基于领域类别信息C-value的多词串自动抽取

该本的多词串抽取是自然语言处理领域一项重要的研究内容。该文提出了一种多类别C-value(Multi-Class C-value)方法,利用多词串在不同领域的分布信息改善领域相关的多词串抽取的性能。在汽车、科技和旅行三个领域的数据上进行实验,评价多词串的准确率,在top-100级别上,较传统的C-value方法在三个领域中分别提高了12、12和13个百分点。实验结果验证了方法的有效性。     

维吾尔语多词领域术语的自动抽取

多词领域术语抽取是自然语言处理技术中的一个重点和难点问题, 结合维吾尔语语言特征,该文提出了一种基于规则和统计相结合的维吾尔语多词领域术语的自动抽取方法。该方法分为四个阶段: ①语料预处理, 包括停用词过滤和词性标注; ② 对字串取N元子串, 利用改进的互信息算法和对数似然比率计算子串内部的联合强度, 结合词性构成规则, 构建候选维吾尔语多词领域术语集; ③ 利用相对词频差值, 得到尽可能多的维吾尔语多词领域术语; ④ 结合C_value值获取最终领域术语并作后处理。实验结果准确率为85.08%, 召回率为 73.19%, 验证了该文提出的方法在维吾尔语多词领域术语抽取上的有效性。     

融合规则与统计的微博新词发现方法

结合微博新词的构词规则自由度大和极其复杂的特点,针对传统的C/NC-value方法抽取的结果新词边界的识别准确率不高,以及低频微博新词无法正确识别的问题,提出了一种融合人工启发式规则、C/NC-value改进算法和条件随机场（CRF）模型的微博新词抽取方法。一方面,人工启发式规则是指对微博新词的分类和归纳总结,并从微博新词构词的词性（POS）、字符类别和表意符号等角度设计的微博新词的构词规则;另一方面,改进的C/NC-value方法通过引入词频、邻接熵和互信息等统计量来重构NC-value目标函数,并使用CRF模型训练和识别新词,最终达到提高新词边界识别准确率和低频新词识别精度的目的。实验结果显示,与传统方法相比,所提出的方法能有效地提高微博新词识别的F值。     

Term extraction from sparse,ungrammatical domain-specific documents

Existing term extraction systems have predominantly targeted large and well-written document collections, which provide reliable statistical and linguistic evidence to support term extraction. In this article, we address the term extraction challenges posed by sparse, ungrammatical texts with domain-specific contents, such as customer complaint emails and engineers’ repair notes. To this aim, we present ExtTerm, a novel term extraction system. Specifically, as our core innovations, we accurately detect rare (low frequency) terms, overcoming the issue of data sparsity. These rare terms may denote critical events, but they are often missed by extant TE systems. ExtTerm also precisely detects multi-word terms of arbitrarily lengths, e.g. with more than 2 words. This is achieved by exploiting fundamental theoretical notions underlying term formation, and by developing a technique to compute the collocation strength between any number of words. Thus, we address the limitation of existing TE systems, which are primarily designed to identify terms with 2 words. Furthermore, we show that open-domain (general) resources, such as Wikipedia, can be exploited to support domain-specific term extraction. Thus, they can be used to compensate for the unavailability of domain-specific knowledge resources. Our experimental evaluations reveal that ExtTerm outperforms a state-of-the-art baseline in extracting terms from a domain-specific, sparse and ungrammatical real-life text collection.     

A probabilistic justification for using tf×idf term weighting in information retrieval

This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well-known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf×idf term weighting. The paper shows that the new probabilistic interpretation of tf×idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the TREC collection shows that the linguistically motivated weighting algorithm outperforms the popular BM25 weighting algorithm. Received: 17 December 1998 / Revised: 31 May 1999     

Extraction of type style-based meta-information from imaged documents

Extraction of some meta-information from printed documents without carrying out optical character recognition (OCR) is considered. It can be statistically verified that important terms in technical articles are mainly printed in italic, bold, and all-capital style. A quick approach to detecting them is proposed here. This approach is based on the global shape heuristics of these styles of any font. Important words in a document are sometimes printed in larger size as well. A smart approach for the determination of font size is also presented. Detection of type styles helps in improving OCR performance, especially for reading italicized text. Another advantage to identifying word type styles and font size has been discussed in the context of extracting: (i) different logical labels; and (ii) important terms from the document. Experimental results on the performance of the approach on a large number of good quality, as well as degraded, document images are presented. Received July 12, 2000 / Revised October 1, 2000     

A word extraction algorithm for machine-printed documents using a 3D neighborhood graph model

Automatic character recognition and image understanding of a given paper document are the main objectives of the computer vision field. For these problems, a basic step is to isolate characters and group words from these isolated characters. In this paper, we propose a new method for extracting characters from a mixed text/graphic machine-printed document and an algorithm for distinguishing words from the isolated characters. For extracting characters, we exploit several features (size, elongation, and density) of characters and propose a characteristic value for classification using the run-length frequency of the image component. In the context of word grouping, previous works have largely been concerned with words which are placed on a horizontal or vertical line. Our word grouping algorithm can group words which are on inclined lines, intersecting lines, and even curved lines. To do this, we introduce the 3D neighborhood graph model which is very useful and efficient for character classification and word grouping. In the 3D neighborhood graph model, each connected component of a text image segment is mapped onto 3D space according to the area of the bounding box and positional information from the document. We conducted tests with more than 20 English documents and more than ten oriental documents scanned from books, brochures, and magazines. Experimental results show that more than 95% of words are successfully extracted from general documents, even in very complicated oriental documents. Received August 3, 2001 / Accepted August 8, 2001     

A model-driven approach for real-time road recognition

This article describes a method designed to detect and track road edges starting from images provided by an on-board monocular monochromic camera. Its implementation on specific hardware is also presented in the framework of the VELAC project. The method is based on four modules: (1) detection of the road edges in the image by a model-driven algorithm, which uses a statistical model of the lane sides which manages the occlusions or imperfections of the road marking – this model is initialized by an off-line training step; (2) localization of the vehicle in the lane in which it is travelling; (3) tracking to define a new search space of road edges for the next image; and (4) management of the lane numbers to determine the lane in which the vehicle is travelling. The algorithm is implemented in order to validate the method in a real-time context. Results obtained on marked and unmarked road images show the robustness and precision of the method. Received: 18 November 2000 / Accepted: 7 May 2001     

Improving effectiveness of mutual information for substantival multiword expression extraction

One of the deficiencies of mutual information is its poor capacity to measure association of words with unsymmetrical co-occurrence, which has large amounts for multi-word expression in texts. Moreover, threshold setting, which is decisive for success of practical implementation of mutual information for multi-word extraction, brings about many parameters to be predefined manually in the process of extracting multiword expressions with different number of individual words. In this paper, we propose a new method as EMICO (Enhanced Mutual Information and Collocation Optimization) to extract substantival multiword expression from text. Specifically, enhanced mutual information is proposed to measure the association of words and collocation optimization is proposed to automatically determine the number of individual words contained in a multiword expression when the multiword expression occurs in a candidate set. Our experiments showed that EMICO significantly improves the performance of substantival multiword expression extraction in comparison with a classic extraction method based on mutual information.     

A new phase extraction algorithm for phase profilometry

This paper describes a new phase extraction algorithm for 3D optical profilometry based on the projection of a periodic light pattern and phase measurement (phase profilometry). The algorithm uses a square wave to demodulate phase and moving averages and comb-shaped filters to extract the phase information from low frequency. The proposed algorithm is compared with the two major profilometry techniques, namely Fourier domain profilometry and signal domain profilometry based on FIR low-pass filtering. Comparison is focused on adaptiveness to changes of the pattern frequency, ability to deal with nonuniform surfaces and computational complexity. Adaptiveness analysis is carried out by means of simulations. The issue of nonuniform surfaces is discussed on the basis of experimental results obtained from application of phase profilometry to on-line 3D printed circuit board inspection. With regard to complexity, theoretical estimates are verified by means of actual computation time measurements. Received: 30 November 1996 / Accepted: 6 June 1997     

Automatic video data structuring through shot partitioning and key-frame computing

In video processing, a common first step is to segment the videos into physical units, generally called shots. A shot is a video segment that consists of one continuous action. In general, these physical units need to be clustered to form more semantically significant units, such as scenes, sequences, programs, etc. This is the so-called story-based video structuring. Automatic video structuring is of great importance for video browsing and retrieval. The shots or scenes are usually described by one or several representative frames, called key-frames. Viewed from a higher level, key frames of some shots might be redundant in terms of semantics. In this paper, we propose automatic solutions to the problems of: (i) video partitioning, (ii) key frame computing, (iii) key frame pruning. For the first problem, an algorithm called “net comparison” is devised. It is accurate and fast because it uses both statistical and spatial information in an image and does not have to process the entire image. For the last two problems, we develop an original image similarity criterion, which considers both spatial layout and detail content in an image. For this purpose, coefficients of wavelet decomposition are used to derive parameter vectors accounting for the above two aspects. The parameters exhibit (quasi-) invariant properties, thus making the algorithm robust for many types of object/camera motions and scaling variances. The novel “seek and spread” strategy used in key frame computing allows us to obtain a large representative range for the key frames. Inter-shot redundancy of the key-frames is suppressed using the same image similarity measure. Experimental results demonstrate the effectiveness and efficiency of our techniques.     

Integrating ontologies and thesauri for RDF schema creation and metadata querying

In this paper we present a new approach for building metadata schemas by integrating existing ontologies and structured vocabularies (thesauri). This integration is based on the specification of inclusion relationships between thesaurus terms and ontology concepts and results in application-specific metadata schemas incorporating the structural views of ontologies and the deep classification schemes provided by thesauri. We will also show how the result of this integration can be used for RDF schema creation and metadata querying. In our context, (metadata) queries exploit the inclusion semantics of term relationships, which introduces some recursion. We will present a fairly simple database-oriented solution for querying such metadata which avoids a (recursive) tree traversal and is based on a linear encoding of thesaurus hierarchies. Published online: 22 September 2000     

面向机器辅助翻译的汉语语块自动抽取研究

本文提出了一种统计和规则相结合的语块抽取方法。本文使用Nagao串频统计算法进行基于词语的串频统计,进一步分别利用统计方法、语块边界过滤规则对2-gram到10-gram语块进行过滤,得到候选语块,取得了令人满意的结果。通过实验发现,在统计方法中互信息和信息熵相结合的方法较单一的互信息方法好;在语块边界规则过滤方法中语块左右边界规则和停用词对语块抽取的结果有较大影响。实验结果表明统计和过滤规则相结合的方法要优于纯粹的统计方法。应用本文方法,再辅以人工校对,可以方便地获取重复出现的多词语块。在机器辅助翻译系统中,使用现有的语块抽取方法抽取重复的语言单位,就可以方便地建设翻译记忆库,提高翻译的工作效率。     

A Location Model for Communicating and Processing of Context

Location is one of the most important elements of context in ubiquitous computing. In this paper we describe a location model, a spatial-aware communication model and an implementation of the models that exploit location for processing and communicating context. The location model presented describes a location tree, which contains human-readable semantic and geometric information about an organisation and a structure to describe the current location of an object or a context. The proposed system is dedicated to work not only on more powerful devices like handhelds, but also on small computer systems that are embedded into everyday artefact (making them a digital artefact). Model and design decisions were made on the basis of experiences from three prototype setups with several applications, which we built from 1998 to 2002. While running these prototypes we collected experiences from designers, implementers and users and formulated them as guidelines in this paper. All the prototype applications heavily use location information for providing their functionality. We found that location is not only of use as information for the application but also important for communicating context. In this paper we introduce the concept of spatial-aware communication where data is communicated based on the relative location of digital artefacts rather than on their identity. Correspondence to: Michael Biegl, Telecooperation Office (TecO), University of Karlsruhe, Vincenz-Prieβritz-Str. 1 D-76131 Karlsruhe, Germany. Email: michael@teco.edu     

中文领域本体学习中术语的自动抽取*

提出一种领域术语自动抽取的混合策略,首先进行多字词候选术语抽取和分词,然后合并其结果,最后通过领域相关度和领域主题一致度抽取出最终领域术语。在多字词抽取和最终领域术语抽取阶段分别对现有方法进行了改进,降低了字符串分解的时间复杂度并提高了领域术语抽取的准确率和召回率。实验表明,术语抽取准确率为90.64%,优于现有的抽取方法。     

Maximum mutual information training for an online neural predictive handwritten word recognition system

In this paper, we present a hybrid online handwriting recognition system based on hidden Markov models (HMMs). It is devoted to word recognition using large vocabularies. An adaptive segmentation of words into letters is integrated with recognition, and is at the heart of the training phase. A word-model is a left-right HMM in which each state is a predictive multilayer perceptron that performs local regression on the drawing (i.e., the written word) relying on a context of observations. A discriminative training paradigm related to maximum mutual information is used, and its potential is shown on a database of 9,781 words. Received June 19, 2000 / Revised October 16, 2000     

Automatic extraction of roads from aerial images based on scale space and snakes

Abstract. We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by evaluating them quantitatively based on ground truth. Received: 22 July 1999 / Accepted: 20 March 2000     

Views in a large-scale XML repository

We are interested in defining and querying views in a huge and highly heterogeneous XML repository (Web scale). In this context, view definitions are very large, involving lots of sources, and there is no apparent limitation to their size. This raises interesting problems that we address in the paper: (i) how to distribute views over several machines without having a negative impact on the query translation process; (ii) how to quickly select the relevant part of a view given a query; (iii) how to minimize the cost of communicating potentially large queries to the machines where they will be evaluated. The solution that we propose is based on a simple view definition language that allows for automatic generation of views. The language maps paths in the view abstract DTD to paths in the concrete source DTDs. It enables a distributed implementation of the view system that is scalable both in terms of data and load. In particular, the query translation algorithm is shown to have a good (linear) complexity. Received: November 1, 2001 / Accepted: March 2, 2002 Published online: September 25, 2002     

基于双语语料的单个源语词汇和目标语多词单元的对齐

多词单元包括固定搭配、多词习语和多词术语等。本文提供了一个基于双语口语语料库的自动对齐单个源语词汇和目标语多词单元的算法,算法一方面通过计算对应于同一个源语词汇,多个目标语词汇之间的互信息和t值的归一化差值的大小来衡量目标语多个词语之间的关联程度以提取多词单元,另一方面通过计算互信息和t值的平均值作为多词单元和单个源语词汇之间互为相互翻译的衡量程度,用局部最优、首尾禁用词过滤以及长词优先等策略很好地解决了这个问题。另外,对短语翻译词典的分级,有效地减少了高级别词典中非正确翻译项的数目,使得翻译词典具有更好的实用性。     

Range image segmentation using local approximation of scan lines with application to CAD model acquisition

Abstract. Automatic acquisition of CAD models from existing objects requires accurate extraction of geometric and topological information from the input data. This paper presents a range image segmentation method based on local approximation of scan lines. The method employs edge models that are capable of detecting noise pixels as well as position and orientation discontinuities of varying strengths. Region-based techniques are then used to achieve a complete segmentation. Finally, a geometric representation of the scene, in the form of a surface CAD model, is produced. Experimental results on a large number of real range images acquired by different range sensors demonstrate the efficiency and robustness of the method. Received: 1 August 2000 / Accepted: 23 January 2002 Correspondence to: I. Khalifa