What you always wanted to know about semantic transfer

The transfer in Verbmobil is primarily semantic-based. To further move up the level of abstractness, it integrates a variety of interlingual elements that allow the generation of alternative translations. In this report, we present the treatment and implementation of translational phenomena on both levels. Concerning the conceptual mapping level, we focus on problems of lexical and structural abstraction by generalization and decomposition. With respect to the semantic mapping level, we give an insight into the treatment of a wide range of structural divergences. Another topic of this report is the resolution of translational ambiguities which is relevant on both mapping levels. A catalog of examples will provide an overview over the various types of contextual constraints used for disambiguation

The Verbmobil semantic database

The distributed development of the modules of a large natural language processing system at different sites makes interface definitions a vital issue. It becomes even more urgent when several modules with the same intended functionality are developed in parallel and should be indistinguishable with respect to their input—output—behaviour. Another important issue is the acquisition and maintenance of lexical information which should be stored independently of an application to make it (re)usable for different purposes. This paper describes the design and use of the Verbmobil Semantic Database which we developed in order to deal with these issues in the area of lexical semantics in Verbmobil

German super and intensifiers in social communication

This study examines the intensifier super in German with data taken from Das Wortauskunftssystem zur deutschen Sprache in Geschichte und Gegenwart (DWDS, Digitales Wörterbuch der deutschen Sprache, Geyken 2020). We inspect the morphosyntactic and semantic development of super, and aim to explore its delexicalization tendency. In closing, the development of intensifiers in social communication is presented. The results show that German super enjoys morphosyntactic flexibility. The meaning of this versatile intensifier shifts freely among various magnitudes of intensification, from 'more than', 'very', to 'top most', and even has gone lower than the reference point to mean 'actually not good'. German super has advanced itself in terms of linguistic performance and is gradually losing its role as an intensifier. A diachronic inspection of the use of German super attests linguistic revival in that from a rarely used intensifier, super escalates its use in the last decades in German society. Outer world influences have put super through lexical competitions. Reports on general intensifiers reveal the inner linguistic motivation of change that intensifiers own, and sociolinguistic factors such as gender, age and education background all contribute to the variability of intensifiers in social communication

DFKI Workshop on Natural Language Generation

On the Saarbrücken campus sites as well as at DFKI, many research activities are pursued in the field of Natural Language Generation (NLG). We felt that too little is known about the total of these activities and decided to organize a workshop in order to share ideas and promote the results. This DFKI workshop brought together local researchers working on NLG. Several papers are co-authored by international researchers. Although not all NLG activities are covered in the present document, the papers reviewed for this workshop clearly demonstrate that Saarbrücken counts among the important NLG sites in the world

DFKI Workshop on Natural Language Generation

On the Saarbrücken campus sites as well as at DFKI, many research activities are pursued in the field of Natural Language Generation (NLG). We felt that too little is known about the total of these activities and decided to organize a workshop in order to share ideas and promote the results. This DFKI workshop brought together local researchers working on NLG. Several papers are co-authored by international researchers. Although not all NLG activities are covered in the present document, the papers reviewed for this workshop clearly demonstrate that Saarbrücken counts among the important NLG sites in the world

Réseau social numérique et images vectorielles : introduction à une communication à vocation internationale

Dans le contexte de la mondialisation, nous fondons ce travail préliminaire sur l'émergence de la relation entre les Réseaux Sociaux Numériques (RSN) et les systèmes artificiels de communication visuelle, à savoir la signalétique. Cette dernière sert à donner une information sur un sujet, pour faciliter la communication entre les usagers à l'échelle internationale. Ce système de communication visuelle a également une visée pragmatique : il doit conduire le destinataire à accomplir une action et/ ou il doit influer sur sa perception de la réalité. Le signagramme, qui est de type figuratif, est son unité d'écriture. Notre objectif est, tout d'abord, de concevoir un nouveau prototype d'un RSN de communication à vocation internationale, en nous servant de la signalétique et de l'outil de traduction automatique de syntagmes en signagrammes. Le SignaComm est l'intitulé de notre RSN : il est spécialisé et informatif. Ensuite, nous développons ce prototype en vue de tester ses capacités de communiquer des messages visuels à des usagers nationaux et internationaux. En guise d'exemple, nous traitons le cas d'une secousse sismique appartenant au domaine des risques et catastrophes naturels.Dans le contexte de la mondialisation, nous fondons ce travail préliminaire sur l'émergence de la relation entre les Réseaux Sociaux Numériques (RSN) et les systèmes artificiels de communication visuelle, à savoir la signalétique. Cette dernière sert à donner une information sur un sujet, pour faciliter la communication entre les usagers à l'échelle internationale. Ce système de communication visuelle a également une visée pragmatique : il doit conduire le destinataire à accomplir une action et/ ou il doit influer sur sa perception de la réalité. Le signagramme, qui est de type figuratif, est son unité d'écriture. Notre objectif est, tout d'abord, de concevoir un nouveau prototype d'un RSN de communication à vocation internationale, en nous servant de la signalétique et de l'outil de traduction automatique de syntagmes en signagrammes. Le SignaComm est l'intitulé de notre RSN : il est spécialisé et informatif. Ensuite, nous développons ce prototype en vue de tester ses capacités de communiquer des messages visuels à des usagers nationaux et internationaux. En guise d'exemple, nous traitons le cas d'une secousse sismique appartenant au domaine des risques et catastrophes naturels

Automatic lexicon acquisition from encyclopedia.

Lo, Ka Kan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 97-104).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.3Chapter 1.2 --- New paradigm in language learning --- p.5Chapter 1.3 --- Semantic Relations --- p.7Chapter 1.4 --- Contribution of this thesis --- p.9Chapter 2 --- Related Work --- p.13Chapter 2.1 --- Theoretical Linguistics --- p.13Chapter 2.1.1 --- Overview --- p.13Chapter 2.1.2 --- Analysis --- p.15Chapter 2.2 --- Computational Linguistics - General Learning --- p.17Chapter 2.3 --- Computational Linguistics - HPSG Lexical Acquisition --- p.20Chapter 2.4 --- Learning approach --- p.22Chapter 3 --- Background --- p.25Chapter 3.1 --- Modeling primitives --- p.26Chapter 3.1.1 --- Feature Structure --- p.26Chapter 3.1.2 --- Word --- p.28Chapter 3.1.3 --- Phrase --- p.35Chapter 3.1.4 --- Clause --- p.36Chapter 3.2 --- Wikipedia Resource --- p.38Chapter 3.2.1 --- Encyclopedia Text --- p.40Chapter 3.3 --- Semantic Relations --- p.40Chapter 4 --- Learning Framework - Syntactic and Semantic --- p.46Chapter 4.1 --- Type feature scoring function --- p.48Chapter 4.2 --- Confidence score of lexical entry --- p.50Chapter 4.3 --- Specialization and Generalization --- p.52Chapter 4.3.1 --- Further Processing --- p.54Chapter 4.3.2 --- Algorithm Outline --- p.54Chapter 4.3.3 --- Algorithm Analysis --- p.55Chapter 4.4 --- Semantic Information --- p.57Chapter 4.4.1 --- Extraction --- p.58Chapter 4.4.2 --- Induction --- p.60Chapter 4.4.3 --- Generalization --- p.63Chapter 4.5 --- Extension with new text documents --- p.65Chapter 4.6 --- Integrating the syntactic and semantic acquisition framework --- p.65Chapter 5 --- Evaluation --- p.68Chapter 5.1 --- Evaluation Metric - English Resource Grammar --- p.68Chapter 5.1.1 --- English Resource Grammar --- p.69Chapter 5.2 --- Experiments --- p.71Chapter 5.2.1 --- Tasks --- p.71Chapter 5.2.2 --- Evaluation Measures --- p.77Chapter 5.2.3 --- Methodologies --- p.78Chapter 5.2.4 --- Corpus Preparation --- p.79Chapter 5.2.5 --- Results --- p.81Chapter 5.3 --- Result Analysis --- p.85Chapter 6 --- Conclusions --- p.95Bibliography --- p.9

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical