Concept Extraction and Clustering for Topic Digital Library Construction

This paper is to introduce a new approach to build topic digital library using concept extraction and document clustering. Firstly, documents in a special domain are automatically produced by document classification approach. Then, the keywords of each document are extracted using the machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy of the subset. Lastly, the taxonomy is modified to the hierarchical structure for user navigation by manual adjustments. The topic digital library is constructed after combining the full-text retrieval and hierarchical navigation function

Concept Extraction and Clustering for Topic Digital Library Construction

This paper is to introduce a new approach to build topic digital library using concept extraction and document clustering. Firstly, documents in a special domain are automatically produced by document classification approach. Then, the keywords of each document are extracted using the machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy of the subset. Lastly, the taxonomy is modified to the hierarchical structure for user navigation by manual adjustments. The topic digital library is constructed after combining the full-text retrieval and hierarchical navigation function

Text Mining Infrastructure in R

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Textual Data Clustering Methods

Shlukování textových dat je jednou z úloh dolování v textech. Slouží k rozdělení dokumentů do různých kategorií na základě jejich podobnosti, což nám umožňuje snadnější vyhledávání v takto rozdělených dokumentech. V práci jsou popsány současné metody sloužící k shlukování textových dokumentů, jež se využívají. Z těchto metod je vybrán algoritmus Simultaneous keyword identification and clustering of text documents (SKWIC), který by měl při shlukování dosahovat lepších výsledků, než standardní algoritmy jako např. k-means. Je navrhnuta a implementována aplikace řešící tento algoritmus. Na závěr je provedeno srovnání SKWIC se standardním k-means.Clustering of text data is one of tasks of text mining. It divides documents into the different categories that are based on their similarities. These categories help to easily search in the documents. This thesis describes the current methods that are used for the text document clustering. From these methods we chose Simultaneous keyword identification and clustering of text documents (SKWIC). It should achieve better results than the standard clustering algorithms such as k-means. There is designed and implemented an application for this algorithm. In the end, we compare SKWIC with a k-means algorithm.

Statistical learning techniques for text categorization with sparse labeled data

Many applications involve learning a supervised classifier from very few explicitly labeled training examples, since the cost of manually labeling the training data is often prohibitively high. For instance, we expect a good classifier to learn our interests from a few example books or movies we like, and recommend similar ones in the future, or we expect a search engine to give more personalized search results based on whatever little it learned about our past queries and clicked documents. There is thus a need for classification techniques capable of learning from sparse labeled data, by exploiting additional information about the classification task at hand (e.g., background knowledge) or by employing more sophisticated features (e.g., n-gram sequences, trees, graphs). In this thesis, we focus on two approaches for overcoming the bottleneck of sparse labeled data. We first propose the Inductive/Transductive Latent Model (ILM/TLM), which is a new generative model for text documents. ILM/TLM has various building blocks designed to facilitate the integration of background knowledge (e.g., unlabeled documents, ontologies of concepts, encyclopedia) into the process of learning from small training data. Our method can be used for inductive and transductive learning and achieves significant gains over state-of-the-art methods for very small training sets. Second, we propose Structured Logistic Regression (SLR), which is a new coordinate-wise gradient ascent technique for learning logistic regression in the space of all (word or character) sequences in the training data. SLR exploits the inherent structure of the n-gram feature space in order to automatically provide a compact set of highly discriminative n-gram features. Our detailed experimental study shows that while SLR achieves similar classification results to those of the state-of-the-art methods (which use all n-gram features given explicitly), it is more than an order of magnitude faster than its opponents. The techniques presented in this thesis can be used to advance the technologies for automatically and efficiently building large training sets, therefore reducing the need for spending human computation on this task.Viele Anwendungen benutzen Klassifikatoren, die auf dünn gesäten Trainingsdaten lernen müssen, da es oft aufwändig ist, Trainingsdaten zur Verfügung zu stellen. Ein Beispiel für solche Anwendungen sind Empfehlungssysteme, die auf der Basis von sehr wenigen Büchern oder Filmen die Interessen des Benutzers erraten müssen, um ihm ähnliche Bücher oder Filme zu empfehlen. Ein anderes Beispiel sind Suchmaschinen, die sich auf den Benutzer einzustellen versuchen, auch wenn sie bisher nur sehr wenig Information über den Benutzer in Form von gestellten Anfragen oder geklickten Dokumenten besitzen. Wir benötigen also Klassifikationstechniken, die von dünn gesäten Trainingsdaten lernen können. Dies kann geschehen, indem zusätzliche Information über die Klassifikationsaufgabe ausgenutzt wird (z.B. mit Hintergrundwissen) oder indem raffiniertere Merkmale verwendet werden (z.B. n-Gram-Folgen, Bäume oder Graphen). In dieser Arbeit stellen wir zwei Ansätze vor, um das Problem der dünn gesäten Trainingsdaten anzugehen. Als erstes schlagen wir das Induktiv-Transduktive Latente Modell (ILM/TLM) vor, ein neues generatives Modell für Text-Dokumente. Das ILM/TLM verfügt über mehrere Komponenten, die es erlauben, Hintergrundwissen (wie z.B. nicht Klassifizierte Dokumente, Konzeptontologien oder Enzyklopädien) in den Lernprozess mit einzubeziehen. Diese Methode kann sowohl für induktives als auch für transduktives Lernen eingesetzt werden. Sie schlägt die modernsten Alternativmethoden signifikant bei dünn gesäten Trainingsdaten. Zweitens schlagen wir Strukturierte Logistische Regression (SLR) vor, ein neues Gradientenverfahren zum koordinatenweisen Lernen von logistischer Regression im Raum aller Wortfolgen oder Zeichenfolgen in den Trainingsdaten. SLR nutzt die inhärente Struktur des n-Gram-Raums aus, um automatisch hoch-diskriminative Merkmale zu finden. Unsere detaillierten Experimente zeigen, dass SLR ähnliche Ergebnisse erzielt wie die modernsten Konkurrenzmethoden, allerdings dabei um mehr als eine Größenordnung schneller ist. Die in dieser Arbeit vorgestellten Techniken verbessern das Maschinelle Lernen auf dünn gesäten Trainingsdaten und verringern den Bedarf an manueller Arbeit

Topic-driven Clustering for Document Datasets

In this paper, we define the problem of topic-driven clustering, which organizes a document collection according to a given set of topics (either from domain experts, or as a requirement satisfying users' needs). We propose three topic-driven schemes that consider the similarity between the document to its topic and the relationship among the documents within the same cluster and from different clusters simultaneously. We present the experimental results of the proposed topic-driven schemes on five datasets. Our experimental results show that the proposed topic-driven schemes are efficient and effective with topic prototypes of different levels of specificity