexity than the same clauses in post­ verbal, object positions. If this is true, relative clauses should not be identified by a push within a noun-phrase, for this predicts the same degree of com­ plexity for all relative clauses, no matter where they appear in relation to the main verb. We are currently explor­ ing the possibility of analyzing rela­ tives on subjects at the s level, by a system of arcs emanating from state S/SUBJ. This approach complicates the grammar to some extent, but it appears that it can account for the difficulty with subject relatives as well as the tremendous complexity of center-embedded sentences. It also explains the obser­ vation by Blumenthal (2) that subjects tend to perceive center-embedded con­ structions as simple structures of con­ joined nouns and conjoined verbs. We will report on the details of this ap­ proach in the near future.
4. The problem with {14b] is not that perform is intransitive, but that mon­ ~ is not permitted as its objecr:­ TITUS, it is perfectly acceptable to say They are performing ~lays. A more pre­ cise grammar would a low the push at arc 3 and then when the noun-phrase monkeys is returned, it would be compared with the verb's object selectional restric­ tions and the analysis path would be aborted. We make the assumption that the verb is intransitive for simplicity of exposition.
5. Intuitively, an adjective is more "nounlike" the more syntactic or semantic properties it shares with nouns. Thus ~ is ranked below red and red below plastic because of the-relative-number of noun frames they can fit in, as in (a-d):
7. (a) I like red.
8. (b) 'I like large.
9. (c) The toothbrush is made of plastic.
10. (d) 1 The toothbrush is made of red.

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