Adaptive text mining: Inferring structure from sequences

Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively

A Context Centric Model for building a Knowledge advantage Machine Based on Personal Ontology Patterns

Throughout the industrial era societal advancement could be attributed in large part to introduction a plethora of electromechanical machines all of which exploited a key concept known as Mechanical Advantage. In the post-industrial era exploitation of knowledge is emerging as the key enabler for societal advancement. With the advent of the Internet and the Web, while there is no dearth of knowledge, what is lacking is an efficient and practical mechanism for organizing knowledge and presenting it in a comprehensible form appropriate for every context. This is the fundamental problem addressed by my dissertation.;We begin by proposing a novel architecture for creating a Knowledge Advantage Machine (KaM), one which enables a knowledge worker to bring to bear a larger amount of knowledge to solve a problem in a shorter time. This is analogous to an electromechanical machine that enables an industrial worker to bring to bear a large amount of power to perform a task thus improving worker productivity. This work is based on the premise that while a universal KaM is beyond the realm of possibility, a KaM specific to a particular type of knowledge worker is realizable because of the limited scope of his/her personal ontology used to organize all relevant knowledge objects.;The proposed architecture is based on a society of intelligent agents which collaboratively discover, markup, and organize relevant knowledge objects into a semantic knowledge network on a continuing basis. This in-turn is exploited by another agent known as the Context Agent which determines the current context of the knowledge worker and makes available in a suitable form the relevant portion of the semantic network. In this dissertation we demonstrate the viability and extensibility of this architecture by building a prototype KaM for one type of knowledge worker such as a professor

Enhancing Feature Extraction through G-PLSGLR by Decreasing Dimensionality of Textual Data

The technology of big data has become highly popular in numerous industries owing to its various characteristics such as high value, large volume, rapid velocity, wide variety, and significant variability. Nevertheless, big data presents several difficulties that must be addressed, including lengthy processing times, high computational complexity, imprecise features, significant sparsity, irrelevant terms, redundancy, and noise, all of which can have an adverse effect on the performance of feature extraction. The objective of this research is to tackle these issues by utilizing the Partial Least Square Generalized Linear Regression (G-PLSGLR) approach to decrease the high dimensionality of text data. The suggested algorithm is made up of four stages: Firstly, gathering featured data in vector space model (VSM) and training it with bootstrap technique. Second, grouping trained feature samples using a Pearson correlation coefficient and graph-based technique. Third, getting rid of unimportant features by ranking significant group features using PLSGR. Lastly, choosing or extracting significant features using Bayesian information criterion (BIC). The G-PLSGLR algorithm surpasses current methods by achieving a high reduction rate and classification performance, while minimizing feature redundancy, time consumption, and complexity. Furthermore, it enhances the accuracy of features by 35%

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Distributed Document Clustering and Cluster Summarization in Peer-to-Peer Environments

This thesis addresses difficult challenges in distributed document clustering and cluster summarization. Mining large document collections poses many challenges, one of which is the extraction of topics or summaries from documents for the purpose of interpretation of clustering results. Another important challenge, which is caused by new trends in distributed repositories and peer-to-peer computing, is that document data is becoming more distributed. We introduce a solution for interpreting document clusters using keyphrase extraction from multiple documents simultaneously. We also introduce two solutions for the problem of distributed document clustering in peer-to-peer environments, each satisfying a different goal: maximizing local clustering quality through collaboration, and maximizing global clustering quality through cooperation. The keyphrase extraction algorithm efficiently extracts and scores candidate keyphrases from a document cluster. The algorithm is called CorePhrase and is based on modeling document collections as a graph upon which we can leverage graph mining to extract frequent and significant phrases, which are used to label the clusters. Results show that CorePhrase can extract keyphrases relevant to documents in a cluster with very high accuracy. Although this algorithm can be used to summarize centralized clusters, it is specifically employed within distributed clustering to both boost distributed clustering accuracy, and to provide summaries for distributed clusters. The first method for distributed document clustering is called collaborative peer-to-peer document clustering, which models nodes in a peer-to-peer network as collaborative nodes with the goal of improving the quality of individual local clustering solutions. This is achieved through the exchange of local cluster summaries between peers, followed by recommendation of documents to be merged into remote clusters. Results on large sets of distributed document collections show that: (i) such collaboration technique achieves significant improvement in the final clustering of individual nodes; (ii) networks with larger number of nodes generally achieve greater improvements in clustering after collaboration relative to the initial clustering before collaboration, while on the other hand they tend to achieve lower absolute clustering quality than networks with fewer number of nodes; and (iii) as more overlap of the data is introduced across the nodes, collaboration tends to have little effect on improving clustering quality. The second method for distributed document clustering is called hierarchically-distributed document clustering. Unlike the collaborative model, this model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as a hierarchy of node neighborhoods. Summarization of the global distributed clusters is achieved through a distributed version of the CorePhrase algorithm. Results on large document sets show that: (i) distributed clustering accuracy is not affected by increasing the number of nodes for networks of single level; (ii) we can achieve decent speedup by making the hierarchy taller, but on the expense of clustering quality which degrades as we go up the hierarchy; (iii) in networks that grow arbitrarily, data gets more fragmented across neighborhoods causing poor centroid generation, thus suggesting we should not increase the number of nodes in the network beyond a certain level without increasing the data set size; and (iv) distributed cluster summarization can produce accurate summaries similar to those produced by centralized summarization. The proposed algorithms offer high degree of flexibility, scalability, and interpretability of large distributed document collections. Achieving the same results using current methodologies require centralization of the data first, which is sometimes not feasible

Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing

Mental illness is a serious health problem and it affects many people. Increasingly,Clinical Decision Support Systems (CDSS) are being used for diagnosis and it is important to improve the reliability and performance of these systems. Missing a potential clue or a wrong diagnosis can have a detrimental effect on the patient's quality of life and could lead to a fatal outcome. The context of this research is the Galatean Risk and Safety Tool (GRiST), a mental-health-risk assessment system. Previous research has shown that success of a CDSS depends on its ease of use, reliability and interactivity. This research addresses these concerns for the GRiST by deploying data mining techniques. Clinical narratives and numerical data have both been analysed for this purpose.Clinical narratives have been processed by natural language processing (NLP)technology to extract knowledge from them. SNOMED-CT was used as a reference ontology and the performance of the different extraction algorithms have been compared. A new Ensemble Concept Mining (ECM) method has been proposed, which may eliminate the need for domain specific phrase annotation requirements. Word embedding has been used to filter phrases semantically and to build a semantic representation of each of the GRiST ontology nodes.The Chi-square and FP-growth methods have been used to find relationships between GRiST ontology nodes. Interesting patterns have been found that could be used to provide real-time feedback to clinicians. Information gain has been used efficaciously to explain the differences between the clinicians and the consensus risk. A new risk management strategy has been explored by analysing repeat assessments. A few novel methods have been proposed to perform automatic background analysis of the patient data and improve the interactivity and reliability of GRiST and similar systems