Representing semantic relatedness

University of Technology Sydney. Faculty of Engineering and Information Technology.To do text mining, the first question we must address is how to represent documents. The way a document is organised reflects certain explicit and implicit semantic and syntactical coupling relationships which are embedded in its contents. The effective capturing of such content couplings is thereby crucial for a genuine understanding of text representations. It has also led to the recent interest in document similarity analysis, including semantic relatedness, content coverage, word networking, and term-term couplings. Document similarity analysis has become increasingly relevant since roughly 80% of big data is unstructured. Accordingly, semantic relatedness has generated much interest owing to its ability to extract coupling relationships between terms (words or phrases). Existing work has focused more on explicit couplings and this is reflected in the models that have been built. In order to address the research limitations and challenges associated with document similarity analysis, this thesis proposes a semantic coupling similarity measure and the hierarchical tree learning model to fully enrich the semantics within terms and documents, and represent documents based on the comprehensive couplings of term pairs. In contrast to previous work, the models proposed can deal with unstructured data and terms that are coupled for various reasons, thereby addressing natural language ambiguity problems. Chapter 3 explores the semantic couplings of pairwise terms by involving three types of coupling relationships: (1) intra-term pair couplings, reflecting the explicit relatedness within term pairs that is represented by the relation strength over probabilistic distribution of terms across document collection; (2) the inter-term pair couplings, capturing the implicit relatedness between term pairs by considering the relation strength of their interactions with other term pairs on all possible paths via a graph-based representation of term couplings; and finally, (3) semantic coupling similarity, which effectively combine the intra- and inter-term couplings. The corresponding term semantic similarity measures are then defined to capture such couplings for the purposes of analysing term and document similarity. This approach effectively addresses both synonymy (many words per sense) and polysemy (many senses per word) in a graphical representation, two areas that have up until now been overlooked by previous models. Chapter 4 constructs a hierarchical tree-like structure to extract highly correlated terms in a layerwise fashion and to prune weak correlations in order to maintain efficiency. In keeping with the hierarchical tree-like structure, a hierarchical tree learning method is proposed. The main contributions of our work lie in three areas: (1) the hierarchical tree-like structure featuring hierarchical feature extraction and correlation computation procedures whereby highly correlated terms are merged into sets, and these are associated with more complete semantic information; (2) each layer is a maximal weighted spanning tree to prune weak feature correlations; (3) the hierarchical treelike structure can be applied to both supervised and unsupervised learning approaches. In this thesis, the tree is associated with Tree Augmented Naive Bayes (TAN) as the Hierarchical Tree Augmented Naive Bayes (HTAN). All of these models can be applied in the text mining tasks, including document clustering and text classification. The performance of the semantic coupling similarity measure is compared with typical document representation models on various benchmark data sets in terms of document clustering and classification evaluation metrics. These models provide insightful knowledge to organise and retrieve documents

Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection

Detecting whether a news article is fake or genuine is a crucial task in today's digital world where it's easy to create and spread a misleading news article. This is especially true of news stories shared on social media since they don't undergo any stringent journalistic checking associated with main stream media. Given the inherent human tendency to share information with their social connections at a mouse-click, fake news articles masquerading as real ones, tend to spread widely and virally. The presence of echo chambers (people sharing same beliefs) in social networks, only adds to this problem of wide-spread existence of fake news on social media. In this paper, we tackle the problem of fake news detection from social media by exploiting the very presence of echo chambers that exist within the social network of users to obtain an efficient and informative latent representation of the news article. By modeling the echo-chambers as closely-connected communities within the social network, we represent a news article as a 3-mode tensor of the structure - and propose a tensor factorization based method to encode the news article in a latent embedding space preserving the community structure. We also propose an extension of the above method, which jointly models the community and content information of the news article through a coupled matrix-tensor factorization framework. We empirically demonstrate the efficacy of our method for the task of Fake News Detection over two real-world datasets. Further, we validate the generalization of the resulting embeddings over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and \textbf{2)} Collaborative News Recommendation. Our proposed method outperforms appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1

Automatic organisation of retrieved images into a hierarchy

Image retrieval is of growing interest to both search engines and academic researchers with increased focus on both content-based and caption-based approaches. Image search, however, is different from document retrieval: users often search a broader set of retrieved images than they would examine returned web pages in a search engine. In this paper, we focus on a concept hierarchy generation approach developed by Sanderson and Croft in 1999, which was used to organise retrieved images in a hierarchy automatically generated from image captions. Thirty participants were recruited for the study. Each of them conducted two different kinds of searching tasks within the system. Results indicated that the user retrieval performance in both interfaces of system is similar. However, the majority of users preferred to use the concept hierarchy to complete their searching tasks and they were satisfied with using the hierarchical menu to organize retrieved results, because the menu appeared to provide a useful summary to help users look through the image results

Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application

We present two novel models of document coherence and their application to information retrieval (IR). Both models approximate document coherence using discourse entities, e.g. the subject or object of a sentence. Our first model views text as a Markov process generating sequences of discourse entities (entity n-grams); we use the entropy of these entity n-grams to approximate the rate at which new information appears in text, reasoning that as more new words appear, the topic increasingly drifts and text coherence decreases. Our second model extends the work of Guinaudeau & Strube [28] that represents text as a graph of discourse entities, linked by different relations, such as their distance or adjacency in text. We use several graph topology metrics to approximate different aspects of the discourse flow that can indicate coherence, such as the average clustering or betweenness of discourse entities in text. Experiments with several instantiations of these models show that: (i) our models perform on a par with two other well-known models of text coherence even without any parameter tuning, and (ii) reranking retrieval results according to their coherence scores gives notable performance gains, confirming a relation between document coherence and relevance. This work contributes two novel models of document coherence, the application of which to IR complements recent work in the integration of document cohesiveness or comprehensibility to ranking [5, 56]