Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.92
H. Lüngen, Angelika Storrer
Word nets are lexical reference systems that follow the design principles of the Princeton WordNet project (Fellbaum 1998, henceforth referred to as PWN1). Domain ontologies (or domain-specific ontologies, e.g. GOLD2 or the GENE Ontology3) represent knowledge about a specific domain in a format that supports automated reasoning about the objects in that domain and the relations between them (cf. Erdmann 2001, 78). Word nets have been used in various applications of text processing, e.g. discourse parsing, lexical and thematic chaining, cohesion analyses, automatic segmentation and linking, anaphora resolution, and information extraction. When these applications process documents dealing with a specific domain, one needs to combine knowlegde about the domain-specific vocabulary represented in domain ontologies with lexical repositories representing general vocabulary (like PWN). In this context, it is useful to represent and interrelate the entities and relations in both types of resources using a common representation language. In our research group “Text-technological Information Modelling4” we chose OWL as a common format for this purpose. Since our projects are mainly concerned with German documents, we developed an OWL model that relates the German wordnet GermaNet (henceforth referred to as GN)5 with domain-specific ontologies in an approach that was inspired by the Plug-In model proposed in Magnini/Speranza (2002). Our approach is decribed in Kunze et al. (to appear); it was evaluated using representative subsets of GN and of the domain ontology TermNet6 (henceforth referred to as TN) as data and Protégé
{"title":"Domain ontologies and wordnets in OWL: Modelling options","authors":"H. Lüngen, Angelika Storrer","doi":"10.21248/jlcl.22.2007.92","DOIUrl":"https://doi.org/10.21248/jlcl.22.2007.92","url":null,"abstract":"Word nets are lexical reference systems that follow the design principles of the Princeton WordNet project (Fellbaum 1998, henceforth referred to as PWN1). Domain ontologies (or domain-specific ontologies, e.g. GOLD2 or the GENE Ontology3) represent knowledge about a specific domain in a format that supports automated reasoning about the objects in that domain and the relations between them (cf. Erdmann 2001, 78). Word nets have been used in various applications of text processing, e.g. discourse parsing, lexical and thematic chaining, cohesion analyses, automatic segmentation and linking, anaphora resolution, and information extraction. When these applications process documents dealing with a specific domain, one needs to combine knowlegde about the domain-specific vocabulary represented in domain ontologies with lexical repositories representing general vocabulary (like PWN). In this context, it is useful to represent and interrelate the entities and relations in both types of resources using a common representation language. In our research group “Text-technological Information Modelling4” we chose OWL as a common format for this purpose. Since our projects are mainly concerned with German documents, we developed an OWL model that relates the German wordnet GermaNet (henceforth referred to as GN)5 with domain-specific ontologies in an approach that was inspired by the Plug-In model proposed in Magnini/Speranza (2002). Our approach is decribed in Kunze et al. (to appear); it was evaluated using representative subsets of GN and of the domain ontology TermNet6 (henceforth referred to as TN) as data and Protégé","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116352209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.87
Edda Leopold, J. Kindermann, G. Paass
We categorise contributions to an e-discussion platform using Classifier Induced Semantic Spaces and Self-Organising Maps. Analysing the contributions delivers insight into the nature of the communication process, makes it more comprehensible and renders the resulting decisions more transparent. Additionally, it can serve as a basis to monitor how the structure of the communication evolves over time. We evaluate our approach on a public ediscussion about an urban planning project, the Berlin Alexanderplatz, Germany. The proposed technique does not only produce high-level-features relevant to structure and monitor computer mediated communication, but also provides insight into how typical a particular document is for a specific category.
{"title":"Analysis of E-Discussions Using Classifier Induced Semantic Spaces","authors":"Edda Leopold, J. Kindermann, G. Paass","doi":"10.21248/jlcl.22.2007.87","DOIUrl":"https://doi.org/10.21248/jlcl.22.2007.87","url":null,"abstract":"We categorise contributions to an e-discussion platform using Classifier Induced Semantic Spaces and Self-Organising Maps. Analysing the contributions delivers insight into the nature of the communication process, makes it more comprehensible and renders the resulting decisions more transparent. Additionally, it can serve as a basis to monitor how the structure of the communication evolves over time. We evaluate our approach on a public ediscussion about an urban planning project, the Berlin Alexanderplatz, Germany. The proposed technique does not only produce high-level-features relevant to structure and monitor computer mediated communication, but also provides insight into how typical a particular document is for a specific category.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121208046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.95
Alexander Mehler, Peter Geibel, O. Pustylnikov
Texts can be distinguished in terms of their content, function, structure or layout (Brinker, 1992; Bateman et al., 2001; Joachims, 2002; Power et al., 2003). These reference points do not open necessarily orthogonal perspectives on text classification. As part of explorative data analysis, text classification aims at automatically dividing sets of textual objects into classes of maximum internal homogeneity and external heterogeneity. This paper deals with classifying texts into text types whose instances serve more or less homogeneous functions. Other than mainstream approaches, which rely on the vector space model (Sebastiani, 2002) or some of its descendants (Baeza-Yates and Ribeiro-Neto, 1999) and, thus, on content-related lexical features, we solely refer to structural dierentiae. That is, we explore patterns of text structure as determinants of class membership. Our starting point are tree-like text representations which induce feature vectors and tree kernels. These kernels are utilized in supervised learning based on cross-validation as a method of model selection (Hastie et al., 2001) by example of a corpus of press communication. For a subset of categories we show that classification can be performed very well by structural dierentia only.
文本可以根据其内容、功能、结构或布局来区分(Brinker, 1992;贝特曼等人，2001;约阿希姆,2002;Power et al.， 2003)。这些参考点并不一定打开文本分类的正交视角。作为探索性数据分析的一部分，文本分类旨在将文本对象集自动划分为最大内部同质性和最大外部异质性的类别。本文讨论将文本分类为文本类型，这些文本类型的实例或多或少具有同质功能。主流方法依赖于向量空间模型(Sebastiani, 2002)或它的一些后代(Baeza-Yates和Ribeiro-Neto, 1999)，因此，与内容相关的词汇特征不同，我们只参考结构差异。也就是说，我们探索文本结构模式作为阶级成员的决定因素。我们的出发点是树状的文本表示，它引出特征向量和树核。这些核被用于基于交叉验证的监督学习中，作为模型选择的一种方法(Hastie等人，2001)，以新闻传播语料库为例。对于类别的一个子集，我们表明仅通过结构差异可以很好地执行分类。
{"title":"Structural Classifiers of Text Types: Towards a Novel Model of Text Representation","authors":"Alexander Mehler, Peter Geibel, O. Pustylnikov","doi":"10.21248/jlcl.22.2007.95","DOIUrl":"https://doi.org/10.21248/jlcl.22.2007.95","url":null,"abstract":"Texts can be distinguished in terms of their content, function, structure or layout (Brinker, 1992; Bateman et al., 2001; Joachims, 2002; Power et al., 2003). These reference points do not open necessarily orthogonal perspectives on text classification. As part of explorative data analysis, text classification aims at automatically dividing sets of textual objects into classes of maximum internal homogeneity and external heterogeneity. This paper deals with classifying texts into text types whose instances serve more or less homogeneous functions. Other than mainstream approaches, which rely on the vector space model (Sebastiani, 2002) or some of its descendants (Baeza-Yates and Ribeiro-Neto, 1999) and, thus, on content-related lexical features, we solely refer to structural dierentiae. That is, we explore patterns of text structure as determinants of class membership. Our starting point are tree-like text representations which induce feature vectors and tree kernels. These kernels are utilized in supervised learning based on cross-validation as a method of model selection (Hastie et al., 2001) by example of a corpus of press communication. For a subset of categories we show that classification can be performed very well by structural dierentia only.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128065404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.96
J. Michaelis, Uwe Mönnich
It is a matter of fact that a long history in artificial intelligence and computational linguistics tries to develop tools to extract semantic knowledge from syntactic information. In particular, from a text technological point of view the general research perspective is to extract (semantic) information from annotated documents. Regarding this aim, some of the relevant annotation models used are:
{"title":"Towards a Logical Description of Trees in Annotation Graphs","authors":"J. Michaelis, Uwe Mönnich","doi":"10.21248/jlcl.22.2007.96","DOIUrl":"https://doi.org/10.21248/jlcl.22.2007.96","url":null,"abstract":"It is a matter of fact that a long history in artificial intelligence and computational linguistics tries to develop tools to extract semantic knowledge from syntactic information. In particular, from a text technological point of view the general research perspective is to extract (semantic) information from annotated documents. Regarding this aim, some of the relevant annotation models used are:","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128116242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.86
C. Sporleder
Wordnets are lexical reference systems that follow the design principles of the Princeton WordNet project (Fellbaum, ). Domain ontologies (or domain-specific ontologies such as GOLD, or the GENE Ontology) represent knowledge about a specific domain in a format that supports automated reasoning about the objects in that domain and the relations between them (Erdmann, ). In this paper, we will discuss how the Web Ontology Language OWL can be used to represent and interrelate the entities and relations in both types of resources. Our special focus will be on the question, whether synsets should be modelled as individuals (we use individual and instance as synonyms and will refer to this option as instance model) or as classes (we will refer to this option as class model). We will present three OWL models, each of which offers different solutions to this question. These models were developed in the context of the research group “Text-technological Modelling of Information” as a collaboration of the projects SemDok and HyTex. Since these projects are mainly concerned with German documents and with corpora that contain documents of a special technical or scientific domain, we used subsets of the German wordnet GermaNet (Kunze and Lemnitzer, ), henceforth referred to as GN, and the German domain ontology TermNet (Beiswenger et al., ), henceforth referred to as TN, to develop and evaluate the three models. To relate the general vocabulary of GN with the domain specific terms in TN, we developed an approach that was inspired by the plug-in model proposed by Magnini and Speranza (). In this approach, which has been developed in cooperation with the GermaNet research group (see Kunze et al. () for details), we adapted the OWL model for the English Princeton WordNet suggested by van Assem et al. () to GN, i.e. we modelled German synsets as instances of word-class-specific synset classes. For the reasons explained in section , we wanted to experiment with alternative models that implement the class model. In section we will present three alternative OWL representations for GN and TN and discuss their benefits and drawbacks.
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Pub Date : 2007-07-01DOI: 10.21248/jlcl.22.2007.89
Franziskus Geeb
Chatkommunikation im Sinne eines interaktiven, textbasierten Gesprächs von Internetnutzern als Teil des Internets ist in verschiedenen Benutzungszusammenhängen und für verschiedenste Anwendungen von Marketing bis Freizeit belegt. Als Chatpartner kommen neben anderen Internetnutzern aber auch Computer in Betracht, und auch diese Kommunikationsform ist sowohl in der Wirtschaft als auch im Privatgebrauch bekannt. Der Erfolg eines Chatroboters begründet sich dabei wesentlich in seiner Fähigkeit, einen Dialog mit dem Chatpartner zu führen und sinnvolle Aussagen zu machen. Als Wissensbasis für diese Kommunikation ist neben regelbasierten Verfahren auch ein Rückgriff auf fachlexikographische / terminologische Daten denkbar – nicht zuletzt in einer Fachkommunikation. Der vorliegende Beitrag versucht diese Problematik einzugrenzen und konzipiert Randbedingungen einer möglichen Umsetzung.
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Pub Date : 2006-07-01DOI: 10.21248/jlcl.21.2006.79
W. Zenk
LDV FORUM – Band 21(1) – 2006 Abstract Th is article presents UniTerm, a typical representative of terminology management systems (TMS). Th e fi rst part will highlight common characteristics of TMS and give further insight into the UniTerm entry format and database design. Practise has shown that automatic, i.e. blind exchange of terminologies is diffi cult to achieve. Th e second section gives criteria where the exchange between diff erent TMS can fail and points out the relationship between the UniTerm like TMS data formats and existing terminology standards. Finally, it will be discussed what requirements have to be met in order to enable a deeper integration of terminology standards in a TMS and thus also a smoother transition between diff erent TMS. Th ese requirements are evaluated with Acolada s next generation TMS UniTerm Enterprise.
{"title":"UniTerm - Formats and Terminology Exchange","authors":"W. Zenk","doi":"10.21248/jlcl.21.2006.79","DOIUrl":"https://doi.org/10.21248/jlcl.21.2006.79","url":null,"abstract":"LDV FORUM – Band 21(1) – 2006 Abstract Th is article presents UniTerm, a typical representative of terminology management systems (TMS). Th e fi rst part will highlight common characteristics of TMS and give further insight into the UniTerm entry format and database design. Practise has shown that automatic, i.e. blind exchange of terminologies is diffi cult to achieve. Th e second section gives criteria where the exchange between diff erent TMS can fail and points out the relationship between the UniTerm like TMS data formats and existing terminology standards. Finally, it will be discussed what requirements have to be met in order to enable a deeper integration of terminology standards in a TMS and thus also a smoother transition between diff erent TMS. Th ese requirements are evaluated with Acolada s next generation TMS UniTerm Enterprise.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114605350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-07-01DOI: 10.21248/jlcl.21.2006.80
Stefanie Geldbach
LDV FORUM – Band 21(1) – 2006 Abstract Th is paper discusses the question to what extent lexicon exchange in MT has been standardized during the last years. Th e introductory section is followed by a brief description of OLIF2, a format specifi cally designed for the exchange of terminological and lexicographical data (Section 2). Section 3 contains an overview of the import/ export functionalities of fi ve MT systems (Promt Expert 7.0, Systran 5.0 Professional Premium, Translate pro 8.0, LexShop 2.2, OpenLogos). Th is evaluation shows that despite the standardization eff orts of the last years the exchange of lexicographical data between MT systems is still not a straightforward task.
摘要本文讨论了近年来机器翻译中词汇交换标准化的程度。介绍部分之后是OLIF2的简要描述，OLIF2是一种专门为术语和词典数据交换而设计的格式(第2节)。第3节包含五个MT系统(prompt Expert 7.0, systeman 5.0 Professional Premium, Translate pro 8.0, LexShop 2.2, OpenLogos)的导入/导出功能概述。该评估表明，尽管过去几年的标准化努力，MT系统之间的词典编纂数据交换仍然不是一个直截了当的任务。
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Pub Date : 2006-07-01DOI: 10.21248/jlcl.21.2006.78
Uta Seewald-Heeg
LDV FORUM – Band 21(1) – 2006 Abstract Th e present article gives an overview over exchange formats supported by Terminology Management Systems (TMS) available on the market. As translation is one of the eldest application domains for terminology work, most terminology tools analyzed here are components of computer-aided translation (CAT) tools. In big corporates as well as in the localization industry, linguistic data, fi rst of all terminology, have to be shared by diff erent departments using diff erent systems, a situation that can be best solved by standardized formats. Th e evaluation of seven widely used TMS shows, however, that formats other than the standards proposed by organizations like LISA currently dominate the picture. In many cases, the only way to share data is to pass through fl at structured data stored as tab-delimited text fi les.
{"title":"Terminology Exchange without Loss? Feasibilities and Limitations of Terminology Management Systems (TMS)","authors":"Uta Seewald-Heeg","doi":"10.21248/jlcl.21.2006.78","DOIUrl":"https://doi.org/10.21248/jlcl.21.2006.78","url":null,"abstract":"LDV FORUM – Band 21(1) – 2006 Abstract Th e present article gives an overview over exchange formats supported by Terminology Management Systems (TMS) available on the market. As translation is one of the eldest application domains for terminology work, most terminology tools analyzed here are components of computer-aided translation (CAT) tools. In big corporates as well as in the localization industry, linguistic data, fi rst of all terminology, have to be shared by diff erent departments using diff erent systems, a situation that can be best solved by standardized formats. Th e evaluation of seven widely used TMS shows, however, that formats other than the standards proposed by organizations like LISA currently dominate the picture. In many cases, the only way to share data is to pass through fl at structured data stored as tab-delimited text fi les.","PeriodicalId":346957,"journal":{"name":"LDV Forum","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115088965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-07-01DOI: 10.21248/jlcl.21.2006.83
Georg Heeg
LDV FORUM Abstract Th is paper discusses a software design approach to allow interchange of linguistic data. It focuses on the modelling of the linguistic concepts represented in the data and describes the transfer between exchange formats as a multi-tier interpretation/generation. Th ese concepts are implemented in Smalltalk, a programming environment enabling fl exible conversion of data between formats supported by Terminology Management Systems (TMS).
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