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"Machine Learning (ML) algorithms have opened up new possibilitiesfor the acquisition and processing of documents in InformationRetrieval (IR) systems. Indeed, it is now possible to automate severallabor-intensive tasks related to documents such as categorization andentity extraction. Consequently, the application of machine learning techniquesfor various large-scale IR tasks has gathered significant researchinterest in both the ML and IR communities. This tutorial provides areference summary of our research in applying machine learning techniquesto diverse tasks in Digital Libraries (DL). Digital library portalsare specialized IR systems that work on collections of documentsrelated to particular domains. We focus on open-access, scientific digitallibraries such as CiteSeerx, which involve several crawling, ranking,content analysis, and metadata extraction tasks. We elaborate on thechallenges involved in these tasks and highlight how machine learningmethods can successfully address these challenges."

"This book constitutes the refereed proceedings of the 19th International Symposium on String Processing and Information Retrieval, SPIRE 2012, held in Cartagena de Indias, Colombia, in October 2012. The 26 full papers, 13 short papers, and 3 keynote speeches were carefully reviewed and selected from 81 submissions. The following topics are covered, fundamentals algorithms in string processing and information retrieval, SP and IR techniques as applied to areas such as computational biology, DNA sequencing, and Web mining."

"Information retrieval of text document requires a method that is able to restore a number of documents that have high relevance according to the user's request. One important step in the process is a text representation of the weighting process. The use of LCS in Tf-Idf weighting adjustments considers the appearance of the same order of words between the query and the text in the document. There is a very long document but irrelevant cause weight produced is not able to represent the value relevance of documents. This research proposes the use of LCS which gives weight to the word order by considering long documents related to the average length of documents in the corpus. This method is able to return a text document effectively. Additional features of word order by normalizing the ratio of the overall length of the document to the documents in the corpus generate values of precision and recall as well as the method of Tasi et al.Sistem temu kembali dokumen teks membutuhkan metode yang mampu mengembalikan sejumlah dokumen yang memiliki relevansi tinggi sesuai dengan permintaan pengguna. Salah satu tahapan penting dalam proses representasi teks adalah proses pembobotan. Penggunaan LCS dalam penyesuaian bobot Tf-Idf mempertimbangkan kemunculan urutan kata yang sama antara query dan teks di dalam dokumen. Adanya dokumen yang sangat panjang namun tidak relevan menyebabkan bobot yang dihasilkan tidak mampu merepresentasikan nilai relevansi dokumen. Penelitian ini mengusulkan penggunaan metode LCS yang memberikan bobot urutan kata dengan mempertimbangkan panjang dokumen terkait dengan rata-rata panjang dokumen dalam korpus. Metode ini mampu melakukan pengembalian dokumen teks secara efektif. Penambahan fitur urutan kata dengan normalisasi rasio panjang dokumen terhadap keseluruhan dokumen dalam korpus menghasilkan nilai presisi dan recall yang sama baiknya dengan metode Tasi dkk."