A new patent processing suite for academic and research purposes

Patent databases are a counterpart of a technical encyclopedia providing a valuable informational source from patent documents. Patents apply to a wide variety of uses, and this is expanding. The increasing kinds of users of patent documents result in a lack of educational programs regarding this matter. This paper describes Patent2Net (P2N) a patentinformatic suite whose purpose is to fill the lack in the academic world (education and research) of a tool to use with students (STEM, Masters, PhD), by valuation services and for defining corpuses for research in general patent analysis, specifically on textual content. P2N is a free open-source modular, scalable, customizable and derivable tool, written in the python language. We present here the main functions of the tool and the technical aspects after discussing the skills to be reached by students for state of the art patent analysis.     

Collecting and evaluating the CUNY ASL corpus for research on American Sign Language animation

While there is great potential for sign language animation generation software to improve the accessibility of information for deaf individuals with low written-language literacy, the understandability of current sign language animation systems is limited. Data-driven methodologies using annotated sign language corpora encoding detailed human movement have enabled some researchers to address several key linguistic challenges in ASL generation. This article motivates and describes our current research on collecting a motion-capture corpus of American Sign Language (ASL). As an evaluation of our motion-capture configuration, calibration, and recording protocol, we have conducted several rounds of evaluation studies with native ASL signers, and we have made use of our collected data to synthesize novel animations of ASL, which have also been evaluated in experimental studies with native signers.     

浅谈对中文信息处理的认识

中文信息处理是一个未来具有广阔前景的科研领域。从中文信息处理的研究内容、主流技术等几个方面浅谈了对计算机中文信息处理的认识。     

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.     

基于内容主题的语料库系统设计与实现*

介绍了笔者开发制作的汉语语料库系统的特点、功能,以及系统的设计思想和总体框架。系统以2001年《人民日报》电子文本为生语料,实现了KWIC索引、词表生成、词语搭配分析和分类主题词提取等功能,能够为网络信息挖掘和自然语言处理研究提供丰富而真实的语言环境。     

基于语料库和面向统计学的自然语言处理技术

1引言 语料库语言学(Corpus Linguistics)是八十年代才崭露头角的一门新的计算语言学分支学科.它研究机器可读的自然语言文本的采集、存储、检索、统计、语法标注、句法语义分,以及具有上述功能的语料库在语言定量分析、词典编纂、作品风格分     

Second step algorithms in the Burrows–Wheeler compression algorithm

In this paper we focus our attention on the second step algorithms of the Burrows–Wheeler compression algorithm, which in the original version is the Move To Front transform. We discuss many of its replacements presented so far, and compare compression results obtained using these replacements. Then we propose a new algorithm that yields a better compression ratio than the previous algorithms. Copyright © 2001 John Wiley & Sons, Ltd.     

一种基于语料库的词义消歧策略

词义消歧一直是一个难点,同时,也是很多领域都需解决的一个环节。该文首先介绍了目前基于语料库进行词义消歧方法的研究进展和各自的特点,在此基础上探讨了一种基于实例和基于统计的方法(即基于语料库的两种主流方法)相结合的混合策略,并对其可能性和优越性进行了讨论。     

The IAM-database: an English sentence database for offline handwriting recognition

In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words. Received September 28, 2001 / Revised October 10, 2001