Summarization of Scientific Paper through Reinforcement Ranking on Semantic Link Network

The Semantic Link Network is a semantics modeling method for effective information services. This paper proposes a new text summarization approach that extracts Semantic Link Network from scientific paper consisting of language units of different granularities as nodes and semantic links between the nodes, and then ranks the nodes to select Top-k sentences to compose summary. A set of assumptions for reinforcing representative nodes is set to reflect the core of paper. Then, Semantic Link Networks with different types of node and links are constructed with different combinations of the assumptions. Finally, an iterative ranking algorithm is designed for calculating the weight vectors of the nodes in a converged iteration process. The iteration approximately approaches a stable weight vector of sentence nodes, which is ranked to select Top-k high-rank nodes for composing summary. We designed six types of ranking models on Semantic Link Networks for evaluation. Both objective assessment and intuitive assessment show that ranking Semantic Link Network of language units can significantly help identify the representative sentences. This work not only provides a new approach to summarizing text based on extraction of semantic links from text but also verifies the effectiveness of adopting the Semantic Link Network in rendering the core of text. The proposed approach can be applied to implementing other summarization applications such as generating an extended abstract, the mind map and the bulletin points for making the slides of a given paper. It can be easily extended by incorporating more semantic links to improve text summarization and other information services

Probabilistic inference on uncertain semantic link network and its application in event identification

The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model

Semantic linking through spaces for cyber-physical-socio intelligence:a methodology

Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space — the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP3SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP3SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing

Improving Search Ranking Using a Composite Scoring Approach

In this thesis, the improvement to relevance in computerized search results is studied. Information search tools return ranked lists of documents ordered by the relevance of the documents to the user supplied search. Using a small number of words and phrases to represent complex ideas and concepts causes user search queries to be information sparse. This sparsity challenges search tools to locate relevant documents for users. A review of the challenges to information searches helps to identify the problems and offer suggestions in improving current information search tools. Using the suggestions put forth by the Strategic Workshop on Information Retrieval in Lorne (SWIRL), a composite scoring approach (Composite Scorer) is developed. The Composite Scorer considers various aspects of information needs to improve the ranked results of search by returning records relevant to the user’s information need. The Florida Fusion Center (FFC), a local law enforcement agency has a need for a more effective information search tool. Daily, the agency processes large amounts of police reports typically written as text documents. Current information search methods require inordinate amounts of time and skill to identify relevant police reports from their large collection of police reports. An experiment conducted by FFC investigators contrasted the composite scoring approach against a common search scoring approach (TF/IDF). In the experiment, police investigators used a custom-built software interface to conduct several use case scenarios for searching for related documents to various criminal investigations. Those expert users then evaluated the results of the top ten ranked documents returned from both search scorers to measure the relevance to the user of the returned documents. The evaluations were collected and measurements used to evaluate the performance of the two scorers. A search with many irrelevant documents has a cost to the users in both time and potentially in unsolved crimes. A cost function contrasted the difference in cost between the two scoring methods for the use cases. Mean Average Precision (MAP) is a common method used to evaluate the performance of ranked list search results. MAP was computed for both scoring methods to provide a numeric value representing the accuracy of each scorer at returning relevant documents in the top-ten documents of a ranked list of search results. The purpose of this study is to determine if a composite scoring approach to ranked lists, that considers multiple aspects of a user’s search, can improve the quality of search, returning greater numbers of relevant documents during an information search. This research contributes to the understanding of composite scoring methods to improve search results. Understanding the value of composite scoring methods allows researchers to evaluate, explore and possibly extend the approach, incorporating other information aspects such as word and document meaning