Intelligent Agents for Retrieving Chinese Web Financial News

As the popularity of World Wide Web increases, many newspapers expand their services by providing news information on the Web in order to be competitive and increase benefit. The Web provides real time dissemination of financial news to investors. However, most investors find it difficult to search for the financial information of interest from the huge Web information space. Most of the commercial search engines are not user friendly and do not provide any tailor-made intelligent agents to search for relevant Web documents on behalf of users. Users have to exert a lot of effort to submit an appropriate query to obtain the information they want. Intelligent agents that learn user preferences and monitor the postings of Web information providers are desired. In this paper, we present an intelligent agent that utilizes user profiles and user feedback to search for the Chinese Web financial news articles on behalf of users. A Chinese indexing component is developed to index the continuously fetched Chinese financial news articles. User profiles capture the basic knowledge of user preferences based on the sources of news articles, the regions of the news reported, categories of industries related, the listed companies, and user specified keywords. User feedback captures the semantics of the user rated news articles. The search engine will rank the top 20 news articles that users are most interested in based on these inputs. Experiments were conducted to measure the performance of the agents based on the inputs from user profile and user feedback

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Arabic Query Expansion Using WordNet and Association Rules

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, we review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, we are able to exploit Arabic WordNet to improve the retrieval performance. Our empirical results on a sub-corpus from the Xinhua collection showed that our automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Representativeness and face-ism: Gender bias in image search

Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue

Automatic Concept Extraction in Semantic Summarization Process

The Semantic Web offers a generic infrastructure for interchange, integration and creative reuse of structured data, which can help to cross some of the boundaries that Web 2.0 is facing. Currently, Web 2.0 offers poor query possibilities apart from searching by keywords or tags. There has been a great deal of interest in the development of semantic-based systems to facilitate knowledge representation and extraction and content integration [1], [2]. Semantic-based approach to retrieving relevant material can be useful to address issues like trying to determine the type or the quality of the information suggested from a personalized environment. In this context, standard keyword search has a very limited effectiveness. For example, it cannot filter for the type of information, the level of information or the quality of information. Potentially, one of the biggest application areas of content-based exploration might be personalized searching framework (e.g., [3],[4]). Whereas search engines provide nowadays largely anonymous information, new framework might highlight or recommend web pages related to key concepts. We can consider semantic information representation as an important step towards a wide efficient manipulation and retrieval of information [5], [6], [7]. In the digital library community a flat list of attribute/value pairs is often assumed to be available. In the Semantic Web community, annotations are often assumed to be an instance of an ontology. Through the ontologies the system will express key entities and relationships describing resources in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and object recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content search and retrieval. Text summarization has been an interesting and active research area since the 60’s. The definition and assumption are that a small portion or several keywords of the original long document can represent the whole informatively and/or indicatively. Reading or processing this shorter version of the document would save time and other resources [8]. This property is especially true and urgently needed at present due to the vast availability of information. Concept-based approach to represent dynamic and unstructured information can be useful to address issues like trying to determine the key concepts and to summarize the information exchanged within a personalized environment. In this context, a concept is represented with a Wikipedia article. With millions of articles and thousands of contributors, this online repository of knowledge is the largest and fastest growing encyclopedia in existence. The problem described above can then be divided into three steps: • Mapping of a series of terms with the most appropriate Wikipedia article (disambiguation). • Assigning a score for each item identified on the basis of its importance in the given context. • Extraction of n items with the highest score. Text summarization can be applied to many fields: from information retrieval to text mining processes and text display. Also in personalized searching framework text summarization could be very useful. The chapter is organized as follows: the next Section introduces personalized searching framework as one of the possible application areas of automatic concept extraction systems. Section three describes the summarization process, providing details on system architecture, used methodology and tools. Section four provides an overview about document summarization approaches that have been recently developed. Section five summarizes a number of real-world applications which might benefit from WSD. Section six introduces Wikipedia and WordNet as used in our project. Section seven describes the logical structure of the project, describing software components and databases. Finally, Section eight provides some consideration..

Research on Personalized Recommender System for Tourism Information Service

Since the development in the 1990s, Recommender system has been widely applied in various fields. The conflict between the expansion of tourism information and difficulty of tourists obtaining tourism information allows Tourism Information Recommender System to have a practical significance. Based on the existing online tourism information service and the mature recommendation algorithms, Personal Recommender System can be used to solve present problems of the key recommendation algorithms. In the first place, this research presents an overview of researches on this issue both at home and abroad, and analyzes the applications of main stream recommendation algorithms. Secondly, a comparative study of domestic and international tourism information service websites is conducted. Drawbacks in their applications are defined and advantages are adopted in the settings of Recommender System. Finally, this research provides the framework of Recommender System, which combines the design and test of algorithms and the existing tourism information recommendation websites. This system allows customers to broaden experience of tourism information service and make tourism decisions more accurately and rapidly. Keywords: Tourism information service, Personalized recommendation, Intelligence recommendation module, Apriori algorith

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research