Selectivity-Based Keyword Extraction Method

Beliga, Slobodan; Meštrović, Ana; Martinčić-Ipšić, Sanda

doi:10.4018/ijswis.2016070101

Cited by 59 publications

(46 citation statements)

References 28 publications

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“…Nadalje, značajke, odnosno, vektori značajki koji se koriste u postupcima učenja šume slučajnih stabala (Breiman, 2001) temelje se na mjerama kompleksnih mreža (Newman, 2018). Kompleksne mreže primjenjuju se na raznim područjima, poput predviđanja novih veza u društvenim mrežama (Martinčić-Ipšić et al, 2017), određivanja ključnih riječi u tekstu (Beliga et al, 2015;Beliga et al, 2016), modeliranja jezika , analize koautorstva (Meštrović i Grubiša, 2015) i sličnog, dok je šahovska igra samo mjestimično modelirana kroz formalizme kompleksnih mreža, i to u radu (Farren et al, 2013), stoga je to upravo i predmet istraživanja. U radu se istražuju mogućnosti modeliranja šahovske igre kroz formalizam kompleksnih mreža, s ciljem dobivanja boljeg uvida u razvoj šahovske partije, te uvida u način na koji informacije dobivene iz strukture kompleksnih mreža utječu na predviđanje konačnog ishoda partije.…”

Section: Uvodunclassified

Primjena šume slučajnih stabala za predviđanje ishoda šahovske partije reprezentirane kompleksnom mrežom

Jokić

Martinčić-Ipšić

2019

Zb. Veleuč. Rij. (Online)

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Mag. inf., student, Odjel za informatiku, Sveučilište u Rijeci, Radmile Matejčić 2, Dr. sc., izv. prof., Odjel za informatiku, Sveučilište u Rijeci, Radmile Matejčić 2,SAŽETAK Cilj je ovog rada izgraditi model vrednovanja šahovskih pozicija koji se temelji na informacijama dobivenima iz mrežnih značajki pozicija šahovskih figura na ploči. Međusobni odnosi figura, kao i odnosi figura i polja koja zauzimaju, a koji opisuju određene taktičke i strateške elemente šahovske partije, mogu se modelirati kompleksnom mrežom. Ovim je radom pokazano kako upotrebom samo određenih mjera koje opisuju strukturu kompleksne mreže možemo naučiti klasifikator pozicija koji može predvidjeti krajnji ishod partije bolje od klasične Shannonove evaluacijske funkcije. Shannonova evaluacijska funkcija kvantificira materijalno stanje na ploči obiju strana u šahovskoj igri, mobilnost figura, sigurnost kralja te kvalitetu pješačke strukture. Računalni model koji klasificira na ulazu ima vektore značajki određene iz četiri vrste mreža (mreža podrške, mreža mobilnosti, pozicijska mreža, mreža praćenja) konstruiranih iz baze majstorskih partija, od kojih svaka modelira određeni aspekt šahovske igre. Vektor značajki sadrži značajke dobivene izračunom različitih mjera strukture mreže. Za određivanje značajki prema važnosti, kao i klasifikacijski postupak ishoda igre, upotrebljava se šuma slučajnih stabla. Nadalje, eksperimentalno se određuju osnovne evaluacije statičkih pozicija u šahovskim partijama pomoću Stockfish šahovskog programa. Nakon toga, značajke te pripadne evaluacijske ciljne klase (pobjeda bijelog, pobjeda crnog igrača ili remi) udružuju se u ulazne vektore za učenje modela klasifikacije pozicija, čiji se rezultati uspoređuju sa Shannonovom evaluacijskom funkcijom. Pokazano je kako predložena metoda vrednovanja temeljena na informacijama o strukturi mreže daje bolje rezultate (75 % točnosti) od klasične Shannonove evaluacijske funkcije (52 % točnosti) za testnu bazu partija. Ključne riječi: evaluacijska funkcija u šahu, kompleksne mreže, mrežne značajke, šuma slučajnih stabala, predviđanje ishoda šahovske igre Creative Commons Attribution -NonCommercial 4.0 International License J. Jokić, S. Martinčić-Ipšić: Primjena šume slučajnih stabala za predviđanje ishoda… ABSTRACT This work addresses the problem of constructing a static chess position evaluation model which utilizes only information extracted from complex networks' features of positions of chess pieces on the board. The mutual relations of chess figures, the complex relations of figures and the positions on the chess board, as well as the information on which fields they are attacked, describing tactical and strategic elements of the chess game, all these are modeled by a complex network formalism. The goal of this work is to demonstrate that it is possible to train a classifier which would have better prediction results of the game outcome, utilizing only selected measurements of the complex network' s features rather than the corresponding Shannon' s evaluation function. Sha...

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Section: Uvodunclassified

Primjena šume slučajnih stabala za predviđanje ishoda šahovske partije reprezentirane kompleksnom mrežom

Jokić

Martinčić-Ipšić

2019

Zb. Veleuč. Rij. (Online)

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“…Detailed surveys of the state-of-the-art keyword extraction techniques can be found in [7], [8], [9]. A comparative analysis of automatic keyword extraction algorithms along with text summarization challenges was presented in [10].…”

Section: A Automatic Keyword Extraction Techniquesmentioning

confidence: 99%

“…Existing techniques can be classified by approach as supervised and unsupervised, the latter including simple statistic, linguistics, graph-based and hybrid. Supervised techniques require annotated training data, while unsupervised operate without preliminary annotation or labelling (e.g., [7]). A comprehensive study of performance for supervised ensemble methods and base learning algorithms (Naive Bayes, support vector machines, etc.)…”

Section: A Automatic Keyword Extraction Techniquesmentioning

confidence: 99%

Automatic intonation-based keyword extraction from academic discourse

Lezhenin

Diachkov

Lamtev

et al. 2018

Annals of Computer Science and Information Systems

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This paper examines the perspectives of intonation processing for automatic keyword extraction. Based on a discourse intonation model from D. Brazil, automatic tone pattern recognition in speech stream is performed. It is shown that automatic classification of tone patterns can be done using simple polynomials and correlation. The original software tool PitchKey-wordExtractor (PKE) was applied to academic discourse (on-line lectures) to extract keywords. The results were compared to the output of popular tools for speech analytics: VoiceBase and IBM Watson. All the records were processed also with Praat software and annotated by human experts. Experiments show that none of the automatic systems outperforms the others and PKE, VoiceBase and IBM Watson have the identical error rates with respect to human expert opinion. It motivates further research and supports the tendency to integrate intonation and, more generally, prosody processing in automatic keyword extraction.

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“…Selectivity is defined as the average weight distributed on the links incident to the single node, and has proven efficient for different language network tasks, ranging from the differentiation between original and shuffled text [49] to the differentiation of text genres [50] and for keyword extraction [51, 52]. We also note that link prediction on Twitter has been studied before in [53], where CN, AA, JC and RA measures were combined with the information about corresponding communities as determined with a variant of the label propagation algorithm in unweighted and directed networks.…”

Section: Introductionmentioning

confidence: 99%

Link prediction on Twitter

2017

Self Cite

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With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.

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Selectivity-Based Keyword Extraction Method

Cited by 59 publications

References 28 publications

Primjena šume slučajnih stabala za predviđanje ishoda šahovske partije reprezentirane kompleksnom mrežom

Primjena šume slučajnih stabala za predviđanje ishoda šahovske partije reprezentirane kompleksnom mrežom

Automatic intonation-based keyword extraction from academic discourse

Link prediction on Twitter

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