Emulation of Human Sentence Processing using an

Automatic Dependency Shift-Reduce Parser

Atanas Chanev

Logical Factor Bulgaria, Yakubitsa Street 2a, 1164 Soﬁa, Bulgaria

Abstract. The methods of NLP and Cognitive Science can complement each

other for the design of better models of the human sentence processing mecha-

nism, on the one hand, and the development of better natural language parsers, on

the other. In this paper, we show the performance of an automatic parser consis-

tent with the architecture of the human parser of [2] on various human sentence

processing experimental materials. Moreover, we use a linking hypothesis based

on the concept of surprisal [9] to explain human reaction time patterns. Although

our results are generally not consistent with the human performance, our emu-

lations contribute to understanding the architecture of the human parser and its

disambiguation strategies better. We also suggest that these strategies may possi-

bly be used for improving the performance of automatic parsers.

1 Introduction

Some of the models of the human sentence processing mechanism reported in the recent

years are capable of achieving wide coverage on random corpora (e.g. [7]). Most of

these models are based on natural language parsing algorithms. On the other hand,

automatic parsers can beneﬁt from knowledge about the way humans process sentences

in natural languages (e.g. see [2] for examples and discussion).

The process of selection and extension of a natural language parser to a model of

the human sentence processing mechanism has been reported in [2]. They show that the

porting can be done in three stages, as illustrated below: preparing a list of constraints

for the model based on general knowledge about the human parser, as well as evidence

from experiments with human subjects; design of the architecture and association of a

linking hypothesis which should be capable of emulating and explaining reaction times

of humans in sentence processing experiments.

Constraints → Architecture → LinkingHypothesis

In this paper, we report several emulations of the human sentence processing mech-

anism using sentences from [9]. We have used an automatic dependency parser, [1]

which is compatible with the model of the human parser of [2]. However, we have

used a new linking hypothesis to explain the relationship between the architecture of

the parser and the performance of human subjects in sentence processing experiments.

This linking hypothesis is based on the concept of surprisal, [9].

Chanev A. (2008).

Emulation of Human Sentence Processing using an Automatic Dependency Shift-Reduce Parser.

In Proceedings of the 5th International Workshop on Natural Language Processing and Cognitive Science, pages 137-146

DOI: 10.5220/0001732001370146

 SciTePress

We emulated properly human difﬁculty for only one of ﬁve sets of experimental

materials. We have performed error analysis in the remaining cases to discover the

reasons for the performance of our models. We have suggested three ways to improve

our models in order to emulate human sentence processing better.

The paper is structured as follows: In Section 2 we review the model of [2]. Then, in

Section 3, we present our linking hypothesis for explaining human difﬁculty patterns.

We describe our experimental settings in Section 4. Then, in Section 5, we report our

results. We conclude in Section 6 and list our future plans in Section 7.

2 The Model of (Chanev, 2007) Revisited

(Chanev, 2007), [2] argue for the psychological plausibility of the class of deterministic

dependency shift-reduce parsers. They propose an architecture of the human sentence

processing mechanism. Compared to connectionist models, it is more robust and has a

wider coverage, whereas compared to other broad coverage models of the human parser,

it is more detailed than e.g. [9], and its parsing algorithms are more incremental than

e.g. the top-down algorithm used in [7]

We use an automatic parser from the class of dependency shift-reduce parsers in our

experiments. The basic features of [2] are described below. They include the constraints

satisﬁed by the model, the architecture and a linking hypothesis.

The model of [2] satisﬁes the following constraints:

General Constraints. Wide coverage, high accuracy, robustness and multilinguality.

Architectural Constraints. Incrementality and non-projectivity

Informational Constraints. Lexical frequency, discourse context, semantic plausibil-

ity, prosodic breaks and syntactic preferences.

The model of [2] uses Dependency Grammar,e.g. [11]. Thus, it recognizes sentence

structure as a set of binary head-dependent relations. In Figure 1, we show the depen-

dency structure of a simple sentence. Dependency grammar can be considered a good

choice for a model of the human parser, e.g. because non-projective relations can be

encoded in the syntactic tree easily.

Yesterday, I saw the dog.



subj



det

obj



tmp

Fig.1. The dependency structure of a simple sentence.

The interested reader is referred to [2] for a detailed comparison of the dependency shift-reduce

parsing architecture with other models of the human parser.

Non-projectivity is the ability of a parser to process non-projective grammatical relations, e.g.

the one in the sentence “I saw the dog yesterday with the red nose.” between the head of the

noun phrase The dog and the head of the prepositional phrase with the red nose.

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The architecture of [2] is serial (i.e. it is not parallel). A scheme is shown in Figure

2. The following components can be distinguished:

Stack – for storing partially processed word tokens;

Input Memory – for tokens that have not been yet integrated to a temporary sentence

structure or stored in the stack;

Parsing Actions – for pushing tokens into the stack and popping them out, as well as

to build dependency relations between tokens;

Memory for Processed Tokens – used for tokens that have been popped from the stack

or removed from the input memory;

Classiﬁer – used to learn the sequence of parsing actions needed to build the depen-

dency tree of the sentence.

Processing unit:

shift (push)

reduce (pop)

right

left

non-proj. left

other actions

TOP

...

Stack

ACTIVE

Input

Classiﬁer

Memory for

processed tokens

Fig.2. The model of the human sentence parsing mechanism of [2].

The classiﬁer componentof the model consists of three functionalmodules: a database

of language experience; a learning algorithm and a feature model. The latter speciﬁes

the conﬁguration of features of certain word tokens in the sentence to be learned for

determining next parsing actions.

One of the linking hypotheses of [2] is inﬂuenced by the Surprisal model [7, 9].

The study of [7] was the ﬁrst to measure the surprisal associated with the integration

of each word into a temporary syntactic structure of the sentence. They calculate the

surprisal using preﬁx probabilities [14]. Then, the model has been generalized in [9] for

arbitrary grammars and parsing algorithms deriving the formula for surprisal on infor-

mation theoretical grounds. Both of the models assume parallel activation of syntactic

structures.

In [2] difﬁculty is measured similarly to the way it is measured in the Surprisal

model. However, since the main component of their architecture is a serial dependency

shift-reduce parser, the likelihood, as assigned by the classiﬁer, is measured instead

of preﬁx probabilities. It is calculated with respect to the particular learning algorithm

used by the classiﬁer and over parsing actions rather than syntactic sub-trees.

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3 Our Linking Hypothesis

Our linking hypothesis is in the spirit of the Surprisal model. It is naturally implemented

in the automatic parser that we use, DeSR

, [1] and is deﬁned in terms of the Average

Perceptron learning algorithm, [4] as implemented in the parser.

We had to adapt the Surprisal model which assumed a parallel architecture, to DeSR

which is a serial parser. However, multiple activations of different syntactic interpreta-

tions, as in the Surprisal model, can still be emulated in DeSR through feature models

that avoid learning syntactic dependencies explicitly. In addition, DeSR can measure

the human difﬁculty, using spans of the sentence that does not necessarily begin with

its ﬁrst word, unlike [9]. This makes our model more ﬂexible than the Surprisal theory

while still possessing its basic characteristics.

The Average Perceptron is a multi class perceptron [4]. Each of the classes is a

parsing action, e.g. shift, right (subject), right (determiner), left (object) etc. At each

step, the most likely action is executed. In order to measure the difﬁculty associated

with the integration of a token, we use the likelihood of the integrating parsing action.

Thus, the higher the likelihood is, the lower the human difﬁculty would be.

It must be noted that a word token can be integrated to the temporary syntactic

structure of the sentence as a syntactic dependent exactly once, and as a syntactic head,

zero or more times. In the cases where the integration of the word is done through more

than one dependency relations, the difﬁculty is measured as the sum of the difﬁculties

of building all the relations between the word, its partially processed dependents, if any,

and its partially processed head word.

4 Experimental Settings

Corpus. We used the training set of the dependency version of the Penn treebank [10]

as used in the CoNLL 2007 shared task on dependency parsing [13]. In this format, the

treebank is annotated with part-of-speech tags and dependency syntax. Moreover, we

used the supersense tagger of [3] to annotate the texts in the treebank with semantic

WordNet classes. We merged all the information into one resource and used it to train

our models. We annotated our test set with part-of-speech information, using the SVM-

Tagger

[5] and corrected the errors manually. Then, we used the supersense tagger for

annotating it with semantic classes. Finally we merged all the information.

Feature Models. We started our experiments with the best model for English that was

in the package of the DeSR parser but removed some of the features that we had found

implausible for a model of the human parser. These include the properties of tokens

that are too far ahead in the sentence. We also added semantic features and used a

ﬁrst order Average Perceptron. We trained two models, Base and Syntactic, because we

wanted to distinguish between a model that uses informationabout a particular syntactic

structure for making parsing decisions (Syntactic) from a model that does not use such

information (Base). Base should score similarly to the surprisal model due to measuring

Freely available from http://sourceforge.net/projects/desr/

http://www.lsi.upc.es/∼nlp/SVMTool/

140

the total likelihood of a string instead of the likelihood of a speciﬁc structure associated

to a string.

We show the model that has syntactic features in Table 1. We use the notation of

DeSR. The tokens in the stack are encoded with negative numbers where −1 is the

token that is on the top of the stack. The tokens in the input are encoded using 0 and

positive numbers where 0 is the ﬁrst token in the input.

Table 1. Our syntactic feature model.

Feature tokens Feature tokens Feature tokens Feature tokens

LexFeatures -2 -1 0 1 LexPrev 0 LexSucc -1 SemFeatures -2 -1 0 1

SemLeftChild 0 SemRightChild -1 SemPrev 0 SemSucc -1

PosFeatures -2 -1 0 1 PosLeftChild -1 0 PosRightChild -1 0 PosPrev 0

PosSucc -1 CPosFeatures -1 0 1 DepLeftChild -1 0 DepRightChild -1

Parsing Accuracy. We show the accuracy of the parser in Table 2. High accuracy is

an important pre-requisite for the proper emulation of human reaction times because

the patterns obtained from integrating inaccurate dependency relations would be very

different from human difﬁculty patterns, in the general case.

Table 2. The accuracy of our models.

Model Labeled Attachment Score Unlabeled Attachment Score

Base 77.6% 79%

Syntactic 77.5% 78.9%

We solve the problemof erroneousdisambiguation by ‘forcing’the parser to execute

certain actions which are less likely than those which it otherwise would have executed,

if guided by the classiﬁer. Still, if the parser has a very low accuracy,that would ‘distort’

the likelihood of parsing actions and the predicted difﬁculty, respectively.

5 Emulation of Experiments with Human Subjects

We have emulated human behavior on sentences from ﬁve experiments.They have been

selected among the experiments used by [9] to evaluate the Surprisal theory

. We report

the performance of our models on only one sentence from each condition of each exper-

iment. We do not need to average over many experimental items, because the models

have sufﬁcient linguistic knowledge to make parsing decisions in a plausible way.

The human difﬁcultypatterns for certain regionsin the sentences of two experiments

have been consistent with the Surprisal theory. In the other three experiments, difﬁculty

patterns for certain regions of the sentences are not consistent with the Surprisal theory.

We have tried to emulate human behavior for all the sentences using our models.

Our models have been tested on all the experimental materials used in [9]. We report only

ﬁve experiments due to the lack of space. However, they are sufﬁcient to illustrate the most

important aspects of using our models on the data of [9].

141

Experiment 1. The ﬁrst experiment is reported by [8]. It investigates the relevance

of surprisal in three sentences with a different number of words between the subject

modiﬁed by a relative clause and the main verb, for the difﬁculty observed at the main

verb. The sentences are given below:

1. The Player [that the coach met at 8 o’clock] bought the house...

2. The Player [that the coach met by the river at 8 oclock] bought the house...

3. The Player [that the coach met near the gym by the river at 8 oclock] bought the

house...

The main verb of sentence 3 has been read faster than the one of sentence 2 which

has been read faster than the one of sentence 1. The prediction pattern of the surprisal

theory is the same as the human reaction times (or difﬁculty) pattern. The explanation

of the Surprisal theory is that with the increase of the number of words in the relative

clause, the expectation for the main verb increases.

However,we report a different pattern. The Base model predicted the same difﬁculty

at the main verb for all the sentences. The likelihood numbers of the Average Perceptron

for the integration of the main verb are the same: 119 for attaching the subject to the

main verb and 166 for the recognition of the main verb as the root of the sentence. On

the other hand, the Syntactic model predicted a difﬁculty for sentence 2 (likelihood of

166 for the subject relation and 194 for the root relation), if compared to sentences 1

and 3 (146 for the subject relation and 194 for the root relation).

Our models cannot account for surprisal effects, because at the time of parsing the

features of no previous tokens that are not in the stack except for the adjacent left token

are taken into consideration. Thus, the tokens responsible for the parsing decision are

the same for the three sentences, for the Base model or almost the same for the Syntactic

model. The way to account properly for the differences in the sentences is to include

more past tokens in the feature models or to include features for distance.

Experiment 2. Experiment 2 uses materials from [16]. In their experiments it has been

shown that an unresolved ambiguity can facilitate comprehension. The sentences are

given below:

1. The daughter of the colonel who shot herself on the balcony had been very depressed.

2. The daughter of the colonel who shot himself on the balcony had been very depressed.

3. The son of the colonel who shot himself on the balcony had been very depressed.

In [16] they have measured the difﬁculty of integrating the relative pronoun to the

temporary syntactic structure. They have shown that the difﬁculty of integrating the

relative pronoun in sentences 1 and 2 is greater than the one of integrating it in sentence

3. The Surprisal model predicts this pattern because the integration in the ambiguous

case would have a probability that is the sum of the probabilities of the two different

interpretations. The greater probability would mean a smaller surprisal and a smaller

difﬁculty, respectively.

The Base model predicted a likelihood of 116 for sentence 1; 107, for sentence 2 and

100 or 107, for sentence 3, depending on the syntactic interpretation. On the other hand,

the Syntactic model predicted a likelihood of 132 for sentence 1; 126, for sentence 2

and 120 or 126, for sentence 3 depending on the syntactic interpretation. Taking the sum

of the likelihoods of the two interpretations of sentence 3, both of our models would

emulate the human pattern.

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The only demerit of that assumption is that our model is strictly serial. Summing

likelihoods of different attachments would assume parallel processing at least at some

point in the sentence. However, there might be an alternative interpretation of the re-

sults. The DeSR parser cannot distinguish between the ambiguous situation and the

situation where the relative pronoun is attached low (i.e. in sentence 2). The solution

is to use differently deﬁned parsing actions, e.g. the actions of Maltparser [12]. This

would also make the parsing algorithm more incremental.

Experiment 3. In Experiment 3 we use materials from [6]. In the sentences below, ma-

trix verbs from subject extracted relative clauses have been easier to process by humans

than those from object extracted relative clauses. It must be noted that the surprisal

model cannot explain the observed difﬁculty pattern.

1. The reporter who sent the photographer to the editor hoped for a good story.

2. The reporter who the photographer sent to the editor hoped for a good story.

There are two dependency relations that are to be built for the integration of ‘sent’

to the temporary syntactic structure of the sentence with the subject extracted relative

clause. One of them is the subject relation between ‘who’ and the matrix verb of the

relative clause. The other is the modiﬁer relation between ‘reporter’ and the matrix verb.

The dependency relations to be built for the integration of ‘sent’ in the object extracted

relative clause are: the subject relation between ‘photographer’ and ‘sent’; the modiﬁer

relation between ‘reporter’ and ‘sent’ and the object relation between ‘who’ and ‘sent’.

The difﬁculty at the integration of ‘sent’ would depend on the total likelihood of all the

syntactic relations to be built between the matrix verb of the relative clause and other

tokens.

The Base model integrates ‘sent’ in sentence 1 with a likelihood of 268 (133 for

the subject relation and 135 for the modiﬁer relation). It integrates ‘sent’ in sentence 2

with a likelihood of 144 (4 for the subject relation, 116 for the modiﬁer relation and 24

for the object relation). The pattern is as predicted because the more likely integration

causes less difﬁculty. The Syntactic model shows a similar pattern. For sentence 1, the

matrix verb of the relative clause is integrated with a likelihood of 300 (146 for the

subject relation and 154 for the modiﬁer relation). In sentence 2 the likelihood of the

integration of ‘sent’ is 220 (38 for the subject relation, 159 for the modiﬁer relation and

23 for the object relation). The pattern is again as predicted.

Experiment 4. In experiment 4 we use materials from [6]. There are three sentences

with an object extracted relative clause modifying the subject of the sentence. The sub-

ject in the relative clause is with varying length. The sentences are given below:

1. The administrator who the nurse supervised scolded the medic ...

2. The administrator who the nurse from the clinic supervised scolded the medic ...

3. The administrator who the nurse who was from the clinic supervised scolded the

medic ...

In [6] it is reported that the difﬁculty associated with the integration of the verb

of the relative clause increases with the increase of the distance to the subject of the

relative clause. This means that the verb of the relative clause of sentence 1 will be less

difﬁcult to integrate than the one in sentence 2 which will be less difﬁcult to integrate

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than the one in sentence 3. It must be noted that the Surprisal model cannot explain the

observed difﬁculty pattern.

There are three syntactic relations to be built in order to integrate ‘supervised’ into

the temporary syntactic structure of the sentence. They are: the subject relation be-

tween ‘nurse’ and ‘supervised’; the modiﬁer relation between ‘administrator’ and ‘su-

pervised’; and the object relation between ‘who’ and ‘supervised’.

In this experiment both of our models predict what the surprisal model would. That

is, with the increase of distance, difﬁculty will not increase but decrease. The Base

model assigns likelihoods of 258 (117 for the subject relation; 51 for the object relation

and 90 for the modiﬁer relation) for sentence 1; 265 (124 for the subject relation; 51 for

the object relation and 90 for the modiﬁer relation) for sentence 2 and 280 (139 for the

subject relation; 51 for the object relation and 90 for the modiﬁer relation) for sentence

The patterns of the Syntactic model are similar to those of the Base model. The

likelihood of the integration of the verb of the relative clause is 301 (129 for the subject

relation; 55 for the object relation and 117 for the modiﬁer relation) for sentence 1; 322

(152 for the subject relation; 55 for the object relation and 115 for the modiﬁer relation)

for sentence 2 and 338 (168 for the subject relation; 55 for the object relation and 115

for the modiﬁer relation) for sentence 3.

The possible reason for our results is the lack of features of past tokens that are not

in the stack of the parser. We should also mention that distance can be included as a

feature in a parsing model and used in the learning phase.

Experiment 5. In experiment 5 we have used materials from [15]. They have shown

that reduced relative clauses with non-subject context where the modifying verb has

different forms for past tense and past participle, are easier to understand than reduced

relative clauses with non-subject context where the modifying verb is ambiguous. It

must be noted that the surprisal model cannot explain the observed difﬁculty pattern.

The sentences are given below:

1. The coach smiled at the player thrown the frisbee.

2. The coach smiled at the player tossed the frisbee.

In [15] it has been shown that the integration of ‘thrown’ into the syntactic structure

of sentence 1 is easier than the integration of ‘tossed’ into the syntactic structure of

sentence 2. In both of the sentences, the verb in the relative clause is attached to the

preceding noun with a modiﬁer relation.

None of our models shows any clear pattern. The Base model attaches ‘thrown’ to

‘player’ with a likelihood of 125 and ‘tossed’ to ‘player’ with a likelihood of 126. These

numbers for the Syntactic model are 145 and 144, respectively. The major issue in this

experiment is the use of the past participle part-of-speech tag for the verb ‘tossed’. The

use of this tag assumes that the main-verb/reduced relative clause ambiguity has been

resolved and it should not have been resolved at the time of integration.

There are two ways to overcome this demerit of the parser: to use a single tag for the

verbs in past tense and participle form or to integrate part-of-speech tagging into parsing

vertically. Both of these solutions have their demerits and bottlenecks. For example, the

implementation of the former would prevent the parser from using valuable information

144

in disambiguation. The implementation of the latter would put too much weight on the

knowledge-poor part-of-speech component to take important parsing decisions.

6 Conclusions

In this paper we have used two models to parse experimental materials from a num-

ber of studies. Our models have predicted the pattern of human difﬁculty for only one

of ﬁve experiments. The reasons for the performance of the models in the other four

experiments can be classiﬁed as follows:

In experiment 1 and experiment 4, the reason is the lack of features of previous

tokens that are not in the stack. It may also be useful to use the number of words between

a head and its dependent as a feature in the feature model. It must be noted that the

Surprisal model can explain human difﬁculty patterns in experiment 1 but it cannot

explain them in experiment 4. We believe that there is one and the same reason for the

performance of our models on the materials from both experiment 1 and experiment 4.

The reason for the performance of our model on the materials from experiment 2

is the deﬁnition of the parsing actions of the parser that we use. This way, our models

cannot distinguish between an unresolved ambiguity case and one of the unambiguous

cases at the time of integration. One way to address this demerit is to use differently

deﬁned parsing actions such as the actions of another dependency shift-reduce parser,

Maltparser [12]. This would allow processing in a more incremental way, as well.

For experiment 7, part-of-speech tagging and parsing should be integrated in a rea-

sonable way, in order for the experiment to be plausibly conducted. A possible integra-

tion should result in a main verb/reduced relative clause disambiguation performed by

both the part-of-speech tagger and the parser, at the same time.

Taking into consideration our emulation results, we have identiﬁed three ways to

make our models of the human parser more plausible. They are: a change in the feature

model for training; using parsing actions that would make processing more incremental

and the integration of part-of-speech tagging and parsing for joint disambiguation of the

main-verb/reduced relative clause ambiguity. We expect that using these techniques, it

could be possible to increase the accuracy of the parser, as well.

7 Future Work

For future work, we intend to change the feature model of the parser incorporating

features of previous tokens and possibly, a feature for the distance between the heads

and their dependents. We intend to use a parser with differently deﬁned actions in order

to emulate properly the human difﬁculty pattern for experiments in which ambiguity

facilitates processing. We consider to initiate the integration of part-of-speech tagging

into the parsing process. We will also explore the way that these improvements would

affect ambiguity resolution and the accuracy of the parser.

145

Acknowledgements

Most of the work has been completed while the author was still afﬁliated to University

of Trento and FBK-irst, Povo-Trento. We would like to acknowledge Paolo Bouquet,

Alberto Lavelli and Edward Gibson for valuable discussions on the use of the depen-

dency shift-reduce parser as a model of the human sentence processing mechanism. We

thank two anonymous reviewers for their comments on a previous draft of this paper.

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