{"title":"Linguistically Motivated and Ontological Features for Vietnamese Named Entity Recognition","authors":"Truc-Vien T. Nguyen, T. Cao","doi":"10.1109/rivf.2012.6169818","DOIUrl":"https://doi.org/10.1109/rivf.2012.6169818","url":null,"abstract":"In this paper, we provide a deep analysis on the effect of linguistic features and ontological features for the Vietnamese named entity recognition (NER) task. Plugged in into an off-the-shelf learning framework, we show that, simple lexical words and bi-gram features allow to encode dependencies amongst possible NE labels in Vietnamese language. Results achieved on a standard annotated corpus support our claim, with an accuracy comparable to the state-of-the-art without any external resource. Moreover, when augmented with ontological features from a large knowledge base, the results in both flat and structured classification are almost competitive. Our finding exhibits interesting aspects of linguistically motivated features, including contextual and syntactic patterns for Vietnamese language. Additionally, results achieved with ontological features show that, they can be used to learn as specific as needed, resulting in the first high-performance Vietnamese structured NER system.","PeriodicalId":115212,"journal":{"name":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115682706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Framework to Combine Multiple Matchers for Pair-Wise Schema Matching","authors":"T. Nguyen, Nguyen Quoc Viet Hung, T. Quan","doi":"10.1109/rivf.2012.6169817","DOIUrl":"https://doi.org/10.1109/rivf.2012.6169817","url":null,"abstract":"There are many matching tools (or matchers) have been develop to generate correspondences of elements between two schema. However, the performances of those matchers are highly dependant on the domains they are applied. One tool may achieve best performance in a specific domain but worst when applied in other ones. In this work we propose a combination technique, which enhances mapping quality by merging several mappings. We rely on the well-known Stable Marriage (SM) approach to perform the suitable selection between multiple matching results. In order to reduce complexity and increase the accuracy of SM algorithm, we suggest to combine it with Hyperlink-Induced Topic Search (HITS) algorithm, which can reasonably filter out good candidates for matching selection. We show empirically that the combined solution yields better result than individual matchers in various domains in terms of precision and recall.","PeriodicalId":115212,"journal":{"name":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116848599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}