Muddiest Point for Lecture 13

The classification and clustering is quite useful in information retrieval, since classification is based on supervised learning and clustering is based on unsupervised learning. Can we say that generally classification is more accurate than clustering?

Reading notes for Lecture 14

The main idea of the semantic web is machine-readable ontology standards. An "ontology" is a theory of the categories of things that comprise a given domain. Familiar examples of ontologies include taxonomies and controlled vocabularies. Every computer system uses an ontology. Computer systems that are built independently of one another, however, often cannot interoperate because they use different ontologies. Now that computer systems are heavily networked, numerous user groups have begun standardizing their ontologies. The semantic web consists of mechanisms for "marking up" ontologies and then processing them.
There are roughly four kinds of ontologies: document ontologies (e.g., the chapters of a book or the footnotes of a paper), metadata ontologies (e.g., the format of a file or the copyright status of a document), domain ontologies (e.g., the components of an automobile or the entries of a schedule), and service ontologies (e.g., the inputs of a software module, the steps of a transaction, or the formats of messages that are passed back and forth between a client and server). Because its topic is information retrieval and not web services generally, this course is mainly about document and metadata ontologies. In practice, however, document and metadata ontologies often include elements of domain ontologies, and many services use documents and metadata. For completeness, therefore, weeks 9 and 10 respectively will be on domain and service ontologies.
The semantic web includes a layered series of markup languages starting with XML (itself derived from SGML). The most distinctive aspect of XML is that user groups can use it to define specialized sublanguages to mark up the ontologies that are meaningful for their own work. "User-centered design" in a semantic web context means precisely the codification of a user group's ontologies. This is important because document collections that have been marked up within a standard XML-based language can be stored and retrieved in much more sophisticated ways than unstructured plain text documents, or documents whose structures have not been marked up in a standardized way. Although it is too soon to be certain, the result may be a revolution in the technology of information retrieval. In the past, this course has generally applied ideas from user interface design to more traditional information retrieval technologies.

Muddiest point for lecture 12

This lecuture is pretty interesting since it combine the adaptive system feature with information retrieval. adaptive IR is kind of based on relevance feedback and user profile. My question is:
Will the user profile and feedback be stored in the system, or just used when during the specific retrieval?

Reading notes for lecture 13

Text classification is one of the first applications of machine learning, that applies to the general problem of supervised inductive learning: given a set of training documents, classified to one or more predefined categories, learn to automatically classify new documents. Automated text classification has been used in a number of different applications: automatic indexing, content management, filtering, and routing, word sense disambiguation, and Yahoo!-style search space categorization. A plethora of techniques have been developed for text classification,including Nearest-Neighbours, Regression, Neural Networks
Naive Bayes, Decision Trees, and Support Vector Machines. In most cases, the classification algorithms require sufficient training data in order to generalize well on unseen documents.
However, the generalization using labeled examples is an extremely costly and time-consuming activity. The
need for classifiers that can learn from small training samples is imperative. This is an area of active research and several experiments have been conducted to boost conventional classifiers’ performance, by combining supervised learning with semi-supervised or unsupervised, using techniques such as co-training active learning and transductive SVMs .

Muddiest Point for lecture 10

Lecture 10 talks about some interesting topics on web retrieval. It also solved my question about the web ranking mechanism.

Reading Notes for lecture 11

Popular online communities and services such as Flickr, Youtube, Facebook or LinkedIn are spearheading an emerging type of information on the Web. This information is composed of classical textual and multimedia data, in concert with additional data (tags, annotations, comments, ratings). Perhaps most significantly, the information is overlaid on an explicit social network created by the participants of each of these communities. The result is a rich structure of inter-relationships between content items, participants and services. Although the size of such networks requires the use of advanced Information Retrieval techniques, classical IR models are not tailored for this type of content as they do not (in general) take advantage of the particular structure and unique aspects of this socially-driven content.
I'm interested in changes which such new media made on the IR system.