[日本語] / [English] 京都大学 大学院情報学研究科 知能情報学コース 言語メディア分野（工学部電気電子工学科） 研究室へのアクセス JUMAN は有効な WikiName ではありません。 サイトポリシー

目录 软件简介 在线演示 编译和安装 使用方式 与代表性分词软件的性能对比 词性标记集 THULAC的不同配置 获取链接 注意事项 历史 开源协议 相关论文 作者 常见问题 致谢 软件简介 THULAC（THU Lexical Analyzer for Chinese）由清华大学自然语言处理与社会人文计算实验室研制推出的一套中文词法分析工具包，具有中文分词和词性标注功能。THULAC具有如下几个特点： 能力强。利用我们集成的目前世界上规模最大的人工分词和词性标注中文语料库（约含5800万字）训练而成，模型标注能力强大。 准确率高。该工具包在标准数据集Chinese Treebank（CTB5）上分词的F1值可达97.3％，词性标注的F1值可达到92.9％，与该数据集上最好方法效果相当。 速度较快。同时进行分词和词性标注速度为300KB/s，每秒可处理约15万字。只进行分词速度可达到1.3M

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I am an Associate Professor at the Carnegie Mellon University Language Technology Institute in the School of Computer Science, and work with a bunch of great students in my lab NeuLab. Prospective Students/Visitors: Please see the contact info page for more info. Research My research focuses on machine learning and natural language processing. In particular, I am interested in basic research and a

Daichi Mochihashi NTT Communication Science Laboratories $Id: lwlm.html,v 1.1 2010/03/19 10:15:06 daichi Exp $ lwlm is an exact, full Bayesian implementation of the Latent Words Language Model (Deschacht and Moens, 2009). It automatically learns synonymous words to infer context-dependent "latent word" for each word appearance, in a completely unsupervised fashion. Technically, LWLM is a higher-or

SENNA is a software distributed under a non-commercial license, which outputs a host of Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER) and semantic role labeling (SRL). SENNA is fast because it uses a simple architecture, self-contained because it does not rely on the output of existing NLP system, and accurate because it off

Abstract: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lam

KenLM estimates, filters, and queries language models. Estimation is fast and scalable due to streaming algorithms explained in the paper Scalable Modified Kneser-Ney Language Model Estimation Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. ACL, Sofia, Bulgaria, 4—9 August, 2013. [Paper] [Slides] [BibTeX] Querying is fast and low-memory, as shown in the paper KenLM: Faste

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