LEADER 01249nam 2200325Ia 450 001 996395574303316 005 20210104171713.0 035 $a(CKB)3810000000017404 035 $a(EEBO)2248529735 035 $a(OCoLC)ocn690988118e 035 $a(OCoLC)690988118 035 $a(EXLCZ)993810000000017404 100 $a20101209d1642 uy 0 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aSolomons Song of Songs in English metre$b[electronic resource] $ewith annotations and references to other scriptures, for the easier understanding of it /$fby Henry Ainsvvorth 210 $a[Amsterdam $cPrinted by Richt Right Press]$dprinted in the yeare of our Lord, 1642 215 $a[96] p 300 $aPlace and publisher of publication suggested by Wing (2nd ed.). 300 $aText in double columns. 300 $aImperfect: tightly bound, print show-through with some loss of text. 300 $aReproduction of original in: Corpus Christi College (University of Oxford). Library. 330 $aeebo-0030 700 $aAinsworth$b Henry$f1571-1622?$01001314 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a996395574303316 996 $aSolomons Song of Songs in English metre$92339776 997 $aUNISA LEADER 04022nam 22006255 450 001 9911015964903321 005 20250722130302.0 010 $a981-9688-53-1 024 7 $a10.1007/978-981-96-8853-1 035 $a(CKB)39698594900041 035 $a(MiAaPQ)EBC32227303 035 $a(Au-PeEL)EBL32227303 035 $a(DE-He213)978-981-96-8853-1 035 $a(EXLCZ)9939698594900041 100 $a20250722d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTopic Modeling $eAdvanced Techniques and Applications /$fby Yanghui Rao, Qing Li 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (291 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a981-9688-52-3 327 $aChapter 1. Introduction -- Chapter 2. Classical Topic Models -- Chapter 3. Modern Topic Models -- Chapter 4. Applications -- Chapter 5. Discussions. 330 $aAs a well-known text mining tool, topic modeling can effectively discover the latent semantic structure of text data. Extracting topics from documents is also one of the fundamental challenges in natural language processing. Although topic models have seen significant achievements over the past three decades, there remains a scarcity of methods that effectively model temporal aspect. Moreover, many contemporary topic models continue to grapple with the issue of noise contamination, particularly in social media data. This book presents several approaches designed to address these two limitations. Initially, traditional lifelong topic models aim to accumulate knowledge learned from experience for future task. However, the sequence of topics extracted by these methods may shift over time, leading to semantic misalignment between the topic representations across document streams. Such misalignment can degrade the performances of various downstream tasks, including online document classification and dynamic information retrieval at the topic level. Additionally, the challenge of coherent topic modeling is particularly relevant due to the noise and large scale of social media datasets. Messages on social media platforms often consists of only a few words, resulting in a lack of significant context. Models applied directly to this type of text frequently encounter the problem of feature sparsity, which can yield unsatisfactory outcomes. In the context of emotion detection, public emotions are known to fluctuate across different topics, and topics can evoke public emotion. Thus, there is a strong interconnection between topic discovery and emotion detection. Jointly modeling topics and emotions is a suitable strategy for these tasks. This book also examines the impact of topics on emotion detection and other related areas. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aNatural language processing (Computer science) 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aComputational linguistics 606 $aNatural Language Processing (NLP) 606 $aMachine Learning 606 $aData Science 606 $aComputational Linguistics 615 0$aNatural language processing (Computer science) 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aComputational linguistics. 615 14$aNatural Language Processing (NLP). 615 24$aMachine Learning. 615 24$aData Science. 615 24$aComputational Linguistics. 676 $a006.35 700 $aRao$b Yanghui$01834967 701 $aLi$b Qing$0431258 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015964903321 996 $aTopic Modeling$94410650 997 $aUNINA LEADER 01212nam 2200265 450 001 996710473303316 005 20260227170252.0 100 $a20260227d2003----km y0itay5003 ba 101 0 $aita 102 $aIT 105 $ay 00 y 200 1 $aRatifica e attuazione in Italia dello Statuto di Roma$equestioni di compatibilitą costituzionale e opportunitą di un'armonizzazione legislativa$fAnna Oriolo 210 $a[Napoli]$cEdizioni scientifiche italiane$d2003 215 $a[265]-307 p.$d24 cm 300 $aEstratto da: Attuazione dei trattati internazionali e costituzione italiana. Una riforma prioritaria nell'era della comunitą globale : atti del convegno internazionale, Universitą di Salerno, 13-14 dicembre 2001 / a cura di Giuliana Ziccardi Capaldo ; contributi di G. Ziccardi Capaldo ...[et al.], Napoli : Edizioni scientifiche italiane, 2003 606 0 $aCorti costituzionali$2BNCF 676 $a342.020269 700 1$aORIOLO,$bAnna$0505315 801 0$aIT$bcba$gREICAT 912 $a996710473303316 951 $aXVI.7.Misc. 2598$b2645 FBUO$cXVI.7.Misc. 959 $aBK 969 $aFBUO 996 $aRatifica e attuazione in Italia dello Statuto di Roma$94548794 997 $aUNISA