Automatic Narrative Humor Recognition Method Using Machine Learning
and Semantic Similarity Based Punchline Detection
Rafal Rzepka, Yusuke Amaya, Motoki Yatsu and Kenji Araki
Graduate School of Information Science and Technology,
Hokkaido University, Sapporo, Japan{rzepka,motoki.yatsu,araki}@ist.hokudai.ac.jp
Abstract
In this paper we introduce our method for recognizing jokes written in Japanese language by where
the punchline is detected using WordNet. The results showed that when compared to method basedon Bayesian posterior probability baseline, the proposed system achieved 5.3 point increase in recall
and 2.6 point increase in classification accuracy.
Our work1 is the first challenge to detect humor inJapanese language and this ability can be utilized
not only for more natural reactions while perceiving user’s utterance, but also for discovering funnystories to be uttered by an agent.

1

Introduction

Humor is an important part of our lives and it is good for ourhealth increasing immunity [Bennett and Lengacher, 2009] orlowering the stress level [Berk et al., 1989]. Using it in a rightmoment, except its relaxing properties [Martin, 2010], is often treated as a manifestation of someone’s intelligence. The
same can be said about an ability of spotting a delicate joke
hidden in everyday conversations. For a machine discoveringa chance for using humor or detecting it opens new possibilities for maintaining natural flow of a conversation. This
could be useful to enhance methods aiming at finding critical points in a dialog [Kubo and Abe, 2014] or to deal with
moments, when there is a sudden need to change the topic
as the conversation fades [Montero et al., 2005]. Creating acompanion robot that can deal with humor is regarded as important but very challenging task. For that reason many AI researchers have been developing various methods for both automatic generating [Taylor and Mazlack, 2004] [Tinholt andNijholt, 2007] [Ritchie, 2005] [Strapparava et al., 2011] anddiscovering humor for human-machine interaction. The most
known research in the latter area was done by [Mihalcea andPulman, 2007] [Mihalcea et al., 2010] who introduced jokespecific features to increase efficiency of individual semantic
relatedness measures which replaced stylistic features as usedin their earlier work [Mihalcea, 2005].
1
The second author was the main contributor building the systemand perform experiments. Because he never had a chance to share
his work with the international audience before graduation, we decided to write a paper in English introducing his research.

In the case of Japanese language, there is a quite few examples of generating puns [Takizawa et al., 1997] [Dybala
et al., 2008], but there is no, to the authors’ best knowledge,
research on recognizing humorous texts. To tackle this complicated problem it would be preferable to use deep semantic analysis, however the available tools and dictionaries arestill not efficient enough, therefore we decided to use machinelearning and similarity measures to see how helpful they arein the task of telling what is funny and what is not. Suchability would be preferred in companion machines, especiallyelderly-care robots which will most probably stay with us at
homes in 2060, the year when the population of 60-years andolder people in Japan is estimated to increase from 31.4%(2010) to 46.5%2 . We believe that conversational skills ofsuch agents will be crucial to their usability and for gaining
trust, therefore both discovering a chance of joke in the utterance and finding humorous plots online for reusing it areimportant skills in a future robot toolbox. The ultimate goalof this research is use laughter when it should be used andcause laughter when a user needs it. In the following sections
we introduce what material and methods we used to create
the system and how efficient it was in empirical tests.

1.1

Type of Jokes We Used

Narrative jokes, not puns based mostly on acoustic similarities were used because we find it more challenging and useful in usual everyday conversation and depend on semantic
dependencies between words. We extracted 987 texts fromvarious sites found by a query “funny stories”. Two examples
of such humorous text are shown below.
She always asks me to ask her what she would
like to eat. So I ask her what she would like to eat.
But every time I do it, she answers “some food”.
Couple days ago a gas station guy asked me
if I want some engine antifreeze coolant. I asked
him how much it was and he answered “2000 yen
for you, our valuable customer”. So I asked how
much was the price for a standard customer. He
obviously didn’t have that in his manual so after
2
Governmental White Paper on Aging Society (2013):
http://www8.cao.go.jp/kourei/whitepaper/
w-2013/zenbun/25pdf_index.html, in Japanese, accessed April 2015.

few seconds he just said: “I think it is also 2000
yen, sir”.
For experiments, we also retrieved 1,211 tweets with
#twnovel hashtag as the non-humorous counterparts. Translation of an example is shown below.
I tweeted that I want to quit my job. “It’s OK,
do it”, somebody answered, and his profile said he
was 55. I kept tweeting “I want to quit” every day
and suddenly my father came to Tokyo. “It’s OK”
he said hugging me for the first time in 20 years.
Because most of the “funny stories” were not ranked byreaders and tweet novels could be amusing, we needed to ensure that more than one person agrees that a given story ishumorous or not for the classification. We have cleaned texts
removing duplicates, handle names, retweets and limited online jokes to shorter ones (less than 140 characters) to makeboth sets as similar as possible. We asked 11 students of ouruniversity (6 males, 5 females, 5 with background in humanities and 6 with science background). The survey task was tolabel a given text as laughable or not laughable. If more thanhalf of participants agreed, the text was categorized as humorous (438) on non-humorous (362). An example of “funnystories” text which was not considered funny is given below.
When my son was in high school he told me not
to approach him during the open house. Now hehas long hair and no job. And he’s told not to approach me when he sees me on the street.

2

Hybrid Approach for Recognizing Humor
in Text

To investigate the ability to tell humorous text from nonhumorous one, we decided to utilize two most popular classifying methods which are Support Vector Machine (SVM)[Cortes and Vapnik, 1995] and Naı̈ve Bayes [Maron, 1961]
together, i.e. adding a majority vote step. Because voting
between only two classifiers is not effective if both outputs
differ, we used one of the Naı̈ve Bayes parameter – posterior
probability – as an additional voter. This parameter describesclassification probability of each category; if it is higher thancertain threshold, it means the text contains funny elements
and it is classified as humorous and non-humorous when thevalue is lower than the threshold (Figure 1 shows learning
process and Figure 2 demonstrates how it is used further forrecognition). More details on both methods are introduced in
the next two subsection.

2.1

Classifier 1: Naı̈ve Bayes

Naive Bayes classifiers are based on applying Bayes’ theorem with strong (naı̈ve) independence assumptions between
the features. To classify text x it is needed to compare theposterior probability distributions of every category and tochoose the one with biggest biggest value. The calculation
are done by using the following formula,
T (ck , xi )0
x0 V T (ck , x )P (xi |ck ) = P(1)

Figure 1: Learning part of our system.
where P (xi |ck ) is the conditional probability of xi , V isthe set of all words, and T (ck , xi ) is the number of occurrences of word xi in category ck . According to the Bayes
theorem, we use posterior probability P (B) (probability distribution of phenomena B occurring) and P (B|A) (probability of phenomena B occurring after phenomena A), as shown
below (with a condition that P (A) > 0).
P (A|B)P (B)P (A)Classification is performed with
P (B|A) =classif y = argmaxck P (ck |x)
and then the following Bayesian theorem

(2)

(3)

P (x|ck )P (ck )
(4)
P (x)
is used, where P (ck ) is a ratio of ck category in learning set
texts and P (x|ck ) is the likelihood. Because Naı̈ve Bayes assumes conditional independency between words, the formulais
P (ck |x) =P (x|ck ) = P (x1 . . . xK |ck ) =KYP (xi |ck )

(5)

i=1

where the denominator is needed for normalizing the posterior probability distribution. This is done with
!
M
K
X
Y
P (x) =
P (cj )
P (xi |cj )
(6)
j=1

i=1

and the final formula for classifying texts with Naı̈ve Bayes
becomes as follows.
QK
P (ck ) i=1 P (xi |ck ) (7)classif y = argmaxck P
QKM
j=1 P (cj )i=1 P (xi |cj )objective function LD (λ) becomesmaxλ1 ,...,λN LD (λ) =

N
X

N

λi −

i=1

1 X
λi λj yi yj xTi xj (13)2 i,j=1

However, we assume that the set is linearly separable, essentially it is impossible to apply the method to non-linear
cases, therefore the nonlinear transformation of feature vectors is performed and linear identification is carried out intheir space. Now the original feature vector xi is transformed
by nonlinear map φ(xi ) and because the inner product of input data from Equation 13 exists, the inner product of nonlinear map φ(xi )φ(xj ) is calculated by

Figure 2: Algorithm of the proposed hybrid approach for recognizing humorous texts.

2.2

Classifier 2: Support Vector Machine

SVM is a popular non-probabilistic binary linear classifier
utilizing a set of training examples marked for belonging to
one of two categories, and an SVM training algorithm building a model to assign new examples into one of the categories.
SVM constructs a hyperplane or set of hyperplanes in a highor infinite-dimensional space and he separation is achievedby the hyperplane that has the largest distance to the nearest
training-data point of any class. This point is called functional margin and is needed to assign an example x to oneof the classes c1 or c2 . In our case example x is represented
as a feature vector xT = (x1 , x2 , . . . , xM ) made from morphological chunks (as in case of Naı̈ve Bayes, we use bag-ofword approach here). Input feature vector is used for calculating two values of discrimination function as follows:y = sign(wT x − h)(8)

As the learning set we used N feature vectors x1 , . . . , xN
and their training labels y1 , . . . , yN . If the set is linearly separable, there are parameters satisfying the following equation:yi (wT xi − h) ≥ 1(i = 1, . . . , N )(9)Two hyperplanes wT x−h = 1 and wT x−h = −1 divide1
and the task
the sets and the size of the margin becomes kwk
is to find the biggest margin.
1
L(w) = min kwk2
(10)2
After Lagrangian multipler is used for optimization, thefollowing conditions
NX

i yi = 0

(11)

i=1

0 ≤ λi ≤ C,

(i = 1, . . . , N )(12)φ(xi )T φ(xj ) = K(xi , xj )(14)
and when kernel K(xi , xj ) is also calculated, we can construct optimized nonlinear map from K(xi , xj ) without using high-dimension φ(xi )φ(xj ).
Such implicitly mapping inputs into high-dimensional feature spaces is called a kernel trick and because the kernel’s
inner product φ(xi )T φ(xj ) must be definable and calculations as simple as possible, multinomial expression kernel or
Gaussian kernel are commonly utilized. p is a parameter that
determines the order of the kernel function, σ is a parameterthat determines the spread of the kernel function.
Using the kernel trick,maxλ1 ,...,λN LD (λ) =

N
X
i=1

N

λi −

1 X
λi λj yi yj K(xi , xj )2 i,j=1(15)

y = sign

NX!
λi yi K(xi , xj ) − h

(16)

i=1

decision space parameter h is determined and acquiring
discrimination function y becomes possible without the needfor calculating w directly. Learning is performed by setting
the parameter h that properly divides the learning data andthe Equations (11)(12)(15)(16) are used. Classification is performed by deciding if the input text belongs to category c1 or
c2 . If in Equation (16 )y = 1, c1 is chosen and if y = −1,
c2 is decided. Parameters are set according to Equations (11)(12)(15)(16.

3

Experiment for Classification Accuracy

We divided experiments into three parts. In the first experiment we evaluated classification accuracy on the set of texts
which were classified in the same way by both Naı̈ve Bayesand SVM classifiers. The second test used texts that gave different outputs and it helped us to set the posterior probability
threshold for humorous ones. The probability was normalized to become 1 and we experimented with threshold valuesof 0.001, 0.1, 0.3, 0.7, 0.9 and 1 to find the best one. In thethird experiment we used the best treshold confirmed in thesecond test to compare our proposed method with the baseline
methods. In all experiments we used 10-fold cross-validation.

3.1

Experiment Results

Table 3: Hybrid vs. Baseline – overall evaluation
MethodPrecision (%) Recall (%) F-score
NB
79.0
78.3
0.786
SVM
78.1
83.5
0.807
Both (Hybrid) 79.4
84.4
0.818

We used Naı̈ve Bayes and SVM separately as the baseline
methods using all 724 texts. To measure effectiveness we weused standard precision/recall/accuracy calculations:TP(17)
TP + FPTP
recall =(18)
TP + FN
TP + TN(19)
fscore =TP + TN + FP + FN
where TP, FP, TN, FN being number of classification true
positives, false positives, true negatives and false negatives,
respectively.
The hybrid method precision was 7.5 points better and recall
12.0 points better than Naı̈ve Bayes as the baseline (see Table1). When compared to SVM, proposed method achieved 8.4
higher precision and 6.8 higher recall. In future, we want tocompare our method also with other classification methods,
but they may be more time consuming which is a big obstacle
when the method is utilized in a dialog processing system.
The second experiment was run on 162 sentences which
did not match in the first one. The precision (54.7%) andrecall (64.1%) of the classification was highest when 0.001
threshold was used (see Table 2). We set this treshold in
the third experiment and compared the proposed systemagain with baselines, achieving further improvement – better
precision and recall in both cases, Naı̈ve Bayes (0.4 points
and 6.1 points) and SVM (1.3 points and 0.9 points). The
final results are shown in Table 3.
precision =Table 1: Hybrid vs. Baseline – results comparison
Method(number of texts)
NB (724)
SVM (724)

Precision(%)

Recall(%)

F-score

79.0
78.1

78.3
83.5

0.786
0.807

If both classifiers
agreed (562)

86.5

90.3

0.883

Table 2: Accuracy of different thresholds
Threshold Precision (%) Recall(%) F-score
0.001
54.7
64.1
0.590
0.1
50.9
45.4
0.480
0.3
51.2
41.9
0.461
0.7
49.9
36.4
0.421
0.9
54.2
36.4
0.436
1
49.8
23.3
0.317
However, results of the second experiment when the 0.001
threshold was used are not too high (precision 54.7%, recall
64.1%). The case when both methods disagreed was influencing the overall evaluation, results of the third experiments

were worse than the results of the first one (7.1 points dropin precision and 5.9 points decrease in recall). One probable reason for the lower efficiency is that in the case of humor
posterior probability becomes 0 or 1 and does not differ mucheven if the threshold is set. It was obviously necessary to deal
with the disagreeing outputs.

4

Adding Punchline Detection

To deal with the problem described above, we decided to extend our system adding a punchline detection module which
could help deciding if an input is humorous or not when theclassifiers disagree. We based this idea on Incongruity Theory, which was being developed by various thinkers throughout the ages from Aristotle to Kant, but it was [Schopenhauer,1907] who shaped it philosophically. The theory says that itis the perception of something incongruous, i.e. somethingthat violates our mental patterns and expectations. This approach was successfully adopted for humor detection in English [Mihalcea et al., 2010] and we decided to take the same
approach and use knowledge-base semantic relatedness to see
how surprisingly (incongruously) different is one part of thetext when compared with another. We also used WordNet[Miller, 1995] which is widely used for calculating similarity
between texts and tested six popular methods described later.

4.1

Processing StepsAs the text is input, the system checks if it consists of morethan two sentences, if there is two or less, the posterior probability from Naı̈ve Bayes is used because the comparison between only two sentences is inefficient and in case of one is
impossible. After cleaning the sentences from curly brackets and other unnecessary symbols, morphological analysis
is performed with MeCab analyzer [Kudo, 2005] to separate words (nouns, verbs, adjectives and adverbs that exist
in Japanese WordNet). With these words the semantic similarity between two consecutive sentences is calculated. Afterthe average similarity values are obtained, the system repeats
the procedure for the next pair and this way the standard values for comparison is acquired. We utilize two types of suchvalues – the first one is an average of all sentences excluding similarity between the last sentence and its precedent, thesecond is the smallest value of all sentences also without thelast sentence precedent. In the end the set average similarity
is compared with the last and the preceding sentences similarity. If the two last sentences gave similarity lower than
the average one, the system estimates that incongruity is big
enough to label the text as humorous. If it becomes higher,
then the text is classified as non-humorous (see Figure 3).

length(21)
2∗D
where length is the shortest distance between the two concepts (using node-counting) and D is the maximum depth of
the taxonomy.
Simlch = −log

Wu & Palmer Measure
Measure proposed by [Wu and Palmer, 1994] calculates similarity by considering the depths of the two concepts alongwith the depth of the LCS (Least Common Superconcept).
The formula, shown below, means that 0 < score 6 1. The
score can never be zero because the depth of the LCS is neverzero (the depth of the root of a taxonomy is one). The scoreis one if the two input concepts are the same.
Simwup =2 ∗ depth(LCS)
depth(concept1 ) + depth(concept2 )(22)Resnik Similarity
In the method of [Resnik, 1995] is based on the hypothesis saying that two similar concepts are probably more similar when they share information content. The related value(see Equation 23) is equal to the information content (IC) of
the Least Common Subsumer (LCS) (most informative subsumer), which means that the value will always be greaterthan or equal-to zero.
Simres = IC(LCS)

Figure 3: Algorithm for classification using punchline detection.

4.2

Lin Similarity
In the calculation using LCS and IC proposed by [Lin, 1998],
the similarity value returned by the measure is a number
shown in Equation 24, where IC(concept) is the information
content of concept. The similarity value becomes greaterthan or equal-to zero and less-than or equal-to one.
Simlin =Methods for Calculating Lexical Similarities

For measuring semantic similarities we used six popularmethods described briefly below.
Path Length
Path method computes the semantic relatedness of word
senses by counting the number of nodes along the shortest
path between the senses in the ’is-a’ hierarchies of WordNet.
Since a longer path length indicate less relatedness, the relatedness value returned is the multiplicative inverse of the path
length (distance) between the two concepts: relatedness = 1/ distance (see Equation 20). If the two concepts are identical, then the distance between them is one; therefore, theirrelatedness is also 1 .
1
Simpath =1 + length(20)

Leacock & Chodorow Similarity[Leacock and Chodorow, 1998] proposed the following
method:(23)

2 ∗ IC(LCS)
IC(concept1 ) + IC(concept2 )(24)

Jiang & Conrath Similarity[Jiang and Conrath, 1997] proposed a method which returns
a similarity value calculated as follows.
1
IC(concept1 ) + IC(concept2 ) − 2 ∗ IC(LCS)(25)
The core of the idea, based on Resnik [Resnik, 1995] is toconsider the information content of lowest common subsumer(LCS) and the two compared concepts to calculate the distance between the two concepts and the distance is then used
to compute the similarity measure.

Simjcn =5

Evaluation Experiments

We performed two following experiments. The baseline in
the first one was posterior probability of Naı̈ve Bayes and theaimed to evaluate the efficiency of punchline detection. The
second one was to test if adding punchline detection module to the hybrid method was useful and the hybrid systembecame the baseline method. In both experiments the used

threshold was for posterior probability was set to 0.001, thevalue experimentally confirmed in the preliminary tests. For
validation tests we again used 10-fold cross-validation using
124 texts on which both Naı̈ve Bayes and SVM disagreed.
For the second experiment 726 previously described texts
were utilized.
Results
Table 4 shows results for classifying 124 texts achieved by
the baseline and punchline detection method. Recall wasvery high (96.9% in case of Lin) and all the semantic similarity calculation methods significantly surpassed the baseline.
Also precision appeared to be better for all methods (Leacock & Chodorow abd Lin in the top with 60.4%). F-score
was highest for Lin’s method acquiring 0.726 and it also exceeded the baseline which was 0.590. From the results we
can observe that it is better to use the average similarity as
the base, not the lowest similarity value when detecting incongruity (in the case of Lin’s method, precision increase is
4.0 points and recall increase is 16.6 points).
Table 5 shows performance results of hybrid method
equipped with punchline detection ability. The comparison
showed that, depending on semantic similarity measure, there
was a recall increase between 2.1 and 5.3 points, and precision increase between 2.0 and 2.6 points. When f-score isconsidered and all sentences average is the base, Lin’s method
surpassed the baseline by 3.8 points showing that the systemenhancement was succesfull.
Error analysis
The first observable problem was the insufficient number ofwords in the knowledge base. In erroneous classifications
there were cases where not existing in Japanese WordNet “after that” or “such” had low similarity so the incongruity was
detected where there was actually not much of a surprise. Another problem, which was obvious from the start, was the lackof deeper understanding, because not all funny stories have asurprising punchline, every sentence can be evenly amusing.

6

Conclusions and Future WorkIn this paper we presented the first trial to automatically detect humor in Japanese texts. First, we introduced a hybrid system utilizing both Support Vector Machine and Naı̈ve
Bayes classifiers which performed better than both methods
used separately. To deal with the problem of both methods
disagreement and to enable voting we decided to add a module that is capable of detecting punchlines. For this we used
six popular semantic similarity measures and discovered that
Lin’s method is most useful for our approach. We experimentally confirmed that the performance of the enhanced hybrid
system achieved better results in both precision and recall.
The experiments showed that even without deeper language
understanding algorithms, our statistical approach is able torecognize humor in Japanese texts which is important for
detecting funniness in utterances and finding humorous stories online for reusing them later in own utterances to amuse
users. In the next step we plan to improve the recognition performance, especially the precision, by modeling the humanhumor cognition and adding techniques for deeper language

Table 4: Performance comparison between punchline detection module and posterior probability.
algorithm precision (%) recall (%) F-score
NB Post
54.7
64.1
0.590
Case where last and preceding sentences similarity isomitted and whole text average similarity is the base
Path
60.3
91.7
0.708
Leacock
60.4
91.7
0.708
Wu
59.3
90.2
0.699
Resnik
60.1
94.3
0.717
Lin
60.4
96.9
0.726
Jiang
60.1
94.8
0.714
Case where last and preceding sentences similarity isomitted and whole text lowest similarity is the base
Path
58.4
88.2
0.684
Leacock
58.4
88.2
0.684
Wu
57.0
83.0
0.655
Resnik
56.4
80.3
0.642
Lin
56.4
80.3
0.642
Jiang
56.9
86.1
0.666

Table 5: Performance comparison between hybrid system
alone and hybrid system with punchline detection module.
Algorithm Precision (%) Recall (%) F-score
NB Post
79.4
84.4
0.818
Case where last and preceding sentences similarity isomitted and whole text average similarity is the base
Path
82.0
88.7
0.852
Leacock
82.0
88.7
0.851
Wu
81.8
88.4
0.850
Resnik
82.0
89.2
0.854
Lin
82.0
89.7
0.856
Jiang
81.8
89.2
0.853
Case where last and preceding sentences similarity isomitted and whole text lowest similarity is the base
Path
81.7
88.1
0.848
Leacock
81.7
88.1
0.848
Wu
81.4
87.0
0.841
Resnik
81.4
86.5
0.839
Lin
81.4
86.5
0.839
Jiang
81.4
87.6
0.844

understanding. To achieve this goal it is necessary to analyze
human joking strategies and discover further features which
could be used for automatic classification.

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