Modeling Social Annotation: a Bayesian Approach

Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users, can potentially be used to infer categorical knowledge, classify documents or recommend new relevant information. Traditional text inference methods do not make best use of social annotation, since they do not take into account variations in individual users' perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from Delicious in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.Comment: 29 Pages, Accepted for publication at ACM Transactions on Knowledge Discovery from Data(TKDD) on March 2, 201

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

Identification of Informativeness in Text using Natural Language Stylometry

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned a number of solutions to identify informativeness in text. Nevertheless, a shortfall of most of these solutions is their dependency on the genre and domain of the text. In addition, most of them are not efficient regardless of the natural language processing problem areas. Therefore, we attempt to provide a more general solution to this NLP problem. This thesis takes a different approach to this problem by considering the underlying theme of a linguistic theory known as the Code Quantity Principle. This theory suggests that humans codify information in text so that readers can retrieve this information more efficiently. During the codification process, humans usually change elements of their writing ranging from characters to sentences. Examples of such elements are the use of simple words, complex words, function words, content words, syllables, and so on. This theory suggests that these elements have reasonable discriminating strength and can play a key role in distinguishing informativeness in natural language text. In another vein, Stylometry is a modern method to analyze literary style and deals largely with the aforementioned elements of writing. With this as background, we model text using a set of stylometric attributes to characterize variations in writing style present in it. We explore their effectiveness to determine informativeness in text. To the best of our knowledge, this is the first use of stylometric attributes to determine informativeness in statistical NLP. In doing so, we use texts of different genres, viz., scientific papers, technical reports, emails and newspaper articles, that are selected from assorted domains like agriculture, physics, and biomedical science. The variety of NLP systems that have benefitted from incorporating these stylometric attributes somewhere in their computational realm dealing with this set of multifarious texts suggests that these attributes can be regarded as an effective solution to identify informativeness in text. In addition to the variety of text genres and domains, the potential of stylometric attributes is also explored in some NLP application areas---including biomedical relation mining, automatic keyphrase indexing, spam classification, and text summarization---where performance improvement is both important and challenging. The success of the attributes in all these areas further highlights their usefulness

Crowdsource Annotation and Automatic Reconstruction of Online Discussion Threads

Modern communication relies on electronic messages organized in the form of discussion threads. Emails, IMs, SMS, website comments, and forums are all composed of threads, which consist of individual user messages connected by metadata and discourse coherence to messages from other users. Threads are used to display user messages effectively in a GUI such as an email client, providing a background context for understanding a single message. Many messages are meaningless without the context provided by their thread. However, a number of factors may result in missing thread structure, ranging from user mistake (replying to the wrong message), to missing metadata (some email clients do not produce/save headers that fully encapsulate thread structure; and, conversion of archived threads from over repository to another may also result in lost metadata), to covert use (users may avoid metadata to render discussions difficult for third parties to understand). In the field of security, law enforcement agencies may obtain vast collections of discussion turns that require automatic thread reconstruction to understand. For example, the Enron Email Corpus, obtained by the Federal Energy Regulatory Commission during its investigation of the Enron Corporation, has no inherent thread structure. In this thesis, we will use natural language processing approaches to reconstruct threads from message content. Reconstruction based on message content sidesteps the problem of missing metadata, permitting post hoc reorganization and discussion understanding. We will investigate corpora of email threads and Wikipedia discussions. However, there is a scarcity of annotated corpora for this task. For example, the Enron Emails Corpus contains no inherent thread structure. Therefore, we also investigate issues faced when creating crowdsourced datasets and learning statistical models of them. Several of our findings are applicable for other natural language machine classification tasks, beyond thread reconstruction. We will divide our investigation of discussion thread reconstruction into two parts. First, we explore techniques needed to create a corpus for our thread reconstruction research. Like other NLP pairwise classification tasks such as Wikipedia discussion turn/edit alignment and sentence pair text similarity rating, email thread disentanglement is a heavily class-imbalanced problem, and although the advent of crowdsourcing has reduced annotation costs, the common practice of crowdsourcing redundancy is too expensive for class-imbalanced tasks. As the first contribution of this thesis, we evaluate alternative strategies for reducing crowdsourcing annotation redundancy for class-imbalanced NLP tasks. We also examine techniques to learn the best machine classifier from our crowdsourced labels. In order to reduce noise in training data, most natural language crowdsourcing annotation tasks gather redundant labels and aggregate them into an integrated label, which is provided to the classifier. However, aggregation discards potentially useful information from linguistically ambiguous instances. For the second contribution of this thesis, we show that, for four of five natural language tasks, filtering of the training dataset based on crowdsource annotation item agreement improves task performance, while soft labeling based on crowdsource annotations does not improve task performance. Second, we investigate thread reconstruction as divided into the tasks of thread disentanglement and adjacency recognition. We present the Enron Threads Corpus, a newly-extracted corpus of 70,178 multi-email threads with emails from the Enron Email Corpus. In the original Enron Emails Corpus, emails are not sorted by thread. To disentangle these threads, and as the third contribution of this thesis, we perform pairwise classification, using text similarity measures on non-quoted texts in emails. We show that i) content text similarity metrics outperform style and structure text similarity metrics in both a class-balanced and class-imbalanced setting, and ii) although feature performance is dependent on the semantic similarity of the corpus, content features are still effective even when controlling for semantic similarity. To reconstruct threads, it is also necessary to identify adjacency relations among pairs. For the forum of Wikipedia discussions, metadata is not available, and dialogue act typologies, helpful for other domains, are inapplicable. As our fourth contribution, via our experiments, we show that adjacency pair recognition can be performed using lexical pair features, without a dialogue act typology or metadata, and that this is robust to controlling for topic bias of the discussions. Yet, lexical pair features do not effectively model the lexical semantic relations between adjacency pairs. To model lexical semantic relations, and as our fifth contribution, we perform adjacency recognition using extracted keyphrases enhanced with semantically related terms. While this technique outperforms a most frequent class baseline, it fails to outperform lexical pair features or tf-idf weighted cosine similarity. Our investigation shows that this is the result of poor word sense disambiguation and poor keyphrase extraction causing spurious false positive semantic connections. In concluding this thesis, we also reflect on open issues and unanswered questions remaining after our research contributions, discuss applications for thread reconstruction, and suggest some directions for future work

From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing

Recent improvements in information and communication technologies have contributed to an increasingly globalized and connected world. The digital data that are created as the result of people's online activities and interactions consist of different types of personal and social information that can be used to extract and understand people's implicit or explicit beliefs, ideas, and biases. This thesis leverages methods and theories from natural language processing and social sciences to study and analyze the manifestations of various attributes and signals, namely social impacts, personal values, and moral traits, in user-generated texts. This work provides a comprehensive understanding of people's viewpoints, social values, and interactions and makes the following contributions. First, we present a study that combines review mining and impact assessment to provide an extensive discussion on different types of impact that information products, namely documentary films, can have on people. We first establish a novel impact taxonomy and demonstrate that, with a rigorous analysis of user-generated texts and a theoretically grounded codebook, classification schema, and prediction model, we can detect multiple types of (self-reported) impact in texts and show that people's language can help in gaining insights about their opinions, socio-cultural information, and emotional states. Furthermore, the results of our analyses show that documentary films can shift peoples' perceptions and cognitions regarding different societal issues, e.g., climate change, and using a combination of informative features (linguistic, syntactic, and psychological), we can predict impact in sentences with high accuracy. Second, we investigate the relationship between principles of human morality and the expression of stances in user-generated text data, namely tweets. More specifically, we first introduce and expand the Moral Foundations Dictionary and operationalize moral values to enhance the measurement of social effects. In addition, we provide detailed explanation on how morality and stance are associated in user-generated texts. Through extensive analysis, we show that discussions related to various social issues have distinctive moral and lexical profiles, and leveraging moral values as an additional feature can lead to measurable improvements in prediction accuracy of stance analysis. Third, we utilize the representation of emotional and moral states in texts to study people's interactions in two different social networks. Moreover, we first expand the analysis of structural balance to include direction and multi-level balance assessment (triads, subgroups, and the whole network). Our results show that analyzing different levels of networks and using various linguistic cues can grant a more inclusive view of people and the stability of their interactions; we found that, unlike sentiments, moral statuses in discussions stay balanced throughout the networks even in the presence of tension. Overall, this thesis aims to contribute to the emerging field of "social" NLP and broadens the scope of research in it by (1) utilizing a combination of novel taxonomies, datasets, and tools to examine user-generated texts and (2) providing more comprehensive insights about human language, cultures, and experiences

Generating and applying textual entailment graphs for relation extraction and email categorization

Recognizing that the meaning of one text expression is semantically related to the meaning of another can be of help in many natural language processing applications. One semantic relationship between two text expressions is captured by the textual entailment paradigm, which is defined as a relation between exactly two text expressions. Entailment relations holding among a set of more than two text expressions can be captured in the form of a hierarchical knowledge structure referred to as entailment graphs. Despite the fact that several people have worked on building entailment graphs for different types of textual expressions, little research has been carried out regarding the applicability of such entailment graphs in NLP applications. This thesis fills this research gap by investigating how entailment graphs can be generated and used for addressing two specific NLP tasks: First, the task of validating automatically derived relation extraction patterns and, second, the task of automatically categorizing German customer emails. After laying a theoretical foundation, the research problem is approached in an empirical way, i.e., by drawing conclusions from analyzing, processing, and experimenting with specific task-related datasets. The experimental results show that both tasks can benefit from the integration of semantic knowledge, as expressed by entailment graphs