DEIM Forum 2017 C5-5

Identifying Evolutionary Topic Temporal Patterns
Based on Bursty Phrase Clustering
Yixuan LIU†

Zihao GAO‡

and

Mizuho IWAIHARA‡†早 稲 田 大 学 大 学 院 情 報 生 産 シ ス テ ム 研 究 科‡808 -0135 福 岡 県 北 九 州 市 若 松 区 ひ び き の 1 -15 -4212
E-mail: †liuyixuan@ruri.waseda.jp, ‡gao_jihao@suou.waseda.jp,‡iwaihara@waseda.jp

Abstract Temporal text mining has wide applications in many domains, such as topic trajectories and summarization of
opinion trends online. In Wikipedia, one of the most popular websites, article topics cover quite broad and also highly specificthemes. This paper studies on a particular temporal text mining task: finding evolutionary patterns of topics from a collection ofarticles. Topics in Wikipedia articles grow and fade over time. In order to reveal the evolution of topics in one category, we
propose a time series clustering method based on burstiness of phrases in article revisions, where additions and deletions areboth considered. Our work presents a novel way to simplify phrase time series by burst detection，thus simplified phrase
addition and deletion time series are attained. As for clustering, we use dynamic time warping to measure the distance between
phrases, in addition to classical Euclidean distance. Our experiments provide reasonable results showing that the proposed
method can identify interesting evolutionary topic temporal patterns effectively. Research so far can be applied in diverse
domains like finding temporal association between key phrases, discovering emerging topics, detecting synonym, and so on.
Keyword Topic evolution, Temporal pattern, Burst detection, DTW, Clustering

1

has a huge burst since the 18 t h week from the beginning ofIntroduction

Over the past decade, numerous online collaboration

revisions. If patterns can be found, it will be much easiersystems have appeared and thrived on the Internet. Primefor us to find the reasons and relationship behind theseexamples include Wikipedia, Linux, Yahoo! Answers,

patterns and evolution.

Mechanical Turk-based systems [1]. They enlist a large
number of people to supply information and solve problems.
Users become the main strength of contributors. Moredevelopment of such systems will be witnessed in the near
future.
It is important to note that these platforms have broughta large amount of information. Wikipedia, one of the mostpopularwebsites,

is

a

free,

multilingual

Internetencyclopedia written collaboratively by volunteers aroundthe world [9]. Users who browse in it can also edit articles.
Each edit version will be saved on line and all the edit
history is available to public. Therefore, article topics
cover quite broad and also highly specific themes, and also
topics are changing and evolving as time goes by.
As a large-scale repository of structured knowledge andedit history, Wikipedia has become a valuable resource fortemporal text mining work [8]. Actually, a collection of
Wikipedia articles in one category have more than one
topics. Although the temporal patterns of articles are notobvious, there often exist temporal patterns in theseevolutionary patterns. As we can see in Figure 1, the edit
history of “Obama” in Wikipedia article “Barack Obama”

Figure 1 the edit trend of ObamaIn this paper, we investigate the problem of temporaltext mining and time series simplification. The goal isrevealing the evolutionary patterns of topics in one wholecategory in Wikipedia. Each output cluster contains key
phrases that refer to real topics. Topics in one cluster sharesimilar temporal patterns in edit history. One cluster may
contain multiple events which are related each other.
Usually people try to identify topic patterns fromsemantic level, such as word2vector and so on. Actually
the semantic method performs well at this field. However,few work finish this job from temporal angle. Unlike
traditional text clustering works, we solve the problem

with temporal information, by collecting the frequency and

The fundamental problem in [5] is that it is irrespective

time point of each key phrase candidate, we avoid theof bursts of edits. Kleinberg [6] proposed a burst model forsemantic fuzzy problem. According to time series, theextracting meaningful structures from document streams.

phrases in one cluster share similar temporal patterns,

The work develops a model to identify bursts that imposes

which semantic method cannot find. What is more, we

a hierarchical structure over all the streams. A burst is

combine the addition and deletion informatio n to track thedefined as a rapid increase of a term’s frequency ofdynamic situation of a phrase, instead of just quantity

occurrences. If the term frequency is encountered at an

statistics, which is never implemented in previous work.

unusual high rate, then the term is labeled as ‘bursty’. The

In order to solve the problems above, we propose a novel
clustering method for bursty phrases. More specifically:work is used to identify bursts in text stream and produce
state labels of bursts.

(1) We utilize burst detection technologies to detectburst phrases in a collection of articles of edit3

history. The time series of edit additions anddeletions

important

To find evolution of topics in a Wikipedia category, wetimestamps and burst levels are labeled. Thus weneed to monitor a relatively long period of editing time.

dump the minor details and the time series are

There are often more than one topic in an article, thus thesimplified by burst detection.

first step of our work is to extract typical topics in articles.

(2) Weuse

areattained,

k-means

clusteronly

by

We utilize key phrases to represent candidate topics.

similarities on temporal patterns. Dynamic time

According to Kleinberg [6], the larger the number of

warping (DTW) [7] is applied in time series distancearticles containing the candidate phrases, the larger themeasuring,reflect

probability of detecting it as bursty. Obviously the mostfactors of edit history, such as bursts, additions andimportant and shifty articles in Wikipedia are edited moredeletions.

times, therefore, key phrases are more likely to be shifty

where

to

where

Selecting historically significant
phrases

temporalkey phrases,

similarities

The rest of the paper is organized as follows. Section 2

and could be identified as bursty phrases. Then what wecovers related work on term burst detection and temporalneed to do is observing the time series of these phrases andclustering. Section 3, 4 and 5 presents our proposed method

abstracting

on bursty phrase clustering. Section 3 presents thedeletions need to be distinguished . Dynamic time warping

algorithm of selecting historically significant phrases .

method is used to measure the distance between time series

Section 4 abstracts time series of bursty phrases. Ourof key phrases. The DTW distance will replace classicalclustering method with DTW is demonstrated in Section 5.

Euclidean distance in k-means clustering method according

In

are

to Jang [4], and capture aforementioned features of edit

presented, and finally, Section 7 provides our conclusions

history. Finally clusters and centroids are obtained, whereand further research.

similar temporal patterns are kept together and more

Section6,

comparative

experimental

resultsthem by burst detection. Addition s anddetailed semantic relationships can be discovered by these2

Related work

evolution patterns.

Kalogeratos et al.[5] proposed an algorithm to improving
text clustering algorithm by term co-burstiness. The

Corpus of Revisions

algorithm first focuses on tern burstiness and co -burstiness,then creates a bursty term correlation graph. Then it
partitions the graph into k groups and compute a synthetic
prototypes of each group. Next, it reduces the number ofclusters by an agglomerative approach that merges similargroups based on cosine similarity of prototypes. Finally,

Burst
Detection

Simplified series

POS Tagger and Chunker

K-means Clusteringwith DTW

ChunksClusters

Extraction Rules

Patterns Finding & Summarizing

spherical k-means is used to obtain the final clusters with
the initialization of the synthetic prototypes. This method

Key phrases

Temporal patterns &Evolutionary topics

introduces bursts and inter-burst relationships into the
traditional text clustering. Temporal information is directly
used and the algorithm performs well.

Figure 2should give a higher weight to phrases appearing in long All the procedures are shown in the flow diagram in .

lived revisions.
Let t(r) denote the time point of revision r, and we utilize

Flow diagram of Bursty Phrase Clustering Method
In this section, we discuss about abstracting bursty

the timespan the revision lived to indicate the quality of r.

phrase time series problem. The clustering algorithm will

We propose our timespan-weighted phrase frequency overbe discussed in Section 4.

history as:

3.1

PFH 

POS Tagging and filtering

In order to cluster shifty topics, we first detect keyf ( p, ri ) t (ri 1 )  t (ri )it (rn )  t (r1 )(1)task, POS (part of speech) Tagger and Chunker provided byHere, ρ > 0 is a decay factor, and 𝑟𝑖 is the i-th revisionof the article, i=1,..,N.

Apache openNLP [11] is used to apply POS tagging andIDF (inverse document frequency) of a phrase is widely

chunking function upon the revisions of Wikipedia articles.

used for measuring uniqueness of the phrase in the corpus.

In each revision, sentences are divided into chunks andHowever, since we focus on detecting phrases tha t are

tagged by POS, like noun groups, verb groups and so on.

bursting in most revisions of articles, we do not consider

phrases that could represent topics in one article. In thisThe POS tagger produces various POS labels to eachchunk, such as: NN(Noun, singular or mass), NNS(Noun,

IDF.

We can evaluate how widely a term occurs in theconsidered article set as below:

plural), NNP(Proper noun, singular), and NNPS(Properrf ( p, D) 

noun, plural). Then we consider following features toextract our candidate phrases.
3.2|{r  D : p  r}| 1N(2)Here, N is the total number of articles in the corpus D,

Decay phrase frequency

Next we introduce a number of temporal features torefine candidate phrases.

|{r  D : p  r}|is the number of revisions where the

Revisions of one article are created over time, where

term p occurs. In case of the phrase is not in the corpus, we

various phrases are added or deleted, where phrase

plus one on numerator so that rf has no chance to be zero.

frequencies, namely the times one phrase occur in one
revision, dynamically change over time .

Temporalchanges of phrases capture the evolution of topics andarticles.

Traditional

TF-IDFdoes

notcapture3.3

Survival rate

When a phrase is added at a revision, it will be checkedsuchand sometimes revised by editors. If a phrase survives

temporal factors of document revisions. Revert of athrough a long sequence of revisions, it is an indicator ofrevision, which is to discard the latest revision and restore

good quality. Such long surviving phrases can be regarded

an older revision, sometimes happens due to a variety ofas significant, and they are useful for labeling bursting edit

reasons, but its main cause is vandalism, which appears as

activities.

a total or massive deletion of contents . Since massive

to value the quality of phrase, since editors would deletedeletions discontinue topic evolution, we remove suchpoor quality texts.

deletions from our history analysis.
In order to give weights to phrases through the whole

We measure the number of phrase remain times

We define the survival rate of a phrase p in an article aas:edit history, one way is to compute the term frequency witha decay factor, like Aji [3]. However, their model ignores
revision quality, and decay factor is applied based on

SR( p)  e

scale ( p )| R|contain( p)scale( p)(3)

revision counts, instead of time interval. In real life, some

Here, scale (p) is the number of revisions in a from therevisions can be poorly edited and revised soon , while afirst time p appears to the last time, and contain(p) is theburst of revisions can occur in a short time period due tonumber of a’s revisions containing p. The first part

frequent revises. Long-lived revisions are accepted bymeasures how many revisions the phrase survived over theeditors as trustworthy and high-quality through the edit

history, and the second part measures how long the phrase

history. On the other hand, short-lived revisions are likely

appears without interruptions during its lifespan. The

to be often insignificant, erroneous, or having content

survival rate is independent from text length. Phrases

overlapping due to unsupervised repeated additions . We

having long and non-interrupting lifespans are highlyscored by the survival rate.
Combining the features we proposed so far, we define
the weight function for selecting historically-significant

For adding edits, where  ( p, ri )  0 :To keep the encyclopedia clear, editors tend to reduce
repeated words. When new event happens, editors tend tophrases:Wpi  PFH ( pi , Rx )  SR( pi )  rf ( p, Rx )(4)

add new contents to the related articles, the first editor will
add the most content, which cover main event . The later
editor will supplement less contents. The effect of added4

contents in the early revision will last for a few revision

Abstracting time series of bursty
phrasesand decreases as time goes by. We define the phrase
frequency added in ri will have an adding effect af to

4.1

revision rj:

Time series modeling

Let S be a corpus, which is a sequence of target articles

s1 ,..., sn . The corpus can be chosen based on certain topics,
such as from a Wikipedia catego ry. Each article has asequence of revisions, which are considered as text stream:

S  [s1 ,...sn ](5)si  [r11 ,...rmn ](6)Here, S is the corpus,

 ( p, ri )  e 1 ( j i ) ,  ( p, ri )  0andj  i(8)af ( p, ri , rj )  
0,

(
p
,
r
)

0
orj

i
i

For deleting edits, where  ( p, ri )  0 :Negative frequency added into article means r evise toprevious revisions, which indicate that there was someerrors in previous revisions. Usually these errors would
stay for several revisions. Such Incorrect frequency should
be removed, thus the phrase frequency deleted in ri willsi represents the sequence of

have an deleting effect df to revision rj:

is denoted as rij.

 ( p, ri )  e 2 (i  j ) ,  ( p, ri )  0andj  i
df ( p, ri , rj )  
0,  ( p, ri )  0orj  iThe revision frequency of a phrase is the times the phrase

Therefore, the time series model is defined as follows:

occurs in one revision.

PF ( p, S )  [ Pf ( p, r11 ),...Pf ( p, rnm )], whererevisions of the i-th article in S. The j-th revision ofsi

As new revisions are created overtime, certain phrases increase their revision frequency,
while others keep stable for a time span, or decrease, andsometimes fluctuate.

The revision frequencies of thenPf ( p, rj )   (t , rj )   [af ( p, ri , rj )  df ( p, ri , rj )]i 1same phrase can be diverse between articles, but we can
expect bursting and simultaneous increase of revision
frequencies on multiple articles, due to a significant real

(9)

(10)
Where

rij is the j-th revision in i-th revision in certain

world event that triggers edits of these articles. To detect

category.

edit activities, we should focus only on changes of revision

4.2

frequency, which is caused by additions and deletions of a

Now the time series of each phrase is defined. However,phrase. The frequency difference between two revisions is

the revisions are too many to find rules or patterns.

given by:Therefore, a simplified method is considered on time ( p, ri )  pf ( p, ri )  pf ( p, ri 1 ) ,(7)

where, p is a phrase and pf is the revision frequency ofphrase p in one article.
When an event happens, a number of revisions will becreated on one article, because each editor repeatedly
improves the article, and multiple editors could try todescribe the same event. Also, there can be multiple
articles related to the event.

In our burst modeling, weapply a decay function to consider lingering effects frompreceding edits.

Burst detection

series—burst detection.
The goal of burst detections to develop a model and the“bursts”

canbeidentified

robustly andefficiently.

According to [6], the rapid increase of a phrase’s frequency
of appearance, defines a phrase burst in the text stream.
The bursts are identified with multiple states, where ratedepends on the current state.
First time series PF ( p, S )  [ Pf ( p, r11 ),...Pf ( p, rnm )] aresplit in T non-overlapping time windows:PF ( p, S )  [ Pf ( p, R11 ),...Pf ( p, Rt0 j0 )], where

(11)

n

Pf ( p, Rpj )   ( p, R pj )   [af ( p, R pi , R pj )  df ( p, R pi , R pj )]can be applied in time series mining domain.

5

i 1

Clustering by DTW

the time span divided by finite number, in other word, is

5.1
Dynamic time warping measure
In order to uncover the temporal dynamics of topics in
category in Wikipedia, we use clustering method. It allows
us to find diverse temporal patterns. However, the classic
Euclidean distance metric of traditional clustering method
is not suitable enough for times series distance. That is
because the topic bursts are sparse and random. Sequences
vary in time and different in length. For example, in, two series are quite matched in patterns but different intime unit. Usually we take one day or one week as a time

time span. In Euclidean metric, the distance is too large.

unit.

How

In this equation,

R p is a batch of revisions arriving at

the p-th window, andR pj denotes the revisions of p-th

batch which contain the phrase

p j . The time window is

to

align

the

peaks?

The

time

scalingand( Rp , Rpj ) can benormalization work, but the overall volume of distinct time

seen as an output symbol that is produced probabilisticallyintroduce dynamic time warping (DTW) to measure theaccording to the internal state of a hidden Markov model.

similarity between two sequences.
DTW is implemented using dynamic programming. [2]By forming local cost matrix where each element denotes
the distance between two points with corresponding indicesof two sequences, the method aligns two sequenc es

In burst detection model [5], each

The whole model considers a cost of passing in more burst
states. The goal of it is to find the minimal cost for eachstate. The cost of a state sequenceq  (qi1 ,...qin ) in

series is too diverse to be directly compared. Therefore, we

finite-state automaton is defined as follows:| Rpj | |Rpj || R || R |) p (1 | pi |) pj p ] (12)| Rp | i (i,| Rpj |,| Rp |)   ln[(

Here, p is the probability of a state change,| Rp | isthe number of revisions in the arti cle at the p-th window,
and| Rpj | denotes the number of revisions of p-th batch

which contains the phrase

pj .

with the i-th component of sequence

With this burst detection model the state transition
life span of the burst is a distance from first and last burst
appearance moment. Using this model, we ignore small
spikes and abnormal phenomenon and focus on common
phrase frequency trend.

matrix is as follows:d (i, j )=dis((si1 ,x i1 ),(s j 2 ,x j 2 ))= (si1  si 2 ) 2  (x j1  x j 2 ) 2(14)Then the warping path is constructed by building aD(i, j )  d (i, j )  M ij

Now the time series are detected by Kleinberg model, we
can just simplify the time series with burst and timestamp.
Then only state-transition point are recorded:

Where

and the j-th

global cost matrix D(i, j):Time series abstraction

TS j  [(s1 , x1 ),...(sn , xn )]TS1

TS2 . Then the distance measure in local cost
component of

sequence that minimizes the sot function is at tained. The

4.3

Figure 3

together. Let d (i, j ) be the local distance cost associated(13)

si is the state of xi time point to phrase j.

According to phrase extraction and burst detection
model we simplify phrase frequency time series, which(15)

Where M ij is the minimal value of three elements.

M ij  min[ Di 1, j , Di , j , Di , j 1 ](16)

Therefore, the global distance matrix can be formed:0 d  M
1,1
2,2
D

...

 d m,1  M m1,2...

In order to analyze the temporal patterns of content in... d1,n  M 2,n 1 

...
...

... d m,n  M m1,n 1 

(17)

Wikipedia, we focus on several themes as categories.
Usually the entertainment shows and politic events include
enough contents, with useful information and involving
widely fields. Therefore, these themes will be selected as

In [2], as we can see, using DTW method, the peaks areexperiment samples.

aligned under optimal procedure. After that the distance is

In our experiments, we select 10 different categories. In

calculated. Though there is time delay, the distance is still

each category, articles are filtered by these criteria:

small, that is what we want: focus on temporal patterns

English language, text volume is more than one page,and forms instead of exact time point. While by thehaving more than 200 revisions. After filtering, we

Euclidean metric (Figure 4a), the time series distance ismanually review the articles and remove articles withlarger because of the time lag.

repeat content or irrelevant contents. Finally, in eachcategory 10 articles are filtered as datasets. As shown
below. Then we retrieve all the revisions of each article.
Since the data is too big to present, we just show one
category as an example and explain it. In this category we
chose

10

articles:BarackObama,

DonaldTrump,

Democratic

Party(UnitedStates),DonaldTrumppresidential campaign, 2016, Hillary Clinton, Republican
Figure 5aParty (United States), United States Presidential election,

Figure 4b

2016, United States Presidential election, 2012, UnitedStates Presidential election, 2008. The revisions of these

Each similarity between pair of sequences can bemeasured in DTW now. The distance part is calculated byarticles are sorted by timeline and then processed by POS

2d coordinates instead of value in sequence with unified

tagging and filtering strategy. 100 key phrases candidates

time interval, because the sequences are sparse, unifying

are picked out by the key phrase extraction step, and phrase

time axis does not work out.

frequency on each time point to respon ding phrase Now we

5.2
In

could process the datasets by our method in this paper.
We first consider the POS tagging and filtering problem.

Improving K-means clusteringclassical

k-means

clustering,themethodWhen detecting key phrases that could represent topics inaccomplishes work in two steps. [10] The first one is

one article, POS (part of speech) Tagger and Chunker

assignment step: it selects initial centroids randomly andprovided by Apache openNLP [11] is used to apply POS

assigns each item to the closest cluster. Next is refinement

tagging and chunking function upon the revisions ofstep, the cluster centroids are recalculated by current
situation and the items are reassigned to the cluster closest
to them. The second step is repeated until the sum of the

Wikipedia articles.
Since articles contain quite a large number of chunks,
but not all of them are useful, we reduce the amount of

Euclidean distances between the items inside one cluster

calculation, by filtering chunks through linguistic features.

minimize.

As we know, the most frequent but least useful POS in an

However, Euclidean metric is not suitable for sequence
distance measuring. So DTW metric is introduced. Now theGoal become minimizing the function F:KF    d DTW ( Pfi , k )adjective and adverbs from the articles. There are also stop
words and phrases, such as “let’s”, “for example”,“December”, which are also removed from candidate

2

(18)

k 1 xCk

Wherearticle is preposition. We remove prepositions, as well as k is the centroid of cluster k, Ck represents

phrases. After the filtering step, the majority of remaining
chunks are nouns, followed by verbs and others.
After collecting phrases of revisions, we utilize themethod in this paper to extract key phrases and cluster themaccording to time series.

the cluster k.

6

Experiments

The results are shown as Figure 6 temporal patterns of(1) Clusters without burst detection.

Cluster 1

Cluster 2

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 3

Cluster 4

Figure 7 Temporal patterns without burst detection

Figure 6 temporal patterns of evolutionary topics
evolutionary topics

In Figure 7, we still try to divide the datasets into 4

Since the edits of a phrase of one day are too small, weclusters, but the results are not obvious. Only 11 phrases

choose “week” as a time unit. There are totally 167 weeks

are clustered into cluster 1 and cluster 4. The number of

2015. We test many times

phrases in middle clusters is too many, which reduces theand 4 clusters are observed. The burst detection step helps

uniqueness of each cluster. The “news post” phrases

us to remove most of spikes and noises so that onl y notable

distribute in every cluster. We cannot tell the difference

burst are remarked. In cluster 1, there is a small burst at

between cluster 2 and 3.

from Aug.1st, 2012 toOct.6 t h ,October 2012, and main burst is at 2015 June to August. In

That is because the frequency range is large so that details

this cluster, most key phrases are American news post, suchare neglected. The time series are only the frequency delta

as Washington post, Huffington post and so on. That isof each phrase, which leads to spikes and noises. Therefore

because in 2012 and 2016, political news emerges under

temporal pattern of each topic is difficult to summarize.

the circumstances of presidential elections. Cluster 2 only
has 3 topic peaks, most phrases are sudden events which
we cannot predict though it may happen before, forexample, an illegal immigrant. In 2015 Sept, a new policy
for immigration is practiced, therefore the burst of this
topic is obvious. Situations like this are many: ISIS, Syria,
Mac Cain and so on. Cluster 3 is similar to cluster 1, butthey are different. In cluster 3, two main bursts share thesame level of bursts while cluster 1 does not. In cluster 3,
most phrases are like “Trump”, “Rand Paul” and “TedCruz”.

From

2012

Octoberto2013

January,

thepresidential election of US brought extensive influence and(2) Clusters without dynamic time warping distancecalculation.
In Figure 8, we calculate the distance between timeseries by Euclidean method, this time we cannot evendistinguish temporal patterns and only 3 clusters are
divided. Two clusters are similar and the other is zero. Only
14 phrases are clustered into cluster 1 and 3. T he last 86
phrases are in cluster 2. It is difficult for us to find the
topics in so many phrases.
Cluster 1

Cluster 2

debate, which is displayed on Wikipedia edit history. Ted
Cruz became famous for his Ob amacare All-Nighter lasting
21 hours. Then in 2016, he joins the election again and thephease bursts at 2015 summer. In cluster 4, there is no
obvious bursts, most phrases in this cluster are “actors”,

Cluster 3“campaign”, “crime” and so on. These phrases displaye dimportant role and are quite frequent in articles. Additions
and deletions go with the revision editing all the time. But
these edits are fluctuations, not bursts.
We try to do the experiments without bursts or additions
and deletions, the results are as follows:

Figure 8 temporal patterns with Euclidean metricThis is because the addition and deletion delta is verysmall in time series, direct subtraction would make furthereffect on reducing the difference, instea d of expanding it.
By DTW, we focus on the similar fragments of two
sequences, which is proofed better in this experiment.
All in all we can see that we find different temporal
patterns of topics by our method. These phrases are similar
in semantic level, we cannot distinguish them from thisangle.

However,

from

the

perspectiveoftemporal

information, we can find their similarities and differences.
The reasons behind these patterns are multiple, some comefrom the common political events, and some come fro m
famous person or festivals. The method helps us to identify
these features and the rules behind these clusters.

7

Conclusion and future work

In this work, we proposed a novel approach for using
bursty phrase information in text clustering. By the use ofedit history we discover multiple evolutionary temporal
patterns of topic in certain category in Wikipedia. The
bursts of phrases are regarded as the way to simplify phrase
time series. In addition to burst detection we cluster timeseries into multiple clusters, each of them represents a kindof evolutionary pattern of hot topics. To avoid thedifference of phrase frequency, we consi der the “change”
of each phrase, that is, we calculate the addition anddeletion of phrase frequency.
In this work we investigate the time series simplificationand temporal patterns clustering. A novel way is proposed
to simplify the time series, burst detection makes us focus
on only burst part of time series. What is more, interesting
evolutionary temporal patterns are identified, which helps
us discover the semantic relationship from a temporalperspective. Phrases in one cluster share similar temporal
patterns, which results from common events or festivals.
These reasons are waiting for discovering.
Our experiments performs well. The reasonable results
show that the algorithm can identify interesting temporal
patterns effectively. Research so far can be applied indiverse domains like finding temporal association between
key phrases,

discovering

emerging

topics,detectingsynonym and so on. Further work will focus on theclustering methods. DTW is also considered to be improvedin order to stress the burst overlapping between time series.

8

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