Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I,
IMECS 2014, March 12 - 14, 2014, Hong Kong

Extraction of Naming Concepts
Based on Modifiers in Recipe Titles
Akiho Tachibana, Shoko Wakamiya, Hidetsugu Nanba and Kazutoshi Sumiya

Abstract—On user-generated recipe-sharing sites such as
Rakuten recipe, various modifiers such as “Kid-friendly” and“Simple” are often used in the titles of the recipes to signify
thier characteristics. Although a modifier is used in a numberof recipes’ titles, the underlying concepts utilized vary. In thispaper, we propose a method that extracts Naming Concepts
for recipes based on modifiers in their titles. Specifically, we
obtain typical ingredients and cooking utensils by summarizing
the recipes for a dish to extract the differences between theelements of recipes and the typical elements in terms of addition,
deletion and exchangeability and extract additional informationfrom procedures. Then, we identify Naming Concepts for therecipes by extracting feature patterns based on the differences
extracted and grouping them on the basis of the patterns. We
also present a system that provides recipes with granted Naming
Concepts for readers.
Index Terms—Modifiers, Cooking Recipe, Naming Concepts.

I. I NTRODUCTIONOOKING is one of the most important creative activities in daily life. Nowadays, we can obtain large
numbers of recipes from cooking websites. For example,
Rakuten recipe [1] provides over 740,000 user-generated
recipes written in Japanese. COOKPAD [2], another famous
cooking website, provides more than 1,600,000 recipes.
Although these websites have recipes that meet a wide
variety of users’ demands, they are difficult to find because
numerous recipes for any particular dish are available on
the sites. For example, when we searched for recipes on the
Rakuten recipe website using the query “carbonara,” we were
presented with more than 1,300 different recipes. Thus, inorder to find recipes that meets users’ demands, invariably,
clear distinctions must be made among recipes. In this paper,
we propose a method called to extract “Naming Concept,”
which can identify the features of each recipe.
Each recipe on the Rakuten recipe website comprises atitle, dish categories, an ingredient list, and a procedure thatgives step-by-step instructions on how to cook the dish. Here,recipe titles are typically represented in the form “modifier+ dish name.” For example, in the two titles, “Simple! carbonara” and “kid-friendly omelette rice,” “Simple!” and “kidfriendly” are modifiers and “carbonara” and “omelette rice”
are dish names. The modifiers are assigned after considering
the features of each recipe. In addition, the same modifier
might be used in different ways. Fig. 1 shows Naming
Concepts for recipes whose titles include the modifier “Kidfriendly.” In the figure, there are three types of Naming

C

Manuscript received January 8, 2014; revised January 30, 2014.
A. Tachibana, S. Wakamiya, and K. Sumiya: School of Human Science and Environment, University of Hyogo, (e-mail : {nc10x087@st,
wakamiya@, sumiya@}shse.u-hyogo.ac.jp).
H. Nanba: Graduate School of Information Sciences, Hiroshima City
University, (e-mail : nanba@hiroshima-cu.ac.jp)

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

Fig. 1.

A modifier of recipes based on Naming Concepts

Concepts: “Kid-friendly” is used in the title of a recipe in
the first type of Naming Concept because soy milk, whichis considered to be preferred by many children, is used
as an ingredient in carbonara instead of garlic and milk.
In the second type of Naming Concept, the same modifier
is used from a procedure in each recipe because a mixer
is additionally used to mince the ingredients and enable
children to eat without difficulty.
In this paper, we propose a method that extracts Naming
Concepts for recipes by identifying the characteristic ingredients, cooking utensils, and procedure in each recipe using
the following four steps: 1) extracting the typical ingredientsand cooking utensils for the dish, 2) extracting the differences
between the typical elements of the dish and the elementsof a recipe for the same dish, 3) extracting tips as additional
information, 4) grouping recipes with the same modifier byfeature patterns of the differences.
The remainder of this paper is organized as follows:
Section II discusses related work. Section III presents ourprocedure for extracting Naming Concepts from recipes indetail. Section IV shows the Naming Concepts for somerecipes and discusses experimental results obtained. Finally,
we conclude this paper and outline future work in Section V.
II. R ELATED W ORK
Ueda et al. [3] proposed a method that recommends
personalized recipes by measuring each user’s food preference based on ingredients extracted from the user’s recipebrowsing and cooking history.

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Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I,
IMECS 2014, March 12 - 14, 2014, Hong Kong
Tsukuda et al. [4] proposed a method that enables users tobrowse from the current recipe to a desired recipe by adding
one element into it or deleting one element from it. However,they considered only element addition and deletion, whereas,
we consider both those factors along with element exchangeability. Tsukuda et al. [5] also analyzed the typicality ofan object from two viewpoints: target of analysis and typeof typicality. By contrast, we extract typical elements andidentify differences by comparing the elements of a particular
recipe with the typical elements used for the dish. Yamakata
et al. [6] proposed a method that creates a typical cooking
procedure from multiple recipes by converting each recipe
text into recipe trees and by integrating them. Then, they
extract features of each recipe by comparing with the typicalone. Although this work is similar to ours in terms of its
attempt to define typicality and to extract recipe features on
the basis of typicality, our work differs in that our aim is to
extract recipe features based on modifiers.
Approaches focusing on modifiers include the method
proposed by Takahashi et al. [7] to measure relevancy
between a web text and modifiers in its title by extracting
suitable words and conflicting words. The method determines
whether modifiers are relevant to the contents or not by measuring information credibility. By contrast, our assumption isthat recipes’ Naming Concepts based on modifiers in theirtitles can be interpreted from multiple perspectives.
Chung [8] proposed an efficient method that finds related
words in a recipe domain using a data structure. Interestingly,
their investigations revealed that people usually write themain ingredient in the first position of the ingredients list ofeach recipe and that such an ingredient is strongly related
to the categories to which the recipes belong. Nanba et
al. [9] constructed a recipe ontology based on Chung’s
method [8] and distributional similarity [10][11], which theyused for multi-recipe summarization. We utilize this ontology
to extract Naming Concepts.
III. E XTRACTION OF NAMING C ONCEPTS BASED ON
M ODIFIERS IN R ECIPE T ITLES
A. Our Proposed Approach
In this work, we define Naming Concepts as featuresof recipes that present concepts of modifiers. We assume
that a Naming Concept can be extracted by considering thedifferences in the various recipes for a dish and the patternsof differences in the recipes of a modifier. On the rightside of Fig. 2, the arrows indicate the differences between
typical elements of a dish and the elements of each recipe.
For example, in the analysis focused on the recipe withthe title “Kid-friendly carbonara!!,” we can extract atypicalingredients and cooking utensils by analyzing the recipes for“carbonara.” Focusing on the modifiers of the recipes, we
extract the elements that can possibly signify the featuresof the recipes such as deletion of ingredients or exchangeof cooking utensils. On the left side of Fig. 2, we extract
the pattern of differences between a set of recipes basedon a modifier. Consequently, our proposed method extracts
Naming Concepts by extracting elements that are typicallyused in the dish from recipes for the same dish, extracting theelements that are different from the typical elements in each
recipe, extracting additional information from the procedures,and grouping the recipes using feature patterns.

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

Modifier

Dish“Kid-friendly”“Carbonara”“Kid-friendly
curry”“Very Kidfriendly! soup”“Kid-friendly
hamburger
steak”“Kid-friendly
gratin”“Kid-friendly
pizza♡”“Kid-friendly♡
Carbonara”“Kid-friendly
Carbonara!!”“Kid-friendly
Carbonara”“Authentic♫
Carbonara”

Typicalelements“Carbonara
for adults”“Kid-friendly
curry!!”“Kid-friendly♡
pilaf”“Japanese
Carbonara”

Fig. 2. Extracting typical elements and Naming Concepts based on relations
between modifier and dishWe extract Naming Concepts as follows:
1) Extracting elements that are typical in the recipes2) Extracting the differences of recipes3) Extracting additional information
4) Grouping recipes based on feature patterns
The steps used to extract Naming Concepts are described in
Sections III-B – III-E, respectively.
B. Extracting Elements that are Typical in the Recipes
In this work, R is constructed from a set of recipes
separated by dish categories. A recipe rjk belongs to a dishcategory j consisting of Mjk , Ijk , and Ujk . Mjk is a set ofmodifiers included in the title of the recipe rjk . Ijk is a setof ingredients of the recipe rjk , and Ujk is a set of cooking
utensils in the procedure of the recipe rjk . Then, M , I, andU are universal sets of modifiers, ingredients, and cooking
utensils, respectively.
R = {R1 , ...Rj , ...Rm },Rj = {rj1 , ...rjk , ...rjn }, rjk = (Mjk , Ijk , Ujk )Mjk ⊂ M, Ijk ⊂ I, Ujk ⊂ U
M = {m1 , m2 , ...}, I = {i1 , i2 , ...}, U = {u1 , u2 , ...}In this work, we assume that the Naming Concepts ofmodifiers are detected in the differences between a recipe andits typical elements. Therefore, we extract typical elementsof a recipe for a dish. One recipe’s data is input, and we
extract ingredients tj .I ′ and cooking utensils tj .U ′ . Here, tj
is a set of recipe Rj of a category that one recipe belongs to.
In this work, we consider ingredients and cooking utensils
that are frequently used as typical ingredients and cooking
utensils for a dish. Therefore, we extract ingredients andcooking utensils from a set of recipes Rj and calculate
recipe frequency (RF ) of each element. Thus, a set of typicalelements tj consists of a set of ingredients I ′ , which meansthat RF is α and over and a set of cooking utensils U ′ whichmeans that RF is β and over, as follows:
tj
tj .I ′= (I ′ , U ′ ), tj ∈ T= {il |RF (il , Rj ) > α, il ∈ Ij }tj .U ′

= {uo |RF (uo , Rj ) > β, uo ∈ Uj }IMECS 2014

Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I,
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C. Extracting the Differences of Recipes
Next, we extract the differences between elements of arecipe rjk and the typical elements tj in the category Rj towhich that recipe belongs. First, we extract a set of additional
ingredients Iadd , a set of deleted ingredients Idel , a set of
additional cooking utensils Uadd , and a set of deleted cooking
utensils Udel as differences.
Iadd= rk .I − tj .I ′

Idel
Uadd
Udel

= tj .I ′ − tk .I= rk .U − tj .U ′= tj .U ′ − tk .U

We determine the relation between the differences of theextracted ingredients and the cooking utensils, because insome cases one different element influences another different
element, or different elements are independent of each other,making it irrelevant. Next, the element included in a set of
additional elements Iadd and Uadd is represented by +, andthen in a set of deleted elements Idel and Udel is represented
by −. For example, when we compare the ingredients of arecipe with a title such as “healthy sweet and sour pork” thathas typical ingredients “healthy sweet and sour pork,” weextract differences Iadd = {chicken, bambooshoot}, Idel ={pork, liquor}. Then, we use “chicken” instead of “pork,”
so we consider that these correspond. Conversely, “bamboo
shoot” and “liquor” are only added and deleted; therefore,
they have no relation. Thus, in a scenario where an element+ is included in Iadd or Uadd and an element − is included
in Idel or Udel correspond mutually, we consider that they
have an exchangeable relation. Otherwise, we consider that
there is no relation between their differences.
In order to determine relations, we calculate the degreeof co-occurrence between different elements. In general,
when two elements + and − are exchangeable, we consider
that they do not co-occur. Therefore, a recipe that contains
element + does not contain element −, and a recipe that
contains element − does not contain element +. Thus, when
the frequency of co-occurrence is low, we consider thatelements + and − are exchangeable. Then, we pair elements+ and − and extract the pair as differences in order todetermine their relations. Consequently, we calculate thedegree of co-occurrence of the various pairs of elements.
Next, we treat element − (which is included as a typicalelement) and element − (which is included in one recipe)
as denominators. In cases where only the degree of cooccurrence based on a typical element and one recipe arelower than the threshold amount, we determine that thedifferent elements in the pair have an exchangeable relation.
Therefore, a pair comprising different elements is added to aset of pairs comprising exchangeable ingredients Iex or a setof pairs comprising exchangeable cooking utensils Uex , andthe elements that fall under Iadd and Idel , or Uadd and Udel
are deleted. Then, the exchangeability relation of the different
elements are determined and signified as “-** → +**”
using the arrow. For example, we represent the recipe called“easy carbonara” that contains “+microwave” and “-pan” incooking utensils as “-pan → +microwave.” On the other
hand, when the degree of co-occurrence of different elements
is higher than the threshold amount, we consider that thedifferent elements in the pair are independent, and therefore

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

have no relations. Thus, we determine that elements + are
additional elements and elements − are deleted elements.
D. Extracting Additional Information
We consider that recipes’ features of modifiers can beextracted from cooking procedures. For example, recipes for“Kid-friendly” may have features such as “small cut” and“easy-to-eat size.” Therefore, we extract these features byanalyzing the cooking procedures for the recipes. In order
to extract them, we use word segmentation on the cooking
procedures. Next, the cooking procedures are associated withword classes. We then extract additional information bydependency parsing.
E. Grouping Recipes based on Feature Patterns
We define the feature patterns of recipes Prjk basedon viewpoints grouped by the relations between different
elements in Section III-B. More specifically, we define thembased on a set of six viewpoints: additional ingredients Iadd ,deleted ingredients Idel , exchangeable ingredients Iex , additional cooking utensils Uadd , deleted cooking utensils Udel ,and exchangeable cooking utensils Uex . Then, to simplify,
we represent patterns using binary vectors: when the element
count is one or more, we present “1”; when the element countis zero, we present “0.”Prk = [bi(|Iadd |), bi(|Idel |), bi(|Iex |),bi(|Uadd |), bi(|Udel |), bi(|Uex |)]We group recipes that use the same modifier by the featurepatterns of viewpoints that have up to 64 (= 26 ) patterns as
Naming Concepts of modifiers.
IV. E XPERIMENT
A. Dataset
We conducted an experiment using a recipe dataset provided by Rakuten Data Release from Rakuten Institute ofTechnology. From the dataset, we selected 192 recipes whose
titles included the modifier “kid-friendly.” We then extracted
ingredients and cooking utensils as recipe elements by considering inconsistent spelling using a recipe ontology [9] that
we constructed by integrating methods for extracting related
words of a given word based on the data structure of usergenerated recipes [8] and summarizing multi-documents. In
order to extract the typical ingredients and cooking utensils
for a dish, we calculated RF (Recipe Frequency) of all therecipes used in ten recipes for the dish. In the experiment, we
set the values of thresholds α and β at 0.5. TABLE I presents
the typical elements extracted for three dishes: hamburger
steak, carbonara, and curry.
B. Result: Naming Concept Extraction
We extracted different elements by comparing the elements
in a recipe with the typical elements for that recipe. Examplesof the recipes used to extract Naming Concepts are shownin TABLE II, and examples of the elements that differ
from typical elements are shown in TABLE III. In order todetermine the relations between different elements, we madeall possible pairs of elements of + and − from the set ofdifferent elements.

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Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I,
IMECS 2014, March 12 - 14, 2014, Hong KongI

TABLE IT YPICAL ELEMENTS IN RECIPE CATEGORIESTABLE IIIE LEMENTS THAT DIFFER FROM TYPICAL ELEMENTS

hamburger steakonion
ground meatbread crumb
saltpepperegg
nutmegWorcester sauce
ketchupmilk
cooking oil

carbonara
egg
peppersalt
bacon
spaghetti
cheesewater
cooking oil
fresh creamgarlicKid-friendly,
excellent and simple
cheese inhamburger steak+butter+soy milk+wine+cheese+lettuce+tomato-nutmeg-milk-cooking oil

frying panbowl

frying pan

curry
curry powder
onion
water
rice
carrotcooking oil
saltbutter

Iadd

Idel
Uadd

U

Kid-friendly
carbonara oftuna and corn

Beans curry
can eatwith children+milk+Non-dairy creamer
+tuna
+corn

+garlic+tomato+soy bean+ground meat+soy milk-salt-bacon-fresh cream-rice-carrot-salt-butter+mixer+frying pan
-pan

+bowl

pan
Udel

TABLE VR ESULTS OF EXTRACTED DIFFERENCE ELEMENTS AND FEATURES INTABLE IIE XAMPLE OF RECIPESTHE THREE RECIPES

I

U

Kid-friendly, excellent
and simple cheese inhamburger steak
ground meatonion
butterbread crumb
soy milk
egg
salt
pepperwine
sauce
ketchupcheesetomatobowl
frying pan

Kid-friendly
carbonara oftuna and corn
spaghetti
cheeseegg
milk
Non-dairy creamer
peppercooking oil
garlic
tuna
corn
water

Beans curry
can eatwith children
oniongarlictomatosoy beanground meat
curry powder
soy milk
watercooking oilbowl
frying pan

mixer
frying pan

recipe titles

Kid-friendly, excellent
and simple cheesein hamburger steakKid-friendly carbonara
of tuna and corn

Next, we calculated the ratio of co-occurrence of each pair
+ and − in all the recipes for a dish. In the experiment, we
set the value of the threshold at 0.1. Then, when the degree ofco-occurrence was lower than the threshold, we determined
the relation of the pair to be exchangeable. Conversely, when
the degree of co-occurrence was higher than the threshold,we determined that no relation existed between the pair.
TABLE IV presents the results of the determination of
the exchangeability relation using the typical elements in
TABLE I and target recipes in TABLE II and calculating
the degree of co-occurrence. We calculated the number ofrecipes containing elements + and − as the denominator,and determined the exchangeability relation when the degreeof co-occurrence was lower than the threshold. We present
the results for the extracted differing elements and featuresin the three recipes. Then, we present the different elements
and viewpoints in TABLE V
We grouped 150 recipes into feature patterns of differences extracted from the experimental recipes, and extracted
Naming Concepts for the modifier “kid-friendly.” The recipes
were grouped into 21 patterns from the 64 patterns in
Section III-E. In TABLE VI, we present the feature patterns
grouped as more than five percent. As a result, eight patterns
included the feature Iex and five patterns included the feature
Idel .

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

Beans curry can
eat with children

different elements+wine+cheese+lettuce+tomato-cooking oil-nutmeg→ +butter-milk→ +soy milk
+milk
-salt
-fresh cream→ +Non-dairy cream-bacon→ +tuna-bowl+garlic+tomato+ground meat+soy milk
-rice
-salt
-butter-carrot→ +soy bean
+mixer-pan→ +frying pan

viewpoints

Iadd
Idel
Iex

Iadd
Idel
Iex
Udel

Iadd
Idel
Iex
Uadd
Uex

TABLE VII presents recipe titles grouped into the topthree patterns. The recipes grouped by feature patterns do
not become unbalanced because of the dishes. Therefore,
recipes for the same dish are grouped by various patterns.
C. Discussion
In our extractions, typical cooking utensils that are normally included in typical elements were not included. For
example, when cooking carbonara, a pan is typically used
to boil spaghetti. However, in the experiment, it was notincluded in the typical elements because there were few
recipes that had “pan” expressly written. When we extracted
difference elements, cooking utensils were included in the
difference elements that were normally included in typicalelements because cooking utensils were used in pictures, but
not included in the procedures. In the future, we need to

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IMECS 2014, March 12 - 14, 2014, Hong Kong
TABLE IVR ESULTS OF CALCULATION OF CONFIDENCE FOR JUDGING THE RELATION
recipe titles

pairs to determine relations

Kid-friendly, excellent
and simple cheesein hamburger steak+butter, -nutmeg+butter, -cooking oil+soy milk, -milk+wine, -milk+milk, -fresh cream+Non-dairy cream, -fresh cream+tuna, -bacon+corn, -bacon+garlic, -salt+garlic, -butter+tomato, -carrot+soy bean, -carrot+ground meat, -carrot+mixer, -pan+frying pan, -pan

Kid-friendly carbonara
of tuna and corn

Beans curry can
eat with children

TABLE VI
NAMING C ONCEPTS FOR ”K ID - FRIENDLY ”

pattern1
pattern2
pattern3
pattern4
pattern5
pattern6
pattern7
pattern8

Iadd
0
0
0
0
1
0
1
1

Idel
0
1
0
1
1
1
1
0

Iex
1
1
1
1
1
1
1
1

Uadd
0
0
0
1
0
1
0
0

Udel
0
0
1
1
0
0
1
0

Uex
0
0
0
0
0
0
0
0

degree of co-occurrence
＋
−
0.12
0.16
0.06
0.05
0.00
0.00
0.00
0.18
0.24
0.17
0.00
0.00
0.00
0.00
0.00
0.25
0.13
0.23
0.10
0.06
0.04
0.13
0.00
0.09
0.00
0.15
0.70
0.00
0.00
0.00

exchangeable
○
○

○
○

○

○

TABLE VIIR ECIPE TITLES GROUPED INTO THE TOP - THREE PATTERNS
appearance
ratio
18 %
18 %
8%
7%
6%
5%
5%
5%

improve the extraction of typical elements and determine
the exchangeability relations. Therefore, we consider that we
can make up for cooking utensils by inferring the cooking
utensils used from the actions in the procedures. For example,
we can infer that “boil” means “pan” and “fry” means “frying
pan,” and so on.
When we determined the exchangeability relation andcompared our results with the correct data, we found thata number of pairs had appropriately determined the relation.
However, with regard to cooking utensils, there are many
recipes in which only actions are written (e.g. “boil” and“fry”). Therefore, cooking utensils that would not normallydetermine the relation of exchangeability here, do so becauseof the lowness of their degree of co-occurrence, resulting
from them not being cooking utensils are not expressly
written in recipes.
In TABLE V, we consider that the Naming Concepts
for “kid-friendly” are addition, deletion, and exchange ofingredients. For example, in the recipe for carbonara in
TABLE V, we could extract the exchangeability relation inwhich “tuna” is used instead of “bacon” as the feature. Thus,
there are also many recipes that include cooking utensils
as Naming Concepts. However, there are some extracted
cooking utensil elements for which it is difficult to consider
that the elements are Naming Concepts. For example, in therecipe for curry in TABLE V, we extracted and used “fryingpan” instead of “pan.” However, it is difficult to consider
these elements as Naming Concepts. Therefore, we needto consider whether extracted elements really present theconcepts of modifiers or noise.
In the results of grouped recipes based on feature patterns

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

patternpattern 1

pattern 2

pattern 3

recipe titles
Kid-friendly! Sweet and sour pork of pork loin
Smile for children 3 kinds of hamburger steaks
Kid-friendly hamburger steaks of fish
Healthy, big and kid-friendly
hamburger steak with vegetable
Kid-friendly the curry with being grated up vegetable
Chocolate in the curry for children
Kid-friendly! Corn in the curry for children
The curry for children
The curry eat with children
Kid-friendly milk curry
The curry for children with a milk
Tomato in the sweet and sour pork kid-friendly!
The hamburger steak in Denmark! Kid-friendly!
Kid-friendly hamburger steaks of tohu
Kid-friendly hamburger steaks! Not use flour and egg
Kid-friendly hamburger steaks
with many kinds of vegetables
For children don’t like vegetables!
Hamburg steak of green pepper
Kid-friendly! Curry with pork cutlet and mushroom
Kid-friendly Cheese hamburg steak
The curry for children!
Kid-friendly sweet and sour pork with potato
Kid-friendly and soft hamburg steakKid-friendly! Hamburg steak with hijiki
Kid-friendly! Raisin in dried curry
Simple and kid-friendly curry in Keema
Kid-friendly Curry of Japanese radish and tohu
Kid-friendly mochi in the curryand extracted Naming Concepts, we found that recipes
included in the same category have various Naming Concepts
such as those listed in TABLE VII. In this paper, we extracted
Naming Concepts for “kid-friendly.” However, we need to
extract more Naming Concepts for various modifiers. By
comparing the difference between modifiers, we could analyze the relations between modifiers. For example, when thetrends for Naming Concepts are similar between modifiers,
they have similar relations.
Fig. 3 depicts a system that presents the Naming Conceptsof recipes based on our method. In this system, when auser searches for recipes containing modifiers such as “Kidfriendly” and “Simple”, it is difficult to understand thefeatures of the recipes because a list of search results often
show only titles and pictures for the recipes. Therefore, if

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Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I,
IMECS 2014, March 12 - 14, 2014, Hong Kong

Fig. 3. System for retrieving and presenting recipes with Naming Concepts

users want to know the features of the recipes, they look into
the details of each recipe. However, it is a really daunting task
and determining its features is a time-consuming process.
Therefore, our system enables users to comprehend thefeatures of the recipes solely by checking the list of search
results.

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V. C ONCLUSION
In this work, we proposed a method that extracts Naming
Concepts for recipes, which are defined as characteristic
elements summarized by modifiers in the recipes’ titles.
We extracted different elements of ingredients and cooking
utensils, determined the relations between them by calculating their degree of co-occurrence and extracted Naming
Concepts by grouping the recipes based on feature patterns.
Further, we experimented with a real recipe dataset to extract
the Naming Concepts of given recipes.
In future work, we plan to enable the inferring of cooking
utensils that are not written expressly in procedures because
we found that typical cooking utensils tend to be omitted
in the procedures of recipes. For instance, although we
generally use a “frying pan” when “frying” something, it
is often omitted because users/readers can easily associate
the action “fry” with the cooking utensil “frying pan” in therecipes. Therefore, we plan to infer cooking utensils fromprocedures by considering actions in procedures.
ACKNOWLEDGMENT
This research was supported in part by the Grant-in-Aid
for JSPS Fellows 24.9154 from the Ministry of Education,
Culture, Sports, Science, and Technology of Japan. The experimental Rakuten recipe dataset was provided by Rakuten
Data Release from the Rakuten, Inc.
R EFERENCES[1] Rakuten recipe, http://recipe.rakuten.co.jp/.
[2] COOKPAD, http://cookpad.com/.

ISBN: 978-988-19252-5-1
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

IMECS 2014