Detecting sentence boundaries in Japanese speech transcriptions
using a morphological analyzer
Sachie Tajima
Hidetsugu Nanba
Manabu OkumuraInterdisciplinary Graduate School of Graduate School ofPrecision and Intelligence
Laboratory
Science and Engineering Information Sciences Hiroshima
Tokyo Institute of Technology, Tokyo
City University,
Tokyo Institute of Technology, Tokyo
oku@pi.titech.ac.jp
tajima@lr.pi.titech.ac.jp
Hiroshimananba@its.hiroshima-cu.ac.jp

Abstract
We present a method to automatically detect sentence boundaries(SBs)in Japanese speech transcriptions. Our
method uses a Japanese morphologicalanalyzer that is based on a cost calculation and selects as the best result theone with the minimum cost. The idea
behind using a morphological analyzer
to identify candidates for SBs is thatthe analyzer outputs lower costs for better sequences of morphemes. After the
candidate SBs have been identified, theunsuitable candidates are deleted by using lexical information acquired fromthe training corpus. Our method had
a 77.24% precision, 88.00% recall, and
0.8277 F-Measure, for a corpus consisting of lecture speech transcriptions inwhich the SBs are not given.

1 Introduction
Textual information is semi-permanent and iseasier to use than speech information, which isonly accessible sequentially when it is recorded.
Therefore, for many purposes, it is convenient totranscribe speech information into textual information. Two methods are currently used for making transcriptions, manual transcription and automatic speech recognition (ASR).
Speech information is generally spoken language. Spoken language is quite different fromwritten language used to describe textual information. For instance, in written language a ‘sentence’ can be a linguistic unit, but in spoken lan-

guage, there exists no linguistic unit like ‘sentence.’ Consequently, SBs are not specified inmanual or ASR speech transcriptions.
However, if SBs can be added to transcribed
texts, the texts would be much more usable. Furthermore, SBs are required by many NLP technologies. For instance, Japanese morphologicalanalyzers and syntactic analyzers typically regard
their input as a sentence.
Since Japanese morphological analyzers regard
their input as a sentence, they tend to output incorrect results when the input is a speech transcription without SBs. For instance, if the character string ‘...tokaitearimasushidekonomoji...’ isinputted to the morphological analyzer Chasen(Matsumoto et al., 2002), the output would be ‘...
/ to / kai / te / arima / sushi / de / kono / moji / ...’ ,where ‘/’ indicates the word boundaries specified
by the morphological analyzer. The correct one
should be ‘... / to / kai / te / ari / masu / shi / de /kono / moji /...’. If a ‘kuten’ (period in English) isinserted between ‘shi’ and ‘de’, which is a correct
SB, the output would be ‘... / to / kai / te / ari /masu / shi / . / de / kono / moji / ...’, which is thecorrect result.
In this paper, we present a method to automatically detect SBs in Japanese speech transcriptions. Our method is solely based on the linguistic
information in a transcription, and it can be integrated with the method that uses prosodic (pause)
information mentioned in the next section.
The target of our system is manual transcriptions rather than ASR transcriptions, but we plan
to apply it to ASR transcriptions in the future. In
the present work, we have used the transcribedspeeches of 50 lecturers whose age and sex are
not biased (The, 2001; The, 2002), and have con-

structed a corpus of 3499 sentences in which theSBs were manually inserted.
The next section discusses work related to SB
detection. Section three describes the method of
detecting SBs by using a morphological analyzer,
and section four discusses the evaluation of our
method.

2 Related work
Despite the importance of a technology that could
detect SBs, there has been little work on the topic.
In English, Stevenson and Gaizauskas (Stevenson and Gaizauskas, 2000) have addressed the
SB detection problem by using lexical cues. In
Japanese, Shitaoka et al. (Shitaoka et al., 2002)
and Nobata et al. (Nobata et al., 2002) have done
work on SB detection.
Shitaoka et al. (Shitaoka et al., 2002) detected SBs by using the pause length in the
speech and information about words that tend
to appear just before and after SBs. Basically, the SBs are detected by using the probability P (pause inf ormation|period). However,since pauses can occur in many places in speech(Seligman et al., 1996), many incorrect insertions
occurred when they inserted kutens in all of them.
Therefore, they limited the places of kuten insertion to the places just before and after the words
such as ‘masu’, ‘masune’, ‘desu’ that tend to appear at the SBs.
Their method employs the following threepause lengths: (1)All pauses are used, (2)Thepauses longer than the average length are used,(3)Assuming that the pause length differs depending on the expression, the pauses whose length
exceeds a threshold for each expression are used.
The best performance of their method, 78.4% recall, 85.1% precision, and 0.816 F-Measure, wasobtained for (3).
Nobata et al. (Nobata et al., 2002) proposeda similar method combining pause information
with the manually created lexical patterns for detecting SBs in lecture speech transcriptions.
Our method, by contrast, detects SBs by using
only linguistic information in the transcription,and it can be integrated with Shitaoka’s prosodic
method.
Although little work has been done on SB detection, there has been work in the related field of
SB disambiguation and comma restoration. SB
disambiguation is a problem where punctuation

is provided, but the categorization of the punctuation as to whether or not it marks a SB is at
issue (Palmer and Hearst, 1994; Reynar and Ratnaparkhi, 1997). Comma restoration is, as it indicates, the task of inserting intrasentential punctuation into ASR output (Beeferman et al., 1998;
Shieber and Tao, 2003; Tooyama and Nagata,2000).

3 Proposed Method
Our method to detect SBs consists of two steps:
1. identify candidate places for inserting SBs,
2. delete unsuitable candidate places.
• delete unsuitable candidates by using
information about words that seldom
appear at a SB,• delete unsuitable candidates by using information about combinations ofwords that seldom appears at a SB.
The following subsections explain each step indetail.
3.1

Identifying candidate SBs

To identify candidate places for inserting SBs,
we use a Japanese morphological analyzer that isbased on a cost calculation and selects as the best
result the one with the minimum cost. The costis determined by learning the suitable size corpus with a tag to the alternative trigram model
which used bigram model as the base (Asahara
and Matsumoto, 2000). The idea behind using amorphological analyzer to identify candidates isthat it outputs lower costs for better sequences ofmorphemes.
Therefore, by comparing the cost of inserting a
SB with the cost of not inserting a SB, if the costis lower for inserting a boundary, we can judge
that the location is a likely candidate and the sequence of morphemes is more correctly analyzed
by the morphological analyzer.
Next, we briefly describe the costs used in theJapanese morphological analyzer and illustrate
the method of identifying candidate SBs.
3.1.1

Costs used in the morphologicalanalyzer
Cost is usually used for indicating the appropriateness of morphological analysis results, andlower cost results are preferred. A Japanese morphological analyzer usually uses a combination ofmorpheme cost (cost for words or POSs (Part of
Speech)) and connection cost (cost for two adjacent words or POSs) to calculate the appropriateness of a sequence of morphemes. The Japanesemorphological analyzer of Chasen (Matsumoto etal., 2002), which we used in our work, analyzes
the input string with the morpheme and connection costs statistically computed from the POS
tagged corpus (Asahara and Matsumoto, 2002).
Consider, for example, the following two
strings, ‘oishii masu(delicious trout)’ and ‘itashimasu(I do)’. Although the end of both strings
is ‘masu’, their POSs are different (‘NounGeneral’(NG) and ‘Auxiliary Verb-Special
MASU’(AVSM)), and their morpheme costs also
differ as follows:• The cost of the Noun- ‘masu’ is 4302,• The cost of the Auxiliary Verb- ‘masu’ is 0.
Since ‘oishii(the cost is 2545)’ is an ‘AdjectiveIndependence-Basic form’(AIB) and ‘itashi(thecost is 3217)’ is a ‘Verb-IndependenceContinuous form’(VIC) , by using the following
connection cost,• The cost of AIB + NG is 404,
• There are no connection rules for AIB +AVSM,• The cost of VIC + NG is 1567,• The cost of VIC + AVSM is 1261.
the cost for each sequence of morphemes is calculated as follows:• ‘oishii(Adjective) + masu(Noun)’: 2545 +
404 + 4302 = 7251,• ‘oishii(Adjective) + masu(Auxiliary Verb)’:
unacceptable.
– Therefore, the analysis result is‘oishii(Adjective) + masu(Noun)’.
• ‘itashi(Verb) + masu(Noun)’: 3217 + 1567 +
4302 = 9086,• ‘itashi(Verb) + masu(Auxiliary Verb)’: 3217
+ 1261 + 0 = 4478.
– Because 9086 > 4487, the analysis result is ‘itashi(Verb) + masu(AuxiliaryVerb)’.
Thus, by using costs, the morphological analyses
will be grammatically correct.

3.1.2

Illustration of the process ofidentifying candidate SBs
Whether the place between ‘shi’ and ‘de’ andthe place between ’de’ and ‘kono’ in a string‘kaitearimasu shi de kono’ can be a SB is judged
according the following procedure:
1. The morphological analysis result of‘kaitearimasushidekono’ is ‘kai(Verb) /te(Particle) / arima(Noun) / sushi(Noun) /de(Particle) / kono(Attribute)’ , and its costis 16656. To compare it with the cost of
the result including a kuten, the morpheme
cost of a kuten (200) and the minimum
connection cost for a kuten (0) are added tothe above cost; therefore, 16656 + 200 + 0 +0 = 16856 is the total cost for the sequenceof morphemes.
2. The morphological analysis result of‘kaitearimasushi.
dekono’ is ‘kai(Verb)/ te(Particle) / ari(Verb) / masu(Verb) /shi(Particle) / .(Sign) / de(Conjunction) /kono(Attribute)’ , and its cost is 14245.
3. The morphological analysis result of‘kaitearimasushide. kono’ is ‘kai(Verb) /te(Particle) / arima(Noun) / sushi(Noun) /de(Particle) / .(Sign) / kono(Attribute)’ , andits cost is 18018.
4. Because 16856 > 14245 from 1 and 2, thelatter can be considered as the better sequence of morphemes. Therefore, the place
between ‘shi’ and ‘de’ can be a candidate fora SB.
5. Because 16856 < 18018 from 1 and 3, theformer can be considered as the better sequence of morphemes. Therefore, the place
between ‘de’ and ‘kono’ cannot be a SB.
As illustrated above, by inserting a kuten between two morphemes in the input string, calculating the cost, and judging whether the placeshould be a candidate SB, we can enumerate all
the candidate SBs.
3.2
3.2.1

Deleting unsuitable candidates

Deletion using words that seldom
appear at a SB
Certain words tend to appear at the beginnings
or ends of sentences. Therefore, the candidate
places just before and after such words can beconsidered as suitable, whereas the other candidates may be unsuitable and should be deleted.

The words that tend to appear at a SB can beobtained by calculating for each word that appears just before and after the identified candidate
SBs the following ratio in the training corpus: thenumber of occurrences in which a word appears
at the correct SB to the number of occurrences inwhich the word appears at the candidate SB. The
words with higher ratios tend to appear at SB. The
sample words with higher ratios are shown in Table1.
Table 1: The sample words with which tend to
appear before and after SBs
the words which appear after SBs
deeee(324/330)(287/436) (204/524)
the words which appear before SBs
masu
ta
desu
(1015/1084) (251/394) (260/367)
By summing the values of the words just before and after each candidate SB, we can judge
whether the candidate is suitable or not. If thesum of these values does not exceed a predetermined threshold, the candidate is judged as unsuitable and deleted. The threshold was empirically set to 0.7 in this work.
3.2.2

Deletion using combinations of words
that seldom appear at a SB

Even if a word that tends to appear in a SB appears before or after the candidate SB, the candidate might still not be suitable, if the combinationof the words seldom appears at a SB.
Consider the following example. In the training corpus, the string ‘desuga’(no kuten insertion
between ‘desu’ and ’ga’ ) occurs, but the string‘desu. ga’ never occurs, although ‘desu’ tends toappear at the end of a sentence, as shown in Table1.
Therefore,
in caseof the string‘kotodesugakono’, the method in the lastsection cannot delete the unsuitable candidate
SB between ‘desu’ and ‘ga’ because the value of‘desu’ exceeds the threshold, as shown in Table1.
• The morphological analysis result of‘kotodesugakono’is‘koto(Noun)/desuga(Conjunction) / kono(Attribute)’. The total cost is 12730 + 200 + 0 + 0 =12930.

• The morphological analysis result of‘kotodesu.
gakono’ is ‘koto(Noun)/ desu(Auxiliary verb) / .(Sign) /ga(Conjunction)/ kono(Attribute)’.
The
cost is 9938.
• Because 12930 > 9938, the place between‘desu’ and ‘ga’ can be a candidate SB.
• The ratio in the last section for ‘desu’ is260/367 = 0.7084 > 0.7; therefore, whatever
the value of ‘ga’ may be, the place between‘desu’ and ‘ga’ will not be deleted as a result of using the method described in the lastsection.
To cope with the above problem, we need
another method to delete unsuitable candidate
places, i.e., one that uses the combination ofwords which seldom appears at a SB:
1. Identify in the corpus all the combination ofwords which tend to appear just before andafter a SB,
2. If the occurrence of the combination ofwords without kuten insertion exceeds thepreset threshold in the training corpus, select the combination as one that seldom appears in a SB. (The threshold was set to 10
in this work.) Furthermore, to prevent incorrect deletions, do not select the combination
which occur once or more with kuten insertion in the training corpus.
3. If the combination of words just before orafter the identified candidate SB is one that
seldom appears at a SB, the candidate isdeleted.
This method can cope with the above example;
that is, it deletes the candidate SB between ‘desu’and ‘ga’.

4 Evaluation
4.1

Evaluation measure

Precision, recall, and F-Measure were the measures used for the evaluation. They were defined
as follows: Precision is the ratio of the number ofcorrect SBs identified by the method to the number of boundaries identified by the method. Recall
is the ratio of the number of correct SBs identified by the method to the total number of correctboundaries. The F-Measure was calculated withfollowing formula:recision∗Recall
F − M easure = 2∗P
P recision+Recall

The corpus, consisting of 3499 sentences forwhich kutens were manually inserted, was divided into five parts, and the experiments used a
5-fold cross validation.
4.2

Determining the direction of identifying
the candidate boundaries

The identification of the candidate SBs using amorphological analyzer in section 3.1 can be performed in two directions: from the beginning to
the end of the input string, or vice versa. If it isperformed from the beginning, the place after the
first morpheme in the string is tried first, and theplace after the second is tried second, and so on.
1

We first conducted experiments in both directions. The F-Measures for either direction wereequal 0.8215, but the places identified sometimes
differed according to direction. Therefore, we
calculated the intersection and union of the places
for the two directions. F-Measure for the intersection is 0.8227 and the union is 0.8218.
From these results, we can conclude that theintersection of both directions yields the best performance; therefore, we will use the intersection
result hereafter.
4.3

Evaluating the effect of each method

Four experiments were conducted to investigate
the effect of each method described in section 3:
1. Use only the method to identify candidate
boundaries,
2. Use the method to identify the candidate
boundaries and the deletion method using
the words which seldom appear at a SB,
3. Use the method to identify the candidate
boundaries and the deletion method using
the combination of words which seldom appears at a SB,
4. Use all the methods.
The results are shown in Table 2. The recallof the identification method turns out to be about
82%. Since recall becomes lower by using thedeletion methods, it is desirable that the identification method have a higher recall.
Comparing 1 and 2 of Table 2, the deletion of
seldom appearing words can improve precision
1(Liu and Zong, 2003) described the same problem, andtries to resolve it by multiplying the probability for the normal and opposite directions.

Table 2: The results for each experiment
RecallPrecision
F-Measure1
0.81840.4602
0.588920.78130.85710.8174

3
0.81820.47190.5982

40.77240.88000.8227by about 40%, while lowering recall by about 4%.
A similar result can be seen by comparing 3 and4.
Comparing 1 and 3 of Table 2, the deletionof seldom appearing combinations of words canslightly improve precision with almost no lowering of recall. A similar result can be seen by comparing 2 and 4.
From these results, we can conclude that sinceboth deletion methods can raise F-Measure, they
can be considered as effective.
4.4

Error Analysis

The following are samples of errors caused by our
method:1. ‘itashimashitadeeenettono’(FN; False Negatives) 2
2. ‘mierunda. keredomokoreha’(FP; False Positives) 3
The reasons for the errors are as follows:
1. The SB between ‘ta’ and ‘de’ cannot be detected for ‘itashimashi ta de ee nettono’, because the string contains a filler ‘ee’(‘ah’in English), and the morphological analyzer
could not correctly analyze the string.
When the input string contains fillers and repairs, the morphological analyzer sometimes
analyzes the string incorrectly.
2. The place between ‘da’ and ‘keredomo’ wasincorrectly detected as a SB for ‘mierun da.
keredomo koreha’, because the combinationof the words ‘da’ and ‘keredomo’ seldom appears at a SB but the number of occurrences
is not zero; the combination was not selected
as one that seldom appears at a SB.

5 Conclusion
In this paper, we presented a method that uses
a Japanese morphological analyzer to automatically detect SBs in Japanese speech transcriptions.
23

Errors where the method misses the correct boundaries
Errors where the method incorrectly inserts boundaries

Our method could yield a 77.24% precision,
88.00% recall, and 0.8277 F-Measure for a corpus consisting of lecture speech transcriptions inwhich SBs are not given. We found that by detecting SBs with our method, the morphologicalanalysis could be performed more accurately andthe error rate of the analyzer could be reduced,
although the quantitative evaluation was not performed.
Our method could outperform Shitaoka et al.’s
method (Shitaoka et al., 2002), which uses pause
information and yields 78.4% precision, 85.1%
recall, and 0.816 F-Measure, although this assessment is somewhat subjective as the corpus
for their evaluations was different from ours.
Our method can be integrated with the methodthat uses prosodic (pause) information, and suchan integration would improve the overall performance.
As we mentioned in section 4.3, our method’s
recall was only 77.24%. A future work would
therefore be to improve the recall, which would
be possible if we had a larger training corpus inwhich SBs are manually tagged. Furthermore, wewould like to apply our method to ASR speechtranscriptions in the future.
We think our method can also be applied toEnglish if a POS tagger is used in place of theJapanese morphological analyzer.

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