A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Ripple Down Rules for Question Answering

Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.Comment: V1: 21 pages, 7 figures, 10 tables. V2: 8 figures, 10 tables; shorten section 2; change sections 4.3 and 5.1.2. V3: Accepted for publication in the Semantic Web journal. V4 (Author's manuscript): camera ready version, available from the Semantic Web journal at http://www.semantic-web-journal.ne

Sentiment classification on polarity reviews: an empirical study using rating-based features

We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task

Design of an LMI-based Polytopic LQR Cruise Controller for an Autonomous Vehicle towards Riding Comfort

In this paper, we present an LMI-based approach for comfort-oriented cruise control of an autonomous vehicle. First, vehicle longitudinal dynamics and a corresponding parameter-dependent state-space representation are explained and discussed. An LMI-based polytopic LQR controller is then designed for the vehicle speed to track the reference value in the presence of noise and disturbances, where the scheduling parameters are functions of the vehicle mass and the speed itself. An appropriate disturbance force compensation term is also included in the designed controller to provide a smoother response. Then we detail how the reference speed is calculated online, using polynomial functions of the given desired comfort level (quantified by the vertical acceleration absorbed by the human body) and of the road type characterized by road roughness. Finally, time-domain simulations illustrate the method’s effectiveness

Real-time Damper Force Estimation for Automotive Suspension: A Generalized H2/LPV Approach

The real-time knowledge of the damper force is of paramount importance in controlling and diagnosing automotive suspension systems. This study presents a generalized H2/LPV observer for damper force estimation of a semi-active electro-rheological (ER) suspension system. First, an extended quarter-car model augmented with the nonlinear and dynamical model of the semi-active suspension system is written into the quasi-LPV formulation. Then, the damper force estimation method is developed through a generalized H2/LPV observer whose objective is to handle the impact of unknown road disturbances and sensor noise on the estimation errors of the state variables thanks to the H2 norm. The measured sprung and unsprung mass accelerations of the quarter-car system are used as inputs for the observer. The proposed approach is simulated with validated model of the 1/5-scaled real vehicle testbed of GIPSA-lab. Simulation results show the performance of the estimation method against unknown disturbances, emphasizing the effectiveness of the damper force estimation in real time

Isolation endophytic bacteria from elephant grass (pennisetum purpureum schumach) and their potential application

In this study, 25 endophytic bacteria were isolated and purified from rhizome, stem and leaf of the elephant grass, which were tested for their biological control properties. The number of living and dead brown plant hoppers were recorded and the mortality rate was analyzed by using Abbott’s formula. The results indicated that three endophytic bacteria including VBL1, VBT1 and VBT5 showed the highest biological control of Nilaparvata lugens at the mortality rate 46.95%, 55.02% and 55.02%, respectively after 8 days of screening and significant difference compared to other isolates (

Risk of Land Degradation: A Case Study of Phu Yen Province, Vietnam

The issue of the land degradation vulnerability index (LDVI) is multifaceted, encompassing climate, soil, vegetation, policy formulation, and human actions. In Vietnam, the convergence of climatic fluctuations and human impact results in phenomena, such as soil sealing, erosion, salinization, and landscape fragmentation. These phenomena are recognized as significant triggers of land degradation. This paper seeks to present a method for assessing a land's susceptibility to degradation by utilizing ten ecological 10 criteria: NDVI; slope; bulk density (cg/cm3); cation exchange capacity in the soil (CEC; mmol(c)/kg); Soil organic carbon stock (SOC; dg/kg), pH; Nitrogen (N; cg/kg); soil thickness (cm); soil surface temperature LST (0C); precipitation of the driest quarter (mm). The research results show that Song Hinh and Son Hoa communes are standing on the most land degradation vulnerability. Some criteria that are considered important in assessing land degradation by the analytic hierarchy process (AHP) technique are NDVI, followed by slope, nitrogen, bulk density, and soil thickness. The results of the study are consistent with records in localities that are often under pressure from drought. Extreme LDVI areas were larger identified on low mountains, slope terrain, and precipitation of driest quarter under 200mm, expanding on the agricultural areas with 40km2 total province agriculture area, followed by grassland (20.3 km2), natural forests (17.2 km2), plantation forests (8.2 km2), residences (8.2 km2), and bare land (8.15 km2). Poor land management practices, such as improper construction, inadequate water management, and lack of terracing, can contribute to soil erosion and land degradation. This LDVI assessment process can be applied to some tropical countries. The NDVI index combined with the slope, nitrogen, bulk density, and soil thickness can be exploratory indicators of land sensitivity to land degradation