David N. Milne

Mining meaning from Wikipedia

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.

An open-source toolkit for mining Wikipedia

The online encyclopedia Wikipedia is a vast, constantly evolving tapestry of interlinked articles. For developers and researchers it represents a giant multilingual database of concepts and semantic relations, a potential resource for natural language processing and many other research areas. This paper introduces the Wikipedia Miner toolkit, an open-source software system that allows researchers and developers to integrate Wikipedia@?s rich semantics into their own applications. The toolkit creates databases that contain summarized versions of Wikipedia@?s content and structure, and includes a Java API to provide access to them. Wikipedia@?s articles, categories and redirects are represented as classes, and can be efficiently searched, browsed, and iterated over. Advanced features include parallelized processing of Wikipedia dumps, machine-learned semantic relatedness measures and annotation features, and XML-based web services. Wikipedia Miner is intended to be a platform for sharing data mining techniques.

A knowledge-based search engine powered by wikipedia

This paper describes Koru, a new search interface that offers effective domain-independent knowledge-based information retrieval. Koru exhibits an understanding of the topics of both queries and documents. This allows it to (a) expand queries automatically and (b) help guide the user as they evolve their queries interactively. Its understanding is mined from the vast investment of manual effort and judgment that is Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We conducted a detailed user study with 12 participants and 10 topics from the 2005 TREC HARD track, and found that Koru and its underlying knowledge base offers significant advantages over traditional keyword search. It was capable of lending assistance to almost every query issued to it; making their entry more efficient, improving the relevance of the documents they return, and narrowing the gap between expert and novice seekers.

Mining Domain-Specific Thesauri from Wikipedia: A Case Study

Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia. In a comparison with a professional thesaurus for agriculture we find that Wikipedia contains a substantial proportion of its concepts and semantic relations; furthermore it has impressive coverage of contemporary documents in the domain. Thesauri derived using our techniques capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts

Clustering Documents with Active Learning Using Wikipedia

Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts; and adding constraints improves clustering performance further by up to 20%.

Clustering Documents Using a Wikipedia-Based Concept Representation

This paper shows how Wikipedia and the semantic knowledge it contains can be exploited for document clustering. We first create a concept-based document representation by mapping the terms and phrases within documents to their corresponding articles (or concepts) in Wikipedia. We also developed a similarity measure that evaluates the semantic relatedness between concept sets for two documents. We test the concept-based representation and the similarity measure on two standard text document datasets. Empirical results show that although further optimizations could be performed, our approach already improves upon related techniques.

Learning a concept-based document similarity measure

Document similarity measures are crucial components of many text-analysis tasks, including information retrieval, document classification, and document clustering. Conventional measures are brittle: They estimate the surface overlap between documents based on the words they mention and ignore deeper semantic connections. We propose a new measure that assesses similarity at both the lexical and semantic levels, and learns from human judgments how to combine them by using machine-learning techniques. Experiments show that the new measure produces values for documents that are more consistent with peoples judgments than people are with each other. We also use it to classify and cluster large document sets covering different genres and topics, and find that it improves both classification and clustering performance.

CLPsych 2016 Shared Task: Triaging content in online peer-support forums.

This paper introduces a new shared task for the text mining community. It aims to directly support the moderators of a youth mental health forum by asking participants to automatically triage posts into one of four severity labels: green, amber, red or crisis. The task attracted 60 submissions from 15 different teams, the best of whom achieve scores well above baselines. Their approaches and results provide valuable insights to enable moderators of peer support forums to react quickly to the most urgent, concerning content.

A competitive environment for exploratory query expansion

Most information workers query digital libraries many times a day. Yet people have little opportunity to hone their skills in a controlled environment, or compare their performance with others in an objective way. Conversely, although search engine logs record how users evolve queries, they lack crucial information about the users intent. This paper describes an environment for exploratory query expansion that pits users against each other and lets them compete, and practice, in their own time and on their own workstation. The system captures query evolution behavior on predetermined information-seeking tasks. It is publicly available, and the code is open source so that others can set up their own competitive environments.

Using different Facebook advertisements to recruit men for an online mental health study: Engagement and selection bias

A growing number of researchers are using Facebook to recruit for a range of online health, medical, and psychosocial studies. There is limited research on the representativeness of participants recruited from Facebook, and the content is rarely mentioned in the methods, despite some suggestion that the advertisement content affects recruitment success. This study explores the impact of different Facebook advertisement content for the same study on recruitment rate, engagement, and participant characteristics. Five Facebook advertisement sets (“resilience”, “happiness”, “strength”, “mental fitness”, and “mental health”) were used to recruit male participants to an online mental health study which allowed them to find out about their mental health and wellbeing through completing six measures. The Facebook advertisements recruited 372 men to the study over a one month period. The cost per participant from the advertisement sets ranged from