基于查询的新闻多文档自动摘要技术研究

针对新闻文本领域,该文提出一种基于查询的自动文本摘要技术,更加有针对性地满足用户信息需求。根据句子的TF-IDF、与查询句的相似度等要素,计算句子权重,并根据句子指示的时间给定不同的时序权重系数,使得最近发生的新闻内容具有更高的权重,最后使用最大边界相关的方法选择摘要句。通过与基于TF-IDF、Text-Rank、LDA等六种方法的对比,该摘要方法ROUGE评测指标上优于其他方法。从结合评测结果及摘要示例可以看出,该文提出的方法可以有效地从新闻文档集中摘取核心信息,满足用户查询内容的信息需求。     

Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval

Due to the language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that can help Chinese-speaking consumers cross the language barrier in finding useful English medical information. A few CLMIR systems utilize MeSH (Medical Subject Headings) to help overcome the language barrier. Unfortunately, the traditional Chinese version of MeSH is currently unavailable.In this paper, we employ a semi-automatic term translation method to construct a Chinese–English MeSH by exploiting abundant multilingual Web resources, including Web anchor texts and search–result pages. Through this method, we have developed a Chinese–English Mesh Compilation System to assist knowledge engineers in compiling a Chinese–English medical thesaurus with more than 19,000 entries. Furthermore, this thesaurus has been used to develop a prototypical system for cross-language medical information retrieval, MMODE, which can help consumers retrieve top-quality English medical information using Chinese terms.     

Inference and retrieval of facial images

Attempts have been made to extend SQL to work with multimedia databases. We are reserved on the representation ability of extended SQL to cope with the richness in content of multimedia data. In this paper we present an example of a multimedia database system, Computer Aided Facial Image Inference and Retrieval system (CAFIIR). The system stores and manages facial images and criminal records, providing necessary functions for crime identification. We would like to demonstrate some core techniques for multimedia database with CAFIIR system. Firstly, CAFIIR is a integrated system. Besides database management, there are image analysis, image composition, image aging, and report generation subsystems, providing means for problem solving. Secondly, the richness of multimedia data urges feature-based database for their management. CAFIIR is feature-based. A indexing mechanism,iconic index, has been proposed for indexing facial images using hierarchical self-organization neural network. The indexing method operates on complex feature measures and provides means for visual navigation. Thirdly, special retrieval methods for facial images have been developed, including visual browsing, similarity retrieval, free text retrieval and fuzzy retrieval.     

Text planning with opportunistic control

This report describes the current state of our central research thrust in the area of natural language generation. We have already reported on our text-level theory of lexical selection in natural language generation ([59, 60]), on a unification-based syntactic processor for syntactic generation ([73]) and designed a relatively flexible blackboard-oriented architecture for integrating these and other types of processing activities in generation ([60]). We have implemented these ideas in our prototype generator, Diogenes — a DIstributed, Opportunistic GENEration System — and tested our lexical selection and syntactic generation modules in a comprehensive natural language processing project — the KBMT-89 machine translation system ([15]). At this stage we are developing a more comprehensive Diogenes system, concentrating on both the theoretical and the system-building aspects of a) formulating a more comprehensive theory of distributed natural language generation; b) extending current theories of text organization as they pertain to the task of planning natural language texts; c) improving and extending the knowledge representation and the actual body of background knowledge (both domain and discourse/pragmatic) required for comprehensive text planning; d) designing and implementing algorithms for dynamic realization of text structure and integrating them into the blackboard style of communication and control; e) designing and implementing control algorithms for distributed text planning and realization. In this document we describe our ideas concerning opportunistic control for a natural language generation planner and present a research and development plan for the Diogenes project.Many people have contributed to the design and development of the Diogenes generation system over the last four years, especially Eric Nyberg, Rita McCardell, Donna Gates, Christine Defrise, John Leavitt, Scott Huffman, Ed Kenschaft and Philip Werner. Eric Nyberg and Masaru Tomita have created genkit, which is used as the syntactic component of Diogenes. A short version of this article appeared in Proceedings of IJCAI-89, co-authored with Victor Lesser and Eric Nyberg. To all the above many thanks. The remaining errors are the responsibility of this author.     

The State of the Art in Text Filtering

This paper develops a conceptual framework for text filtering practice and research, and reviews present practice in the field. Text filtering is an information seeking process in which documents are selected from a dynamic text stream to satisfy a relatively stable and specific information need. A model of the information seeking process is introduced and specialized to define text filtering. The historical development of text filtering is then reviewed and case studies of recent work are used to highlight important design characteristics of modern text filtering systems. User modeling techniques drawn from information retrieval, recommender systems, machine learning and other fields are described. The paper concludes with observations on the present state of the art and implications for future research on text filtering. This revised version was published online in July 2006 with corrections to the Cover Date.     

新颖的基于小波变换的数字水印方案

随着计算机网络技术与多媒体技术的快速发展，数字产品的版权保护已经成为信息技术领域中最重要的问题之一，提出了一种新颖的基于离散小波变换的数字水印方案，该方案利用改进的Pacthwork算法，将水印嵌入到LL子带中，水印信号在解码过程中无需使用原始图像可快速地重嵌入，该算法利用BCH码来降低误码率，应用所提出的算法，把一个32个字符的水印嵌入到图像中，实现结果表明水印是不可察觉的，经过JPEG有损压缩，低通与中值滤波等图像处理操作后仍是鲁棒的。     

一种基于稳健匹配准则的视频文本跟踪方法

本文首先根据文本中各个字符的颜色相似的特性,确定已知文本块的文本的颜色,利用文本的颜色信息二值化文本图像以得到文本块中的字符数、字符的尺寸和间距;其次,根据视频图像的大小和视频的播放速度确定搜索窗口的大小,并利用文本的运动信息来预测搜索窗口的位置;第三,根据在相邻两帧中具有相同内容文本的颜色相似的特性,提出了一个可以解决脉冲噪声和部分缺损或遮挡问题的稳健匹配准则来跟踪文本块;最后,确定跟踪输出的文本的颜色、尺寸和运动方式。本文方法可以很好地跟踪平移、缩小或放大、旋转、淡入淡出和部分被遮挡的水平或倾斜排列的文本,并且可以记录文本平移、旋转和缩放的速度。     

Bridging the generation gap between text planning and linguistic realization

Text planning is traditionally done as a separate process, independent of linguistic realization. However, I show in this paper that it is essential for a text planner to know the linguistic consequences of its actions. The choice of how to realize an element affects how much information is conveyed (e.g., “Floyd arrived safely in Boston” vs. “Floyd landed safely at Logan”) and what other information can be added (e.g., “Peter was deciding for an hour” vs. *“Peter made a decision for an hour”). The choice of realization also affects how the relative salience of the elements being expressed will be perceived (e.g., “The green car is in the garage” vs. “The car in the garage is green”). I have defined an intermediate level of representation for text planning, called the Text Structure. It is an abstract linguistic level that reflects germane linguistic constraints while abstracting away from syntactic detail. This representation allows the text planner to have greater control over the decisions, so that it can take advantage of the expressiveness of language to convey subtleties of meaning. More importantly, the Text Structure allows the generation process overall to be incremental, since it ensures that the text plan being composed will always be expressible in the language. La planification de textes est habituellement réalisée séparément, sans tenir compte de la réalisation linguistique. Cependant, ľauteur demontre dans cet article qu'il est essentiel à un planificateur de textes de connai̊tre les conséquences linguistiques de ses actions. Le choix de la méhode de réalisation ?un élément influe sur la quantité?informations qui est transmise (par ex.: 〈〈 Floyd est arrivé sain et sauf à Boston 〉〉 et 〈〈 Floyd a atterri sain et sauf àľaéroport Logan 〉〉) et sur quelle autre information peut ětre ajoutée. Le choix de la méthode de réalisation influe également sur la façon dont les caractères saillants des ééments qui sont exprimés seront perçus (par ex.: 〈〈 la voiture verte est dans le garage 〉〉 et 〈〈 la voiture dans le garage est verte 〉〉). L'auteur a défini un niveau intérmediaire de représentation pour la planification de textes qu'il a appelé structure de texte. Il; s'agit ?'un niveau linguistique abstrait qui refléte les contraintes linguistiques appropriées tout en s'éloignant du détail syntaxique. Cette représentation permet au planificateur de textes ?avoir un meilleur contrǒle des décisions, et done de tirer profit de la force ?expression du langage afin de tenir compte des subtiliés de sens. Plus important encore, la structure de texte permet au processus de génération ?ětre incrémentiel, car elle s'assure que le plan de texte en voie de composition soit toujours exprimable dans le langage.     

科技论文摘要英译策略研究——以《中国钨业》摘要英译为例

科技论文摘要属典型的信息类语篇,其英译的预期功能是向译文读者传播科研成果。译者应在充分了解汉英两种语言在语用、逻辑和句法差异的基础上,灵活进行相应的变通,使译文做到语言通顺自然、逻辑层次清楚、措辞精确严谨。     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.