Growing a list

It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: Information can be scattered across many sources and must be aggregated to be useful. We introduce a method for intelligently growing a list of relevant items, starting from a small seed of examples. Our algorithm takes advantage of the wisdom of the crowd, in the sense that there are many experts who post lists of things on the Internet. We use a collection of simple machine learning components to find these experts and aggregate their lists to produce a single complete and meaningful list. We use experiments with gold standards and open-ended experiments without gold standards to show that our method significantly outperforms the state of the art. Our method uses the clustering algorithm Bayesian Sets even when its underlying independence assumption is violated, and we provide a theoretical generalization bound to motivate its use.

The detection of fraud activities on the stock market through forward analysis methodology of financial discussion boards

Financial discussion boards (FDBs) have been widely used for a variety of financial knowledge exchange activities through the posting of comments on the FDBs. Popular public FDBs are prone to be used as a medium to spread false financial information due to having a larger group of audiences. Although online forums, in general, are usually integrated with anti-spam tools such as Akismet, moderation of posted contents heavily relies on human moderators. Unfortunately, popular FDBs attract many comments per day which realistically prevents human moderators from continuously monitoring and moderating possibly fraudulent contents. Such manual moderation can be extremely time-consuming. Moreover, due to the absence of useful tools, no relevant authorities are actively monitoring and handling potential financial crimes on FDBs. This paper presents a novel forward analysis methodology implemented in an Information Extraction (IE) prototype system named FDBs Miner (FDBM). This methodology aims to detect potentially illegal comments on FDBs while integrating share prices in the detection process as this helps to categorise the potentially illegal comments into different risk levels for investigation priority. The IE prototype system will first extract the public comments and per minute share prices from FDBs for the selected listed companies on London Stock Exchange (LSE). In the forward analysis process, the comments are flagged using a predefined Pump and Dump financial crime related keyword template. By only flagging the comments against the keyword template yields an average of 9.82% potentially illegal comments. It is unrealistic and unaffordable for human moderators to read these comments on a daily basis in long run. Hence, by integrating the share prices’ hikes and falls to categorise the flagged comments based on risk levels, it saves time and allows relevant authorities to prioritise and investigate into the higher risk flagged comments as it can potentially indicate real Pump and Dump crimes on FDBs

An Analysis of How Interactive Technology Supports the Appreciation of Traditional Chinese Puppetry: A Review of Case Studies

From the perspective of safeguarding Chinese Cultural Heritage, this paper discusses how to enhance the appreciation of traditional Chinese puppetry through the support of interactive technology. The author analyses extensive, yet current case studies, based on the findings described in the interactive systems for puppetry performances and interactive technology for puppetry appreciation. The author summarises four aspects of how to enhance the appreciation of, and engagement with, traditional Chinese puppetry: (1) maintaining originality is necessary for the design phase; (2) it is crucial to explore how to use interactive technology in order to design a way for adults to appreciate this form of art; (3) it is also necessary to determine ways to support adult audiences in grasping the cultural significance and folk customs of traditional Chinese puppetry; and (4) the study’s further main research goals are to investigate ways to use emotional expressions, digital storytelling and other methods in conjunction with interactive technology to help multi-cultural users comprehend traditional Chinese puppetry

A Methodological Reflection: Deconstructing Cultural Elements for Enhancing Cross-cultural Appreciation of Chinese Intangible Cultural Heritage

This paper presents a practical method of deconstructing cultural elements based on the Human Computer Interaction (HCI) perspective to enhance cross-cultural appreciation of Chinese Intangible Cultural Heritage (ICH). The author pioneered this approach during conducting two case studies as a means to enhance appreciation and engagement with Chinese ICH, such as the extraction of elements from traditional Chinese painting and puppetry with potential to support cross-cultural appreciation, as well as the establishment of an elements archive. Through integrating a series of HCI research methods, this approach provides a specific foundational framework that assists non-Chinese people to better understand the cultural significance of Chinese ICH

Preparation of name and address data for record linkage using hidden Markov models

BACKGROUND: Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs). METHODS: HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems. RESULTS: Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed. CONCLUSION: Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve

Enriching a biomedical event corpus with meta-knowledge annotation

Background: Biomedical papers contain rich information about entities, facts and events of biological relevance. To discover these automatically, we use text mining techniques, which rely on annotated corpora for training. In order to extract protein-protein interactions, genotype-phenotype/gene-disease associations, etc., we rely on event corpora that are annotated with classified, structured representations of important facts and findings contained within text. These provide an important resource for the training of domain-specific information extraction (IE) systems, to facilitate semantic-based searching of documents. Correct interpretation of these events is not possible without additional information, e.g., does an event describe a fact, a hypothesis, an experimental result or an analysis of results? How confident is the author about the validity of her analyses? These and other types of information, which we collectively term meta-knowledge, can be derived from the context of the event.Results: We have designed an annotation scheme for meta-knowledge enrichment of biomedical event corpora. The scheme is multi-dimensional, in that each event is annotated for 5 different aspects of meta-knowledge that can be derived from the textual context of the event. Textual clues used to determine the values are also annotated. The scheme is intended to be general enough to allow integration with different types of bio-event annotation, whilst being detailed enough to capture important subtleties in the nature of the meta-knowledge expressed in the text. We report here on both the main features of the annotation scheme, as well as its application to the GENIA event corpus (1000 abstracts with 36,858 events). High levels of inter-annotator agreement have been achieved, falling in the range of 0.84-0.93 Kappa.Conclusion: By augmenting event annotations with meta-knowledge, more sophisticated IE systems can be trained, which allow interpretative information to be specified as part of the search criteria. This can assist in a number of important tasks, e.g., finding new experimental knowledge to facilitate database curation, enabling textual inference to detect entailments and contradictions, etc. To our knowledge, our scheme is unique within the field with regards to the diversity of meta-knowledge aspects annotated for each event. © 2011 Thompson et al; licensee BioMed Central Ltd