INTENTION-BASED SEGMENTATION: 
HUMAN RELIABILITY AND CORRELATION WITH LINGUISTIC CUES 
Rebecca J. Passonneau 
Department of Computer Science 
Columbia University 
New York, NY 10027 
becky@cs.columbia.edu 
Abstract 
Certain spans of utterances in a discourse, referred 
to here as segments, are widely assumedto form 
coherent units. Further, the segmental structure 
of discourse has been claimed to constrain and be 
constrained by many phenomena. However, there 
is weak consensus on the nature of segments and 
the criteria for recognizing or generating them. We 
present quantitative results of a two part study us- 
ing a corpus of spontaneous, narrative monologues. 
The first part evaluates the statistical reliability of 
human segmentation of our corpus, where speaker 
intention is the segmentation criterion. We then use 
the subjects' segmentations to evaluate the corre- 
lation of discourse segmentation with three linguis- 
tic cues (referential noun phrases, cue words, and 
pauses), using information retrieval metrics. 
INTRODUCTION 
A discourse consists not simply of a linear se- 
quence of utterances, 1 hut of meaningful relations 
among the utterances. As in much of the litera- 
ture on discourse processing, we assume that cer- 
tain spans of utterances, referred to here as dis- 
course segments, form coherent units. The seg- 
mental structure of discourse has been claimed to 
constrain and be constrained by disparate phe- 
nomena: cue phrases (Hirschberg and Litman, 
1993; Gross and Sidner, 1986; Reichman, 1985; Co- 
hen, 1984); lexical cohesion (Morris and Hirst, 
1991); plans and intentions (Carberry, 1990; Lit- 
man and Allen, 1990; Gross and Sidner, 1986); 
prosody (Grosz and Hirschberg, 1992; Hirschberg 
and Gross, 1992; Hirschberg and Pierrehumbert, 
1986); reference (Webber, 1991; Gross and Sidner, 
1986; Linde, 1979); and tense (Webber, 1988; Hwang 
and Schubert, 1992; Song and Cohen, 1991). How- 
ever, there is weak consensus on the nature of seg- 
ments and the criteria for recognizing or generat- 
ing them in a natural language processing system. 
Until recently, little empirical work has been di- 
rected at establishing obje'~ively verifiable segment 
boundaries, even though this is a precondition for 
1We use the term utterance to mean a use of a sen- 
tence or other linguistic unit, whether in text or spoken 
language. 
Diane J. Litman 
AT&T Bell Laboratories 
600 Mountain Avenue 
Murray Hill, NJ 07974 
diane@research.att.com 
SEGMENT 1 
Okay. 
tsk There's ~, 
he looks like ay uh Chicano American, 
he is picking pears. 
A-nd u-m he's just picking them, 
he comes off of the ladder, 
a-nd he- u-h puts his pears into the basket. 
SEGMENT 2 
U-h a number of people are going by, 
and one is um /you know/ I don't know, 
I can't remember the first ... the first person that goes by. 
Oh. 
A u-m a man with a goat comes by. 
It see it seems to be a busy place. 
You know, 
fairly busy, 
it's out in the country, 
maybe.in u-m u-h the valley or something. 
um \[-~ goes up the ladder, A-nd 
and picks some more pears. 
Figure 1: Discourse Segment Structure 
avoiding circularity in relating segments to linguis- 
tic phenomena. We present the results of a two 
part study on the reliability of human segmenta- 
tion, and correlation with linguistic cues. We show 
that human subjects can reliably perform discourse 
segmentation using speaker intention as a criterion. 
We use the segmentations produced by our subjects 
to quantify and evaluate the correlation of discourse 
segmentation with three linguistic cues: referential 
noun phrases, cue words, and pauses. 
Figure 1 illustrates how discourse structure in- 
teracts with reference resolution in an excerpt taken 
from our corpus. The utterances of this discourse 
are grouped into two hierarchically structured seg- 
ments, with segment 2 embedded in segment 1. This 
segmental structure is crucial for determining that 
the boxed pronoun he corefers with the boxed noun 
phrase a farmer. Without the segmentation, the ref- 
erent of the underlined noun phrase a man with a 
goat is a potential referent of the pronoun because 
it is the most recent noun phrase consistent with 
the number and gender restrictions of the pronoun. 
With the segmentation analysis, a man with a goat 
is ruled out on structural grounds; this noun phrase 
occurs in segment 2, while the pronoun occurs after 
resumption of segment 1. A farmer is thus the most 
recent noun phrase that is both consistent with, and 
148 
in the relevant interpretation context of, the pro- 
noun in question. 
One problem in trying to model such dis- 
course structure effects is that segmentation has 
been observed to be rather subjective (Mann et al., 
1992; Johnson, 1985). Several researchers have be- 
gun to investigate the ability of humans to agree 
with one another on segmentation. Grosz and 
Hirschberg (Grosz and Hirschberg, 1992; Hirschberg 
and Grosz, 1992) asked subjects to structure three 
AP news stories (averaging 450 words in length) ac- 
cording to the model of Grosz and Sidner (1986). 
Subjects identified hierarchical structures of dis- 
course segments, as well as local structural features, 
using text alone as well as text and professionally 
recorded speech. Agreement ranged from 74%-95%, 
depending upon discourse feature. Hearst (1993) 
asked subjects to place boundaries between para- 
graphs of three expository texts (length 77 to 160 
sentences), to indicate topic changes. She found 
agreement greater than 80%. We present results 
of an empirical study of a large corpus of sponta- 
neous oral narratives, with a large number of poten- 
tial boundaries per narrative. Subjects were asked 
to segment transcripts using an informal notion of 
speaker intention. As we will see, we found agree- 
ment ranging from 82%-92%, with very high levels 
of statistical significance (from p = .114 x 10 -6 to 
p < .6 x 10-9). 
One of the goals of such empirical work is to 
use the results to correlate linguistic cues with dis- 
course structure. By asking subjects to segment 
discourse using a non-linguistic criterion, the corre- 
lation of linguistic devices with independently de- 
rived segments can be investigated. Grosz and 
Hirschberg (Grosz and Hirschberg, 1992; Hirschberg 
and Grosz, 1992) derived a discourse structure for 
each text in their study, by incorporating the struc- 
tural features agreed upon by all of their subjects. 
They then used statistical measures to character- 
ize these discourse structures in terms of acoustic- 
prosodic features. Morris and Hirst (1991) struc- 
tured a set of magazine texts using the theory 
of Grosz and Sidner (1986). They developed a 
lexical cohesion algorithm that used the informa- 
tion in a thesaurus to segment text, then qualita- 
tively compared their segmentations with the re- 
suits. Hearst (1993) derived a discourse structure for 
each text in her study, by incorporating the bound- 
aries agreed upon by the majority of her subjects. 
Hearst developed a lexical algorithm based on in- 
formation retrieval measurements to segment text, 
then qualitatively compared the results with the 
structures derived from her subjects, as well as with 
those produced by Morris and Hirst. Iwanska (1993) 
compares her segmentations of factual reports with 
segmentations produced using syntactic, semantic, 
and pragmatic information. We derive segmenta- 
tions from our empirical data based on the statisti- 
cM significance of the agreement among subjects, or 
boundary strength. We develop three segmentation 
algorithms, based on results in the discourse litera- 
ture. We use measures from information retrieval 
to quantify and evaluate the correlation between 
the segmentations produced by our algorithms and 
those derived from our subjects. 
RELIABILITY 
The correspondence between discourse segments 
and more abstract units of meaning is poorly under- 
stood (see (Moore and Pollack, 1992)). A number 
of alternative proposals have been presented which 
directly or indirectly relate segments to intentions 
(Grosz and Sidner, 1986), RST relations (Mann 
et al., 1992) or other semantic relations (Polanyi, 
1988). We present initial results of an investigation 
of whether naive subjects can reliably segment dis- 
course using speaker intention as a criterion. 
Our corpus consists of 20 narrative monologues 
about the same movie, taken from Chafe (1980) 
(N~14,000 words). The subjects were introductory 
psychology students at the University of Connecti- 
cut and volunteers solicited from electronic bulletin 
boards. Each narrative was segmented by 7 sub- 
jects. Subjects were instructed to identify each point 
in a narrative where the speaker had completed one 
communicative task, and began a new one. They 
were also instructed to briefly identify the speaker's 
intention associated with each segment. Intention 
was explained in common sense terms and by ex- 
ample (details in (Litman and Passonneau, 1993)). 
To simplify data collection, we did not ask sub- 
jects to identify the type of hierarchical relations 
among segments illustrated in Figure 1. In a pilot 
study we conducted, subjects found it difficult and 
time-consuming to identify non-sequential relations. 
Given that the average length of our narratives is 
700 words, this is consistent with previous findings 
(Rotondo, 1984) that non-linear segmentation is im- 
practical for naive subjects in discourses longer than 
200 words. " 
Since prosodic phrases were already marked in 
the transcripts, we restricted subjects to placing 
boundaries between prosodic phrases. In principle, 
this makes it more likely that subjects will agree 
on a given boundary than if subjects were com- 
pletely unrestricted. However, previous studies have 
shown that the smallest unit subjects use in sim- 
ilar tasks corresponds roughly to a breath group, 
prosodic phrase, or clause (Chafe, 1980; Rotondo, 
1984; Hirschberg and Grosz, 1992). Using smaller 
units would have artificially lowered the probability 
for agreement on boundaries. 
Figure 2 shows the responses of subjects at each 
potential boundary site for a portion of the excerpt 
from Figure 1. Prosodic phrases are numbered se- 
quentially, with the first field indicating prosodic 
phrases with sentence-final contours, and the second 
149 
3.3 \[.35+ \[.35\] a-nd\] he- u-h \[.3\] puts his pears into the basket. 
l 6 SUBJECTS I NP, PAUSE 
4.1 \[I.0 \[.5\] U-hi a number of people are going by, 
CUE, PAUSE 
4.2 \[.35+ and \[.35\]\] one is \[1.15 urn/ /you know/I don't know, 
4.3 I can't remember the first ... the first person that goes by. 
\[ 1 SUBJECTS \[ PAUSE 
5.1 Oh 
SUBJECTS I NP tl 
6.1 A u-m.. a man with a goat \[.2\] comes by. I 
\[2 SUBJECTS I NP, PAUSE 
7.1 \[.25\] It see it seems to be a busy place. 
PAUSE 
8.1 \[.1\] You know, 
8.2 fairly busy, 
I, suBJeCTS I 
8.3 it's out in the country, 
PAUSE 
8.4 \[.4\] maybe in u-m \[.8\] u-h the valley or something. 
\[7 SUBJECTS\[ NP, CUE, PAUSE 
9.1 \[2.95 \[.9\] A-nd um \[.25\] \[.35\]\] he goes up the ladder, 
Figure 2: Excerpt from 9, with Boundaries 
field indicating phrase-final contours. 2 Line spaces 
between prosodic phrases represent potential bound- 
ary sites. Note that a majority of subjects agreed 
on only 2 of the 11 possible boundary sites: after 3.3 
(n=6) and after 8.4 (n=7). (The symbols NP, CUE 
and PAUSE will be explained later.) 
Figure 2 typifies our results. Agreement among 
subjects was far from perfect, as shown by the pres- 
ence here of 4 boundary sites identified by only 1 or 2 
subjects. Nevertheless, as we show in the following 
sections, the degree of agreement among subjects 
is high enough to demonstrate that segments can 
be reliably identified. In the next section we dis- 
cuss the percent agreement among subjects. In the 
subsequent section we show that the frequency of 
boundary sites where a majority of subjects assign 
a boundary is highly significant. 
AGREEMENT AMONG SUBJECTS 
We measure the ability of subjects to agree with one 
another, using a figure called percent agreement. 
Percent agreement, defined in (Gale et al., 1992), 
is the ratio of observed agreements with the ma- 
jority opinion to possible agreements with the ma- 
jority opinion. Here, agreement among four, five, 
six, or seven subjects on whether or not there is a 
segment boundary between two adjacent prosodic 
phrases constitutes a majority opinion. Given a 
transcript of length n prosodic phrases, there are 
n-1 possible boundaries. The total possible agree- 
ments with the majority corresponds to the number 
of subjects times n-1. Teral observed agreements 
equals the number of times that subjects' bound- 
ary decisions agree with the majority opinion. As 
2The transcripts presented to subjects did not con- 
tain line numbering or pause information (pauses indi- 
cated here by bracketed numbers.) 
noted above, only 2 of the 11 possible boundaries 
in Figure 2 are boundaries using the majority opin- 
ion criterion. There are 77 possible agreements with 
the majority opinion, and 71 observed agreements. 
Thus, percent agreement for the excerpt as a whole 
is 71/77, or 92%. The breakdown of agreement on 
boundary and non-boundary majority opinions is 
13/14 (93%) and 58/63 (92%), respectively. 
The figures for percent agreement with the ma- 
jority opinion for all 20 narratives are shown in Ta- 
ble 1. The columns represent the narratives in our 
corpus. The first two rows give the absolute number 
of potential boundary sites in each narrative (i.e., n- 
1) followed by the corresponding percent agreement 
figure for the narrative as a whole. Percent agree- 
ment in this case averages 89% (variance ~r=.0006; 
max.=92%; min.=82%). The next two pairs of rows 
give the figures when the majority opinions are bro- 
ken down into boundary and non-boundary opin- 
ions, respectively. Non-boundaries, with an average 
percent agreement of 91% (tr=.0006; max.=95%; 
min.=84%), show greater agreement among subjects 
than boundaries, where average percent agreement 
is 73% (or= .003; max.=80%; min.=60%). This 
partly reflects the fact that non-boundaries greatly 
outnumber boundaries, an average of 89 versus 11 
majority opinions per transcript. The low variances, 
or spread around the average, show that subjects are 
also consistent with one another. 
Defining a task so as to maximize percent agree- 
ment can be difficult. The high and consistent lev- 
els of agreement for our task suggest that we have 
found a useful experimental formulation of the task 
of discourse segmentation. Furthermore, our per- 
cent agreement figures are comparable with the re- 
sults of other segmentation studies discussed above. 
While studies of other tasks have achieved stronger 
results (e.g., 96.8% in a word-sense disambiguation 
study (Gale et al., 1992)), the meaning of percent 
agreement in isolation is unclear. For example, a 
percent agreement figure of less than 90% could still 
be very meaningful if the probability of obtaining 
such a figure is low. In the next section we demon- 
strate the significance of our findings. 
STATISTICAL SIGNIFICANCE 
We represent the segmentation data for each narra- 
tive as an { x j matrix of height i=7 subjects and 
width j=n-1. The value in each cell ci,j is a one if the 
ith subject assigned a boundary at site j, and a zero 
if they did not. We use Cochran's test (Cochran, 
1950) to evaluate significance of differences across 
columns in the matrix. 3 
Cochran's test assumes that the number of Is 
within a single row of the matrix is fixed by ob- 
servation, and that the totals across rows can vary. 
Here a row total corresponds to the total number 
3We thank Julia Hirschberg for suggesting this test. 
150 
Narrative 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 
All Opinions 138 121 55 63 69 83 90 50 96 195 110 160 108 113 112 46 151 85 94 56 
Al~reement 87 82 91 89 89 90 90 90 90 88 92 90 91 89 85 89 92 91 91 86 
Boundary 21 16 7 10 6 5 11 5 8 22 13 17 9 11 8 7 15 11 10 6 
Agreement 74 70 76 77 60 80 79 69 75 70 74 75 73 71 68 73 77 71 80 74 
Non-Boundary 
% Agreement 
117 105 48 53 63 78 79 45 88 173 97 143 99 102 104 39 136 74 84 50 
89 84 93 91 92 91 92 92 92 90 95 91 93 91 87 92 93 94 93 88 
Table 1: Percent Agreement with the Majority Opinion 
of boundaries assigned by subject i. In the case of 
narrative 9 (j=96), one of the subjects assigned 8 
boundaries. The probability of a 1 in any of the j 
cells of the row is thus 8/96, with (9s6) ways for the 
8 boundaries to be distributed. Taking this into ac- 
count for each row, Cochran's test evaluates the null 
hypothesis that the number of ls in a column, here 
the total number of subjects assigning a boundary 
at the jth site, is randomly distributed. Where the 
row totals are ui, the column totals are Tj, and the 
average column total is T, the statistic is given by: 
Q approximates the X 2 distribution with j-1 de- 
grees of freedom (Cochran, 1950). Our results indi- 
cate that the agreement among subjects is extremely 
highly significant. That is, the number of 0s or ls in 
certain columns is much greater than would be ex- 
pected by chance. For the 20 narratives, the prob- 
abilities of the observed distributions range from 
p=.ll4x 10 -6 top<,6x 10 -9 . 
The percent agreement analysis classified all the 
potential boundary sites into two classes, boundaries 
versus non-boundaries, depending on how the ma- 
jority of subjects responded. This is justified by 
further analysis of Q. As noted in the preceding sec- 
tion, the proportion of non-boundaries agreed upon 
by most subjects (i.e., where 0 <Tj < 3) is higher 
than the proportion of boundaries they agree on 
(4 < Tj < 7). That agreement on non-boundaries 
is more probable suggests that the significance of Q 
owes most to the cases where columns have a ma- 
jority of l's. This assumption is borne out when Q 
is partitioned into distinct components for each pos- 
sible value of Tj (0 to 7), based on partioning the 
sum of squares in the numerator of Q into distinct 
samples (Cochran, 1950). We find that Qj is signif- 
icant for each distinct Tj > 4 across all narratives. 
For Tj=4, .0002 < p < .30 x 10-s; probabilities 
become more signfficant for higher levels of Tj, and 
the converse. At Tj=3, p is sometimes above our 
significance level of .01, depending on the narrative. 
DISCUSSION OF RESULTS 
We have shown that an atheoretical notion of 
speaker intention is understood sufficiently uni- 
formly by naive subjects to yield significant agree- 
ment across subjects on segment boundaries in a 
corpus of oral narratives. We obtained high levels of 
percent agreement on boundaries as well as on non- 
boundaries. Because the average narrative length is 
100 prosodic phrases and boundaries are relatively 
infrequent (average boundary frequency=16%), per- 
cent agreement among ? subjects (row one in Ta- 
ble 1) is largely determined by percent agreement 
on non-boundaries (row three). Thus, total percent 
agreement could be very high, even if subjects did 
not agree on any boundaries. However, our results 
show that percent agreement on boundaries is not 
only high (row two), but also statistically significant. 
We have shown that boundaries agreed on by at 
least 4 subjects are very unlikely to be the result of 
chance. Rather, they most likely reflect the validity 
of the notion of segment as defined here. In Figure 
2, 6 of the 11 possible boundary sites were identi- 
fied by at least 1 subject. Of these, only two were 
identified by a majority of subjects. If we take these 
two boundaries, appearing after prosodic phrases 3.3 
and 8.4, to be statistically validated, we arrive at a 
linear version of the segmentation used in Figure 1. 
In the next section we evaluate how well statistically 
validated boundaries correlate with the distribution 
of linguistic cues. 
CORRELATION 
In this section we present and evaluate three dis- 
course segmentation algorithms, each based on the 
use of a single linguistic cue: referential noun 
phrases (NPs), cue words, and pauses. 4 While 
the discourse effects of these and other linguistic 
phenomena have been discussed in the literature, 
there has been little work on examining the use of 
such effects for recognizing or generating segment 
boundaries, s or on evaluating the comparative util- 
ity of different phenomena for these tasks. The algo- 
rithms reported here were developed based on ideas 
in the literature, then evaluated on a representative 
set of 10 narratives. Our results allow us to directly 
compare the performance of the three algorithms, to 
understand the utility of the individual knowledge 
sources. 
We have not yet attempted to create compre- 
hensive algorithms that would incorporate all pos- 
sible relevant features. In subsequent phases of our 
work, we will tune the algorithms by adding and 
4The input to each algorithm is a discourse tran- 
scription labeled with prosodic phrases. In addition, 
for the NP algorithm, noun phrases need to be labeled 
with anaphoric relations. The pause algorithm requires 
pauses to be noted. 
SA notable exception is the literature on pauses. 
151 
Subjects Al~orithm 
Boundary Non-Boundary 
Boundary a b 
Non-Boundary c d 
Recall Precision Fallout Error a/(a+c) a/(a+b) b/(b+d) (b+c)/(a+b+c+d) 
Table 2: Evaluation Metrics 
refining features, using the initial 10 narratives as 
a training set. Final evaluation will be on a test 
set corresponding to the 10 remaining narratives. 
The initial results reported here will provide us with 
a baseline for quantifying improvements resulting 
from distinct modifications to the algorithms. 
We use metrics from the area of information 
retrieval to evaluate the performance of our algo- 
rithms. The correlation between the boundaries 
produced by an algorithm and those independently 
derived from our subjects can be represented as a 
matrix, as shown in Table 2. The value a (in cell 
cz,1) represents the number . of potential boundaries 
identified by both the algorithm and the subjects, b 
the number identified by the algorithm but not the 
subjects, c the number identified by the subjects but 
not the algorithm, and d the number neither the al- 
gorithm nor the subjects identified. Table 2 also 
shows the definition of the four evaluation metrics 
in terms of these values. Recall errors represent the 
false rejection of a boundary, while precision errors 
represent the false acceptance of a boundary. An 
algorithm with perfect performance segments a dis- 
course by placing a boundary at all and only those 
locations with a subject boundary. Such an algo- 
rithm has 100% recall and precision, and 0% fallout 
and error. 
For each narrative, our human segmentation 
data provides us with a set of boundaries classified 
by 7 levels of subject strength: (1 < T/ < 7). 
That is, boundaries of strength 7 are the set of pos- 
sible boundaries identified by all 7 subjects. As a 
baseline for examining the performance of our algo- 
rithms, we compare the boundaries produced by the 
algorithms to boundaries of strength ~ >_ 4. These 
are the statistically validated boundaries discussed 
above, i.e., those boundari.~,,~ identified by 4 or more 
subjects. Note that recall for ~ > 4 corresponds 
to percent agreement for boundaries. We also ex- 
amine the evaluation metrics for each algorithm, 
cross-classified by the individual levels of boundary 
strength. 
REFERENTIAL NOUN PHRASES 
Our procedure for encoding the input to the re- 
ferring expression algorithm takes 4 factors into 
account, as documented in (Passonneau, 1993a). 
Briefly, we construct a 4-tuple for each referential 
NP: <FIC, NP, i, I>. FIC is clause location, NP 
is surface form, i is referential identity, and I is a 
set of inferential relations. Clause location is de- 
25 16.1 You could hear the bicycler2, 
16.2 wheelsls going round. 
CODING <25, wheels, 13, (13 rl 12)> 
Figure 3: Sample Coding (from Narrative 4) 
termined by sequentially assigning distinct indices 
to each functionally independent clause (FIC); an 
FIC is roughly equivalent to a tensed clause that is 
neither a verb argument nor a restrictive relative. 
Figure 3 illustrates the coding of an NP, wheels. 
It's location is FIC number 25. The surface form is 
the string wheels. The wheels are new to the dis- 
course, so the referential index 13 is new. The infer- 
ential relation (13 rl 12) indicates that the wheels 
entity is related to the bicycle entity (index 12) by 
a part/whole relation. 6 
The input to the segmentation algorithm is a 
list of 4-tuples representing all the referential NPs 
in a narrative. The output is a set of boundaries 
B, represented as ordered pairs of adjacent clauses: 
(FIC,,FIC,+I). Before describing how boundaries 
are assigned, we explain that the potential bound- 
ary locations for the algorithm, between each FIC, 
differ from the potential boundary locations for the 
human study, between each prosodic phrase. Cases 
where multiple prosodic phrases map to one FIC, 
as in Figure 3, simply reflect the use of additional 
linguistic features to reject certain boundary sites, 
e.g., (16.1,16.2). However, the algorithm has the 
potential to assign multiple boundaries between ad- 
jacent prosodic phrases. The example shown in Fig- 
ure 4 has one boundary site available to the human 
subjects, between 3.1 and 3.2. Because 3.1 consists 
of multiple FICs (6 and 7) the algorithm can and 
does assign 2 boundaries here: (6,7) and (7,8). To 
normalize the algorithm output, we reduce multiple 
boundaries at a boundary site to one, here (7,8). A 
total of 5 boundaries are eliminated in 3 of the 10 
test narratives (out of 213 in all 10). All the re- 
maining boundaries (here (3.1,3.2)) fall into class b 
of Table 2. 
The algorithm operates on the principle that if 
an NP in the current FIC provides a referential link 
to the current segment, the current segment contin- 
ues. However, NPs and pronouns are treated differ- 
ently based on the notion of focus (cf. (Passonneau, 
1993a). A third person definite pronoun provides a 
referential link if its index occurs anywhere in the 
current segment. Any other NP type provides a ref- 
erential link if its index occurs in the immediately 
preceding FIC. 
The symbol NP in Figure 2 indicates bound- 
aries assigned by the algorithm. Boundary (3.3,4.1) 
is assigned because the sole NP in 4.1, a number of 
people, refers to a new entity, one that cannot be in- 
ferred from any entity mentioned in 3.3. Boundary 
6We use 5 inferrability relations (Passonneau, 1993a). 
Since there is a phrase boundary between the bicycle and 
wheels, we do not take bicycle to modify wheels. 
152 
6 3.1 A-nd he's not ... paying all that much attention 
NP BOUNDARY 
7 because you know the pears fall, 
NP BOUNDARY (no subjects) 
8 3.2 and he doesn't really notice, 
Figure 4: Multiple FICs in One Prosodic Phrase 
FORALL FIC,`,I < n < last 
IF CD,` n CD,`_I ¢ STHENCDs = CDs t9 CD,~ 
% (COREFERENTIAL LINK TO NP IN FIC,,_ 1) 
ELSE IFF,, n CD,,_ 1 ~ ~THEN CDs = CDs U CD,` 
% (INFERENTIAL LINK TO NP IN FIC,`_I) 
ELSE IF PRO,, n CDs ~ STHEN CDs = CDs U CD,` 
% (DEFINITE PRONOUN LINK TO SEGMENT) 
ELSE B = B t9 {(FIC,`_\],FIC,`)} 
% (IF NO LINK, ADD A BOUNDARY) 
Figure 5: Referential NP Algorithm 
(8.4,9.1) results from the following facts about the 
NPs in 9.1: 1) the full NP the ladder is not referred 
to implicitly or explicitly in 8.4, 2) the third person 
pronoun he refers to an entity, the farmer, that was 
last mentioned in 3.3, and 3 NP boundaries have 
been assigned since then. If the farmer had been re- 
ferred to anywhere in 7.1 through 8.4, no boundary 
would be assigned at (8.4,9.1). 
Figure 5 illustrates the three decision points of 
the algorithm. FIC,* is the current clause (at lo- 
cation n); CD, is the set of all indices for NPs in 
FIC,; F, is the set of entities that are inferrentially 
linked to entities in CDn; PRO,, is the subset of CD, 
where NP is a third person definite pronoun; CDn-1 
is the contextual domain for the previous FIC, and 
CDs is the contextual domain for the current seg- 
ment. FIC,* continues the current segment if it is 
anaphorically linked to the preceding clause 1) by a 
coreferential NP, or 2) by an inferential relation, or 
3) if a third person definite pronoun in FIC,* refers 
to an entity in the current segment. If no boundary 
is added, CDs is updated with CDn. If all 3 tests 
fail, FICn is determined to begin a new segment, 
and (FICn_I,FICn) is added to B. 
Table 3 shows the average performance of 
the referring expression algorithm (row labelled 
NP) on the 4 measures we use here. Recall 
is .66 (a=.068; max=l; min=.25), precision is 
.25 (a=.013; max=.44; min=.09), fallout is .16 
(~r=.004) and error rate is 0.17 (or=.005). Note 
that the error rate and fallout, which in a sense 
are more sensitive measures of inaccuracy, are both 
much lower than the precision and have very low 
variance. Both recall and precision have a relatively 
high variance. 
CUE WORDS 
Cue words (e.g., "now") are words that are some- 
times used to explicitly signal the structure of a 
discourse. We develop a b,'~eline segmentation al- 
gorithm based on cue words, using a simplification 
of one of the features shown by Hirschberg and Lit- 
man (1993) to identify discourse usages of cue words. 
Hirschberg and Litman (1993) examine a large set 
of cue words proposed in the literature and show 
that certain prosodic and structural features, in- 
cluding a position of first in prosodic phrase, are 
highly correlated with the discourse uses of these 
words. The input to our lower bound cue word al- 
gorithm is a sequential list of the prosodic phrases 
constituting a given narrative, the same input our 
subjects received. The output is a set of bound- 
aries B, represented as ordered pairs of adjacent 
phrases (P,,P,*+I), such that the first item in P,*+I 
is a member of the set of cue words summarized in 
Hirschberg and Litman (1993). That is, if a cue 
word occurs at the beginning of a prosodic phrase, 
the usage is assumed to be discourse and thus the 
phrase is taken to be the beginning of a new seg- 
ment. Figure 2 shows 2 boundaries (CUE) assigned 
by the algorithm, both due to and. 
Table 3 shows the average performance of the 
cue word algorithm for statistically validated bound- 
aries. Recall is 72% (cr=.027; max=.88; min=.40), 
precision is 15% (or=.003; max=.23; min=.04), fall- 
out is 53% (a=.006) and error is 50% (~=.005). 
While recall is quite comparable to human perfor- 
mance (row 4 of the table), the precision is low while 
fallout and error are quite high. Precision, fallout 
and error have much lower variance, however. 
PAUSES 
Grosz and Hirschberg (Grosz and Hirschberg, 1992; 
Hirschberg and Grosz, 1992) found that in a cor- 
pus of recordings of AP news texts, phrases be- 
ginning discourse segments are correlated with du- 
ration of preceding pauses, while phrases ending 
discourse segments are correlated with subsequent 
pauses. We use a simplification of these results to 
develop a baseline algorithm for identifying bound- 
aries in our corpus using pauses. The input to our 
pause segmentation algorithm is a sequential list of 
all prosodic phrases constituting a given narrative, 
with pauses (and their durations) noted. The out- 
put is a set of boundaries B, represented as ordered 
pairs of adjacent phrases (P,*,Pn+I), such that there 
is a pause between Pn and Pn+l- Unlike Grosz and 
Hirschberg, we do not currently take phrase dura- 
tion into account. In addition, since our segmenta- 
tion task is not hierarchical, we do not note whether 
phrases begin, end, suspend, or resume segments. 
Figure 2 shows boundaries (PAUSE) assigned by the 
algorithm. 
Table 3 shows the average performance of the 
pause algorithm for statistically validated bound- 
aries. Recall is 92% (~=.008; max=l; min=.73), 
precision is 18% (~=.002; max=.25; min=.09), fall- 
out is 54% (a=.004), and error is 49% (a=.004). 
Our algorithm thus performs with recall higher than 
human performance. However, precision is low, 
153 
Recall Precision Fallout 
NP .66 .25 .16 
Cue .72 .15 .53 
Pause .92 .18 .54 
Humans .74 .55 .09 
Table 3: Evaluation for Tj > 4 
Error 
.17 
.50 
.49 
.11 
Tj 1 2 3 4 5 6 7 
NPs 
f Precision .18 .26 .15 .02 .15 .07 .06 Cues 
I "1 °°1 Precision .17 .09 .08 .07 .04 .03 .02 Pauses 
Precision .18 .10 .08 .06 .06 .04 .03 
Humans 
t "1 Precision .14 .14 .17 .15 .15 .13 .14 
Table 4: Variation with Boundary Strength 
while both fallout and error are quite high. 
DISCUSSION OF RESULTS 
In order to evaluate the performance measures for 
the algorithms, it is important to understand how 
individual humans perform on all 4 measures. Row 
4 of Table 3 reports the average individual perfor- 
mance for the 70 subjects on the 10 narratives. The 
average recall for humans is .74 (~=.038), ~ and the 
average precision is .55 (a=.027), much lower than 
the ideal scores of 1. The fallout and error rates of 
.09 (~=.004) and .11 (a=.003) more closely approx- 
imate the ideal scores of 0. The low recall and preci- 
sion reflect the considerable variation in the number 
of boundaries subjects assign, as well as the imper- 
fect percent agreement (Table 1). 
To compare algorithms, we must take into ac- 
count the dimensions along which they differ apart 
from the different cues. For example, the referring 
expression algorithm (RA) differs markedly from the 
pause and cue algorithms (PA, CA) in using more 
knowledge. CA and PA depend only on the ability 
to identify boundary sites, potential cue words and 
pause locations while RA relies on 4 features of NPs 
to make 3 different tests (Figure 5). Unsurprisingly, 
RA performs most like humans. For both CA and 
PA, the recall is relatively high, but the precision 
is very low, and the fallout and error rate are both 
very high. For lZA, recall and precision are not as 
different, precision is higher than CA and PA, and 
fallout and error rate are both relatively low. 
A second dimension to consider in comparing 
7Human recall is equivalent to percent agreement for 
boundaries. However, the average shown here represents 
only 10 narratives, while the average from Table 1 rep- 
resents all 20. 
performance is that humans and RA assign bound- 
aries based on a global criterion, in contrast to CA 
and PA. Subjects typically use a relatively gross 
level of speaker intention. By default, RA assumes 
that the current segment continues, and assigns a 
boundary under relatively narrow criteria. However, 
CA and PA rely on cues that are relevant at the local 
as well as the global level, and consequently assign 
boundaries more often. This leads to a preponder- 
ance of cases where PA and CA propose a boundary 
but where a majority of humans did not, category 
b from Table 2. High b lowers precision, reflected in 
the low precision for CA and PA. 
We are optimistic that all three algorithms can 
be improved, for example, by discriminating among 
types of pauses, types of cue words, and features of 
referential NPs. We have enhanced RA with cer- 
tain grammatical role features following (Passon- 
neau, 1993b). In a preliminary experiment using 
boundaries from our first set of subjects (4 per nar- 
rative instead of 7), this increased both recall and 
precision by ,~ 10%. 
The statistical results validate boundaries 
agreed on by a majority of subjects, but do not 
thereby invalidate boundaries proposed by only 1-3 
subjects. We evaluate how performance varies with 
boundary strength (1 _< 7) _< 7). Table 4 shows 
recall and precision of RA, PA, CA and humans 
when boundaries are broken down into those identi- 
fied by exactly 1 subject, exactly 2, and so on up to 
7. 8 There is a strong tendency for recall to increase 
and precision to decrease as boundary strength in- 
creases. We take this as evidence that the presence 
of a boundary is not a binary decision; rather, that 
boundaries vary in perceptual salience. 
CONCLUSION 
We have shown that human subjects can reliably 
perform linear discourse segmentation in a corpus 
of transcripts of spoken narratives, using an infor- 
mal notion of speaker intention. We found that per- 
cent agreement with the segmentations produced by 
the majority of subjects ranged from 82%-92%, with 
an average across all narratives of 89% (~=.0006). 
We found that these agreement results were highly 
significant, with probabilities of randomly achiev- 
ing our findings ranging from p = .114 x 10 -6 to 
p < .6 x 10 -9. 
We have investigated the correlation of our 
intention-based discourse segmentations with refer- 
ential noun phrases, cue words, and pauses. We de- 
veloped segmentation algorithms based on the use of 
each of these linguistic cues, and quantitatively eval- 
uated their performance in identifying the statisti- 
cally validated boundaries independently produced 
by our subjects. We found that compared to hu- 
man performance, the recall of the three algorithms 
SFallout and error rate do not vary much across T i. 
154 
was comparable, the precision was much lower, and 
the fallout and error of only the noun phrase algo- 
rithm was comparable. We also found a tendency 
for recall to increase and precision to decrease with 
exact boundary strength, suggesting that the cogni- 
tive salience of boundaries is graded. 
While our initial results are promising, there is 
certainly room for improvement. In future work on 
our data, we will attempt to maximize the corre- 
lation of our segmentations with linguistic cues by 
improving the performance of our individual algo- 
rithms, and by investigating ways to combine our 
algorithms (cf. Grosz and Hirschberg (1992)). We 
will also explore the use of alternative evaluation 
metrics (e.g. string matching) to support close as 
well as exact correlation. 
ACKNOWLEDGMENTS 
The authors wish to thank W. Chafe, K. Church, J. 
DuBois, B. Gale, V. Hatzivassiloglou, M. Hearst, J. 
Hirschberg, J. Klavans, D. Lewis, E. Levy, K. McK- 
eown, E. Siegel, and anonymous reviewers for helpful 
comments, references and resources. Both authors' work 
was partially supported by DARPA and ONR under 
contract N00014-89-J-1782; Passonneau was also partly 
supported by NSF grant IRI-91-13064. 

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