Chris Staff

Incremental Clustering of News Reports

When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated processes may be required to help manage this information. In this paper, we describe a clustering system that can cluster news reports from disparate sources into event-centric clusters—i.e., clusters of news reports describing the same event. A user can identify any RSS feed as a source of news he/she would like to receive and our clustering system can cluster reports received from the separate RSS feeds as they arrive without knowing the number of clusters in advance. Our clustering system was designed to function well in an online incremental environment. In evaluating our system, we found that our system is very good in performing fine-grained clustering, but performs rather poorly when performing coarser-grained clustering.

Search Results Clustering without External Resources

Our unsupervised Search Results Clustering (SRC) system partitions into clusters the top-n results returned by a search engine. We present the results of experiments with our SRC system that performs incremental clustering on document titles and snippets only and does not use external resources, yet which outperforms the best performers to date on the SemEval-2013 Task 11 gold standard. We include Latent Semantic Analysis (LSA) as an optional step, using the snippets themselves as the background corpus. We demonstrate that better results are achieved by leaving the query terms out of the clustering process, and that currently, the version without LSA outperforms the version with LSA.

Fusion of News Reports Using Surface-Based Methods

Events occurring in the real world are covered by news reports from different sources. Each report generally contains information that is found in others, but may also contain unique information. To learn all the information about a particular event, a user will need to read all the different reports. This is a duplication of effort since most information will be repeated in the different reports. In our research, we attempt to fuse news reports about the same event into a single coherent document eliminating repetition but preserving all the information contained in the source reports using only surface-based methods. Information in each news report is represented by a set of entity relationship graphs. The graphs representing each report are then merged into a single graph whilst keeping track of the source sentences. The fused report is generated using the maximally expressive set of sentences -- the sentences that carry most information about the entities and their relationships in the news report, and ensuring that all entities and relationships are expressed in the fused document. Our Document fusion system was evaluated using a set of news reports downloaded from MSNBC News that cite their sources, and also using human evaluation. We show that our system is able to capture most of the information found across different source documents whilst maintaining readability.

Automatic classification of web pages into bookmark categories

We describe a technique to automatically classify a web page into an existing bookmark category to help a user to bookmark a page. HyperBK compares a bag-of-words representation of the page to descriptions of categories in the users bookmark file. Unlike default web browser dialog boxes in which the user may be presented with the category into which he or she saved the last bookmarked file, HyperBK also offers the category most similar to the page being bookmarked. The user can also opt to create a new category; or save the page elsewhere. In an evaluation, the users preferred category was offered on average 61% of the time.

The hypercontext framework for adaptive Hypertext

We present HyperContext, a framework for adaptive and adaptable hypertext. Our fundamental premise is that when people encounter the same document, each may interpret the information it contains differently. Usually, the interpretations are not available to future users of the same information. HyperContext permits users to make these interpretations explicit, and provides support to structure hyperspace around interpretations of documents, rather than around the documents themselves. When a user browses through hyperspace, a documents context is used to determine which interpretation to present to the user. We also derive a user model of the users short-term interests, by first representing the users interest in the current document as a salient interpretation before combining it with the salient interpretations of other documents accessed by the user on the same path of traversal. This paper describes the adaptive features of the HyperContext framework, and presents the results of an initial evaluation of one of the features.

Experiments with Document Retrieval from Small Text Collections Using Latent Semantic Analysis or Term Similarity with Query Coordination and Automatic Relevance Feedback

Users face the Vocabulary Gap problem when attempting to retrieve relevant textual documents from small databases, especially when there are only a small number of relevant documents, as it is likely that different terms are used in queries and relevant documents to describe the same concept. To enable comparison of results of different approaches to semantic search in small textual databases, the PIKES team constructed an annotated test collection and Gold Standard comprising 35 search queries and 331 articles. We present two different possible solutions. In one, we index an unannotated version of the PIKES collection using Latent Semantic Analysis (LSA) retrieving relevant documents using a combination of query coordination and automatic relevance feedback. Although we outperform prior work, this approach is dependent on the underlying collection, and is not necessarily scalable. In the second approach, we use an LSA Model generated by SEMILAR from a Wikipedia dump to generate a Term Similarity Matrix (TSM). Queries are automatically expanded with related terms from the TSM and are submitted to a term-by-document matrix Vector Space Model of the PIKES collection. Coupled with a combination of query coordination and automatic relevance feedback we also outperform prior work with this approach. The advantage of the second approach is that it is independent of the underlying document collection.

Automatic Adaptation and Recommendation of News Reports Using Surface-Based Methods

The multitude of news reports being published on the WWW may cause information overload on users. In this paper, we describe a news recommendation system whereby news reports are represented using entity-relationship graphs, and the users’ interaction with these news reports in a specialised web portal is monitored in order to construct and maintain user models that store the user’s reading history and also define entities that appear to be of interest to the user. These user models are used to alert individual users when an event has occurred that falls within their area of interest, and to present news reports to users in an adaptive manner – previously seen information is shown in a summarised form. We evaluated our recommendation system using a corpus of news reports downloaded from Yahoo! News. Results obtained indicate that our recommendation system performs better than the baseline system that uses the Rocchio algorithm without negative feedback.

Task-Based user modelling for knowledge work support

A Knowledge Worker (KW) uses her computer to perform different tasks for which she gathers and uses information from disparate sources such as the Web and e-mail, and creates new information such as calendar events, e-mails, and documents (resources) This forms a Task Space (TS): an information space composed of all computer-based resources the KW uses in relation to a task Furthermore, KWs may switch between multiple tasks, some of which may be suspended and resumed after some time These effects compound the KWs ability to organise and visualise an accurate mental model of the individual TSs We propose a Task-Based User Model (TBUM) that acts as the KWs mental model for each task by automatically tracking, relating and organising resources associated with that task The generated TBUM can be used to support complex activities such as task-resumption, searching within a task-context, task sharing and collaboration.

Bookmark Category Web Page Classification Using Four Indexing and Clustering Approaches

Web browser bookmark files store records of web pages that the user would like to revisit. We use four methods to index and automatically classify documents referred to in 80 bookmark files, based on document title-only and full-text indexing and two clustering approaches. We evaluate the approaches by selecting a bookmark entry to classify from a bookmark file, re-creating a snapshot of the bookmark file to contain only entries created before the selected bookmark entry. The baseline algorithm is 39% accurate at rank 1 when the target category contains 7 entries. By fusing the recommendations of the 4 approaches, we reach 78.7% accuracy on average, recommending at most 3 categories.

HyperContext: A Model for Adaptive Hypertext

HyperContext is a 3-layer model for adaptive hypertext. The model supports the use of multiple interpretations of objects of information. Whenever an object is accessed, HyperContext determines the context in which the access has taken place and presents the appropriate interpretation to the user.