Simple Keyword Matching Research Articles

Evaluation of different machine learning approaches and input text representations for multilingual classification of tweets for disease surveillance in the social web

Twitter and social media as a whole have great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Most deployed systems employ approaches that rely on simple keyword matching and do not distinguish between relevant and irrelevant keyword mentions making them susceptible to false positives as a result of the fact that keyword volume can be influenced by several social phenomena that may be unrelated to disease occurrence. Furthermore, most solutions are intended for a single language and those meant for multilingual scenarios do not incorporate semantic context. In this paper we experimentally examine different approaches for classifying text for epidemiological surveillance on the social web in addition we offer a systematic comparison of the impact of different input representations on performance. Specifically we compare continuous representations against one-hot encoding for word-based, class-based (ontology-based) and subword units in the form of byte pair encodings. We also go on to establish the desirable performance characteristics for multi-lingual semantic filtering approaches and offer an in-depth discussion of the implications for end-to-end surveillance.

Stream-based live public opinion monitoring approach with adaptive probabilistic topic model

Public opinion monitoring, also known as first story detection, is defined within the topic detection and tracking on a particular Internet news event. Generally, it is used to find news propagation. Traditional method adopts text matching to address opinion monitoring. But it has some limitations such as hidden and latent topic discovery and incorrect relevance ranking of matching results on large-scale data. In this paper, we propose three solutions to live public opinion monitoring: simple keyword computing and matching, simple probabilistic topic computing and matching, and stream-based live probabilistic topic computing and matching. We point out the disadvantages of the first two solutions such as semantic matching and low efficiency on timely big data. Stream-based real-time topic computing and topic matching with query-time document and field boosting are proposed to make substantial improvements. Finally, our topic computing and matching experiments with crawled historical Netease news records show that our approaches are effective and efficient.

SPABox: Safeguarding Privacy During Deep Packet Inspection at a MiddleBox

Widely used over the Internet to encrypt traffic, HTTPS provides secure and private data communication between clients and servers. However, to cope with rapidly changing and sophisticated security attacks, network operators often deploy middleboxes to perform deep packet inspection (DPI) to detect attacks and potential security breaches, using techniques ranging from simple keyword matching to more advanced machine learning and data mining analysis. But this creates a problem: how can middleboxes, which employ DPI, work over HTTPS connections with encrypted traffic while preserving privacy? In this paper, we present SPABox, a middlebox-based system that supports both keyword-based and data analysis-based DPI functions over encrypted traffic. SPABox preserves privacy by using a novel protocol with a limited connection setup overhead. We implement SPABox on a standard server and show that SPABox is practical for both long-lived and short-lived connection. Compared with the state-of-the-art Blindbox system, SPABox is more than five orders of magnitude faster and requires seven orders of magnitude less bandwidth for connection setup while SPABox can achieve a higher security level.

Implementation of the Intelligent Question Answering System Based on Remote Service Framework

Remote answering system of modern distance education system is an indispensable component. However, the current system is based on answering user input on the exam questions simple keyword matching, accuracy and query the user interface cannot meet the needs of users. We have designed a semantic network theory to build up intelligent answering system. We analyzed the necessity of establishing intelligent answering system, and presents a model of intelligent question answering system based on its technology defined areas, and two university computer courses as a source of knowledge to achieve the function of the system. The results show that the proposed method can improve the accuracy of the query, user-friendly and convenient. System Based on J2EE three-tier system, in the middle layer of the advanced MVC design pattern thought to achieve page display and business logic separation, improve system maintainability.

Newsmap

This paper presents the results of an evaluation of three different types of geographical news classification methods: (1) simple keyword matching, a popular method in media and communications research; (2) geographical information extraction systems equipped with named-entity recognition and place name disambiguation mechanisms (Open Calais and Geoparser.io); and (3) a semi-supervised machine learning classifier developed by the author (Newsmap). Newsmap substitutes manual coding of news stories with dictionary-based labelling in the creation of large training sets to extract large numbers of geographical words without human involvement and it also identifies multi-word names to reduce the ambiguity of the geographical traits fully automatically. The evaluation of classification accuracy of the three types of methods against 5000 human-coded news summaries reveals that Newsmap outperforms the geographical information extraction systems in overall accuracy, while the simple keyword matching suffers from ambiguity of place names in countries with ambiguous place names.

A multilingual semi-supervised approach in deriving Singlish sentic patterns for polarity detection

Due to the huge volume and linguistic variation of data shared online, accurate detection of the sentiment of a message (polarity detection) can no longer rely on human assessors or through simple lexicon keyword matching. This paper presents a semi-supervised approach in constructing essential toolkits for analysing the polarity of a localised scarce-resource language, Singlish (Singaporean English). Corpus-based bootstrapping using a multilingual, multifaceted lexicon was applied to construct an annotated testing dataset, while unsupervised methods such as lexicon polarity detection, frequent item extraction through association rules and latent semantic analysis were used to identify the polarity of Singlish n-grams before human assessment was done to isolate misleading terms and remove concept ambiguity. The findings suggest that this multilingual approach outshines polarity analysis using only the English language. In addition, a hybrid combination of the Support Vector Machine and a proposed Singlish Polarity Detection algorithm, which incorporates unigram and n-gram Singlish sentic patterns with other multilingual polarity sentic patterns such as negation and adversative, is able to outperform other approaches in comparison. The promising results of a pooled testing dataset generated from the vast amount of unannotated Singlish data clearly show that our multilingual Singlish sentic pattern approach has the potential to be adopted in real-world polarity detection.

SEMANTIC SIMILARITY BASED CONTEXT-AWARE WEB SERVICE DISCOVERY USING NLP TECHNIQUES

Due to the high availability and also the distributed nature of published web services on the Web, efficient discovery and retrieval of relevant services that meet user requirements can be a challenging task. In this paper, we present a semantics based web service retrieval framework that uses natural language processing techniques to extract a service’s functional information. The extracted information is used to compute the similarity between any given service pair, for generating additional metadata for each service and for classifying the services based on their functional similarity. The framework also adds natural language querying capabilities for supporting exact and approximate matching of relevant services to a given user query. We present experimental results that show that the semantic analysis & automatic tagging effectively captured the inherent functional details of a service and also the similarity between different services. Also, a significant improvement in precision and recall was observed during Web service retrieval when compared to simple keyword matching search, using the natural language querying interface provided by the proposed framework.

A Comparative Study of Ranking Techniquesfor Hidden Web and Surface Web

The web consist of Surface web and hidden web. Surface web is also known as publically indexable web. It can be accessed by search engines using hyperlinks present on the pages and using simple keyword matching schemes. Hidden web refers to content that is hidden behind HTML forms. This contains a large collection of data that are unreachable by link-based search engines. A study conducted at University of California, Berkeley estimated that the deep web consists of around 91,000 terabytes of data, whereas the surface web is only about 167 terabytes. The hidden and surface web crawlers return huge result set for the user query. But users commonly look at top ten or twenty results that can be seen without scrolling. Users rarely look at results coming after first response page so ranking of the results is needed. Till now ranking of the web data is a big challenge. Various scholars tried to propose better and efficient techniques for ranking. In this paper, various ranking methods for the hidden web as well as surface web will be explored

채팅 시스템 구현을 위한 3단계 문장 검색 방법

Abstract: The previous chatting systems have generally used methods based on lexical agreement between users’ in-put sentences and target sentences in a database. However, these methods often raise well-known lexical disagreement problems. To resolve some of lexical disagreement problems, we propose a three-step sentence searching method that is sequentially applied when the previous step is failed. The first step is to compare common keyword sequences be-tween users’ inputs and target sentences in the lexical level . The second step is to compare sentence types and se-mantic markers between users’ input and target sentences in the semantic level. The last step is to match users’s in-puts against predefined lexico-syntactic patterns. In the experiments, the proposed method showed better response pre-cision and user satisfaction rate than simple keyword matching methods. Keywords: Chatting, Lexical-level searching, Semantic-level searching, Lexico-syntactic pattern matching 1. 서 론 최근 해양 정보 관련 비정형 데이터의 양이 많이 증가하고 있다

Analysis of the Empirical Effects of Contextual Matching Advertising for Online News

Beyond the simple keyword matching methods in contextual advertising, we propose a rich contextual matching (CM) model adopting a classification method for topic targeting and a query expansion method for semantic ad matching. This letter reports on an investigation into the empirical effects of the CM model by comparing the click-through rates (CTRs) of two practical online news advertising systems. Based on the evaluation results from over 100 million impressions, we prove that the average CTR of our proposed model outperforms that of a traditional model.

Analysis of the Empirical Effects of Contextual Matching Advertising for Online News

Beyond the simple keyword matching methods in contextual advertising, we propose a rich contextual matching (CM) model adopting a classification method for topic targeting and a query expansion method for semantic ad matching. This letter reports on an investigation into the empirical effects of the CM model by comparing the click-through rates (CTRs) of two practical online news advertising systems. Based on the evaluation results from over 100 million impressions, we prove that the average CTR of our proposed model outperforms that of a traditional model.

Data loss prevention – not yet a cure

Data Loss Prevention (DLP) technologies have matured rapidly and now offer a sophisticated means of classifying and monitoring sensitive data. Yet data breaches continue unabated. Data breaches were experienced by 77% of organisations internationally in 2010. DLP technologies are defined as those that perform content inspection of data at a level of sophistication beyond simple keyword matching and standard expressions. Tracey Caldwell examines the DLP solutions marketplace, and the issues and benefits of the technology, which is one of the few network security technologies that aims to enhance users' understanding of their actions in relation to data security, and improve user behaviour.

An Improved Contextual Advertising Matching Approach based on Wikipedia Knowledge

The current boom of the Web is associated with the revenues originated from Web advertising. As one prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial textual ads within the content of a Web page, so as to provide a better user experience and thereby increase the revenues of Web site owners and an advertising platform. Therefore, in contextual advertising, the relevance of selected ads with a Web page is essential. However, some problems, such as homonymy and polysemy, low intersection of keywords and context mismatch, can lead to the selection of irrelevant textual ads for a Web page, making that a simple keyword matching technique generally gives poor accuracy. To overcome these problems and thus to improve the relevance of contextual ads, in this paper we propose a novel Wikipedia-based matching technique which, using selective matching strategies, selects a certain amount of relevant articles from Wikipedia as an intermediate semantic reference model for matching Web pages and textual ads. We call this technique SIWI: Selective Wikipedia Matching, which, instead of using the whole Wikipedia articles, only matches the most relevant articles for a page (or a textual ad), resulting in the effective improvement of the overall matching performance. An experimental evaluation is conducted, which runs over a set of real textual ads, a set of Web pages from the Internet and a dataset of more than 260Â 000 articles from Wikipedia. The experimental results show that our method performs better than existing matching strategies, which can deal with the matching over the large dataset of Wikipedia articles efficiently, and achieve a satisfactory contextual advertising effect.

A Wikipedia Matching Approach to Contextual Advertising

Contextual advertising is an important part of today's Web. It provides benefits to all parties: Web site owners and an advertising platform share the revenue, advertisers receive new customers, and Web site visitors get useful reference links. The relevance of selected ads for a Web page is essential for the whole system to work. Problems such as homonymy and polysemy, low intersection of keywords and context mismatch can lead to the selection of irrelevant ads. Therefore, a simple keyword matching technique gives a poor accuracy. In this paper, we propose a method for improving the relevance of contextual ads. We propose a novel "Wikipedia matching" technique that uses Wikipedia articles as "reference points" for ads selection. We show how to combine our new method with existing solutions in order to increase the overall performance. An experimental evaluation based on a set of real ads and a set of pages from news Web sites is conducted. Test results show that our proposed method performs better than existing matching strategies and using the Wikipedia matching in combination with existing approaches provides up to 50% lift in the average precision. TREC standard measure bpref-10 also confirms the positive effect of using Wikipedia matching for the effective ads selection.

Automated Semantic Indexing of Figure Captions to Improve Radiology Image Retrieval

ObjectiveWe explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals. DesignThe MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval. MeasurementsPrecision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine. ResultsEstimated precision was 0.897 (95% confidence interval, 0.857–0.937). Estimated recall was 0.930 (95% confidence interval, 0.838–1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone. ConclusionConcept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval.

Using contextual analysis for news event detection

The rapidly growing amount of newswire stories stored in electronic devices raises new challenges for information retrieval technology. Traditional query-driven retrieval is not suitable for generic queries. It is desirable to have an intelligent system to automatically locate topically related events or topics in a continuous stream of newswire stories. This is the goal of automatic event detection. We propose a new approach to performing event detection from multilingual newswire stories. Unlike traditional methods which employ simple keyword matching, our method makes use of concept terms and named entities such as person, location, and organization names. Concept terms of a story are derived from statistical context analysis between sentences in the news story and stories in the concept database. We have conducted a set of experiments to study the effectiveness of our approach. The results show that the performance of detection using concept terms together with story keywords is better than traditional methods which only use keyword representation. 2001 John Wiley & Sons, Inc.

Projet CORAIL

This article shows how a high-quality filtering architecture for targetted documents can be achieved by adapting the intex system to the domain (i.e. ‘economic intelligence’), when expressed in finite state automata filters. A push system named corail (‘request composition by linguistic intelligent agents’) is described, developed by a consortium comprising Thomson-CSF Corporate Laboratory (senior partner), Informatique-CDC, the University of Paris X/CRIS, and the ladl. Both practical and theoretical issues regarding the use of finite state automata are discussed, as for example the notion of the ‘automaton classes’ which are to be used as a paraphrasing agent in the process of document filtering. This project can be considered as an example of how linguistics and computer science can interact in anatural language processing task to meet high-quality standards, namely avoiding noise and reducing silence, thus offering an alternative solution to traditional push systems based on simple keyword-matching. A prototype of the corail system is now operational at Thomson-CSF Central Laboratory and Informatique-CDC.

Single‐pass method for determining the semantic relationships between terms

AbstractA fast single‐pass method for the automatic determination of the semantic relationships between terms is presented. The computing time required for the method is small enough for it to be feasible in a practical environment. The experimental results obtained indicate that the method is effective in the assessment of the relationships between terms. The improvement achieved in retrieval performance over simple keyword matching is quite significant.