別紙41 (課程博士(英文))Date of submission (01‑11‑2018)

DepaHmentCon〕puter science andStudenHD Numbcr

D145309

En宮inee血ng
PP11Cant、s nameMasakiAono,SupelNISOTS

Kyojiumemura
Abu Nowshed chy

Abstnct (Doctoo
Exploiting Telnporal and sen〕antic h〕f0Π1〕ation for Miの'oblog Retrievalt111'OUσhIitle oflhesis

Quely Expansion and Reral〕kingApproaches(クエリの拡張と再順位付けアプローチによるマイクロブログ検索のための時間的および意味的情報の活用)

Approx.80o words
Nowadays, microblog websites are not only t11e places in maintaining socia1 ル1ati011Ships but also act as aValuable inlbrmation source. EV創'yday lots of usel's t山'n into these sites to fU1打11their divelse i11f01'mation needs
Moreover, during a disaster pe"od, micl'oblogS 田'e h'eated as an important soul'ce to sel've the situational
infomlation needs. Among seve】'al microblog sel'vices, twitter is now the most popular. Hence, inf011nation
retrievalin Nitter has made a h北 With a lot ofcomplaisance,
However, due to the sh011 1ength chal'acteristics oftweets, people usuaⅡy use 山Iconventional abbl'eviations (e.gUse "2day" instead of"today"), PO01'1inguistic phrases (e.g. use "1YT" instead of"take yourtime"), and uRL toexpress their concise thought. Besides this, some Nitter specific syntaxes (eg.#haS11tags, retweets) also vel'y
Popular among twitter users. MOTeover, usel's usuaⅡy seal'ch t11e t引np01'aⅡy l、elevant information in Ni杜er, suchas bl'eaking news and real‑time events. AⅡ these chal'actel'istics lnake it cha11enoing fbr e仔ective infonnation
1'etl'ieval (1R) over t、veets

In dlis thesis, we propose two di仟引'ent appl'oaches to tackle the chaⅡenges of microblog retrieval. At first, wePI'opose a re↑anking based approach, whel'e the main focus is to rel'al]k the Neets that al'e retl'ieved by using ule
baseline retheval model. whereas, our proposed query expansion based approach augments the oliginal que"esWith expansion terms that best represent tl〕e usel'S' intent. hl both approaches, we used the Lucene'S
hnplelnentatjon ofquery likeHhood as the baseline re廿ieval n〕odel

In our reranking based appl'oach,、ve emphasize t11e a11eviation of vocabulary mismatch, and the leverage of thetemp01'al (e.g. recency and burst nature) and contextual ch印'actel'istics of tweets. one way of a11eviating theVocabu】al'y mismatch problem is to ref01'mulate dle query via qua'y expansion.1n t11is regard, we pl'opose athree・stage quely expansion teC11nique by leve12ging the pseudo‑relevant Neets at the 6rst stage, made use of＼veb search results at ule second stage, and extracted haS11tags relevant to a〕e query at ule third stage. ForWeightina te11ns, we used the lDF‑score ofeach te11τ1

In the feature extl'action stage,、ve extract sevelal effective featⅢ'es for l'eranki11g by leveraging the diff引・enttweet
Characteristics. To extl'acttemporal aspect oftweets, we detennine the temporal dimension ofthe query and ifthe
query is temporaⅡy sensitive,、刃e extract our proposed telnporal features such as recency sCω'e of tweets by
Utilizing the query time and t、veet time.圦le also hypothesize that any tweet migl】t have b山'st、time populal・ity
Once the Neet has been posted.111 m、del'to h11Plement our hypothesis,、ve propose to introduce a "burst、time"aware temp01'al feat山'e as 、veⅡ. To extract the content、aware feat山'es, we utilize some classicalinf01'mation
1'etrieva11nodels. Along with this dh'ection, we extractthe t圦litter speci6C features such as uRL and retweet countand acc0山〕tl'elated features such as f0ⅡOW引's count and status count to address the special charactenstics oftweets and l'elations between twitter users. NI01'eover, we propose some context relevance feat山'es based on wordembedding, kel'nel density esthnation, and quel'y‑Neet sentiment con'elation to address the contextual dimension

Of Neets.圦le 11ypothesize that a query is sentimenta11y sensitive ifthe largest prop011ion of the i11itia11y l'eh'ievedt圦/eetS 11as the silnilm' kind of sentilnent polal'ity. once our pl、oposed feat山'es m'e extracted, a supelYised featⅢ'eSelection 111ethod based on regularized regl'ession is applied to select the best feature combination. A介erestimating ule feature imp011ance using the randonl f01'est, an ensemble of leanling to rank ([2R) frame、vork isapplied to esthnate the relevance ofa query、tweet pail'However,the naive quel'y expansion technique that we pl'oposed in 0山'1'el'anker 介amew01'k used the ΦF‑SC01'e to

1'ank each term which might induce il'1'elevant ral'e ten】1S fl'om t1祀Πoisy Neet contexts. Hel]ce, selecting terms by
Utilizing dle unsupel'vised appl'oaC11 and 11ighly l'eliant on t11e top l'etrieved results without considel'ing temporal
1'elevance may gen引'ate the noisy or hamlful expansion tel'n】s which degl'ade the retrieval perf01'mance
Conside"ng dle above limitauons,、ve pl'esent another qU引'y expansion appl'oaC11,、vhel'e supervised leaming isadopted for selecting expansion ten)1S. upon retl'ieving tweets by our pl'oposed topic modclino based qU剖'yexpansion (1MQE), we utilize the pseudo relevance feedback (PRF) and a new temp01'a11'elatedness (1R)
appl'oach to select the candidate t、veets. Next, we devise several new feat山'es to select the temp01'a11y andSelnantica11y releva11t expansion te11ns by leV引'aging the temp01'al, word embedding, and sentiment association ofCandidate tenn and query. Moreover, we also utilize the lexical and twi杜剖' speci6C feat山'es to quantify t11e tennrelatedness. A負引' supervised feature selection using l'egularized l'egression, we estimate t11e feat山'e importanceby applying random f01'est. Then, we design a linear leamⅡ18 to rank (L2R) model with dle aid of fea加re values

and uleir importance weightto rank the candidate expansion tenns
Experimental result on lREC Microblog 2011 and 2012 test c011ections over the TREc lweetS2011 Corpus
delnonS廿ate the e仟ectiveness of our pl'oposed rerankirlg and query expansion approaches over the baseline andState‑ofthe、art methods