2488340
Named Entity Translation with Web Mining and Transliteration
This paper presents a novel approach to improve the named entity translation by combining a transliteration approach with web mining, using web information as a source to complement transliteration, and using transliteration information to guide and enhance web mining. A Maximum Entropy model is employed to rank translation candidates by combining pronunciation similarity and bilingual contextual co-occurrence. Experimental results show that our approach effectively improves the precision and recall of the named entity translation by a large margin.
14888114
What can be done to Automate Conceptual Simulation Modeling?
Conceptual modeling is a critical step that directly affects the quality and efficiency of simulation projects. However current technology can hardly support the process and most practice demonstrated an ad hoc and inefficient approach. Automation can help improve the efficiency and effectiveness of conceptual simulation modeling. However, a number of issues must be addressed, including the formalization of model concepts, representation of modeling knowledge, and interaction between user and computer system. This paper presents a discussion of these issues based on the research by the authors, and propose suggestions for the design and development of a robust computerized modeling environment that aims to improve conceptual simulation modeling process
11843014
Conceptual simulation modeling of warehousing operations
This study focuses on the simulation modeling of warehousing operations commonly seen at general merchandize distribution centers. The key processes and their structural and behavioral characteristics were identified and analyzed. Robust representations and patterns were developed to represent the elements and logic of the simulation models for these processes to facilitate efficient and effective construction of the models. The issues of conceptual modeling such as synthesis, abstraction and specialization were also discussed.
802139
Resume Information Extraction with Cascaded Hybrid Model
This paper presents an effective approach for resume information extraction to support automatic resume management and routing. A cascaded information extraction (IE) framework is designed. In the first pass, a resume is segmented into a consecutive blocks attached with labels indicating the information types. Then in the second pass, the detailed information, such as Name and Address, are identified in certain blocks (e.g. blocks labelled with Personal Information), instead of searching globally in the entire resume. The most appropriate model is selected through experiments for each IE task in different passes. The experimental results show that this cascaded hybrid model achieves better F-score than flat models that do not apply the hierarchical structure of resumes. It also shows that applying different IE models in different passes according to the contextual structure is effective.
14145262
Synonymous Collocation Extraction Using Translation Information
Automatically acquiring synonymous collocation pairs such as <turn on, OBJ, light> and <switch on, OBJ, light> from corpora is a challenging task. For this task, we can, in general, have a large monolingual corpus and/or a very limited bilingual corpus. Methods that use monolingual corpora alone or use bilingual corpora alone are apparently inadequate because of low precision or low coverage. In this paper, we propose a method that uses both these resources to get an optimal compromise of precision and coverage. This method first gets candidates of synonymous collocation pairs based on a monolingual corpus and a word thesaurus, and then selects the appropriate pairs from the candidates using their translations in a second language. The translations of the candidates are obtained with a statistical translation model which is trained with a small bilingual corpus and a large monolingual corpus. The translation information is proved as effective to select synonymous collocation pairs. Experimental results indicate that the average precision and recall of our approach are 74% and 64% respectively, which outperform those methods that only use monolingual corpora and those that only use bilingual corpora.
8866458
Collocation Translation Acquisition Using Monolingual Corpora
Collocation translation is important for machine translation and many other NLP tasks. Unlike previous methods using bilingual parallel corpora, this paper presents a new method for acquiring collocation translations by making use of monolingual corpora and linguistic knowledge. First, dependency triples are extracted from Chinese and English corpora with dependency parsers. Then, a dependency triple translation model is estimated using the EM algorithm based on a dependency correspondence assumption. The generated triple translation model is used to extract collocation translations from two monolingual corpora. Experiments show that our approach outperforms the existing monolingual corpus based methods in dependency triple translation and achieves promising results in collocation translation extraction.
29594281
A 2-D boundary element method approach to the simulation of DMOS transistors
A boundary element method is introduced for the calculation of two-dimensional potential and electric field distributions in high-voltage DMOS transistors. An algorithm is proposed to determine the dimensions of the depletion layers in two-dimensional geometries. Regions with different permittivities are taken into account using appropriate boundary conditions. As an application, the high-voltage behavior of the DMOS transistors is investigated, and the avalanche breakdown conditions of the transistors are determined by calculating the ionization integral. The results are compared with simulations based on finite difference methods. >