<p>Understanding technical documentation is of vital importance, since instructions and descriptions are given about how technical products are used, maintained and repaired. Because of the increased economic globalization, more and more documentation is both written in English by non-native English writers, and delivered to non-native English readers. More and more documentation is also translated by means of computerized aids. In order to improve comprehension and translatability of technical documentation, controlled languages are created. Controlled languages are subsets of ordinary languages, but with restricted vocabularies and writing rules.</p><p>The aim of this report is to discuss the advantages and disadvantages for Swedish export companies to use Simplified Technical English (STE), which is a controlled language, for their technical documentation. In this work technical writers are asked about their opinions of STE. Additionally, technical texts written in traditional English are compared with versions written in STE, in order to find out whether texts written in a controlled language are easier to read or not. Within the comparison, the differences between the versions are discussed and a readability measurement is done. The measurement showed that readability in technical documentation is improved by using STE. The writers’ opinions are illuminated in three areas, which are: higher documentation quality, reduced translation costs and reduced production costs.</p>

Understanding technical documentation is of vital importance, since instructions and descriptions are given about how technical products are used, maintained and repaired. Because of the increased economic globalization, more and more documentation is both written in English by non-native English writers, and delivered to non-native English readers. More and more documentation is also translated by means of computerized aids. In order to improve comprehension and translatability of technical documentation, controlled languages are created. Controlled languages are subsets of ordinary languages, but with restricted vocabularies and writing rules. The aim of this report is to discuss the advantages and disadvantages for Swedish export companies to use Simplified Technical English (STE), which is a controlled language, for their technical documentation. In this work technical writers are asked about their opinions of STE. Additionally, technical texts written in traditional English are compared with versions written in STE, in order to find out whether texts written in a controlled language are easier to read or not. Within the comparison, the differences between the versions are discussed and a readability measurement is done. The measurement showed that readability in technical documentation is improved by using STE. The writers’ opinions are illuminated in three areas, which are: higher documentation quality, reduced translation costs and reduced production costs.

Knowledge base systems are brittle when the users of the knowledge base are unfamiliar with its content and structure. Querying a knowledge base requires users to state their questions in precise and complete formal representations that relate the facts in the question with relevant terms and relations in the underlying knowledge base. This requirement places a heavy burden on the users to become deeply familiar with the contents of the knowledge base and prevents novice users to effectively using the knowledge base for problem solving. As a result, the utility of knowledge base systems is often restricted to the developers themselves. The goal of this work is to help users, who may possess little domain expertise, to use unfamiliar knowledge bases for problem solving. Our thesis is that the difficulty in using unfamiliar knowledge bases can be addressed by an approach that funnels natural questions, expressed in English, into formal representations appropriate for automated reasoning. The approach uses a simplified English controlled language, a domain-neutral ontology, a set of mechanisms to handle a handful of well known question types, and a software component, called the Question Mediator, to identify relevant information in the knowledge base for problem solving. With our approach, a knowledge base user can use a variety of unfamiliar knowledge bases by posing their questions with simplified English to retrieve relevant information in the knowledge base for problem solving. We studied the thesis in the context of a system called ASKME. We evaluated ASKME on the task of answering exam questions for college level biology, chemistry, and physics. The evaluation consists of successive experiments to test if ASKME can help novice users employ unfamiliar knowledge bases for problem solving. The initial experiment measures ASKME's level of performance under ideal conditions, where the knowledge base is built and used by the same knowledge engineers. Subsequent experiments measure ASKME's level of performance under increasingly realistic conditions. In the final experiment, we measure ASKME's level of performance under conditions where the knowledge base is independently built by subject matter experts and the users of the knowledge base are a group of novices who are unfamiliar with the knowledge base. Results from the evaluation show that ASKME works well on different knowledge bases and answers a broad range of questions that were posed by novice users in a variety of domains. / text

Le langage mathématique courant et les langages mathématiques formelssont très éloignés. Par <<langage mathématique courant>> nousentendons la prose que le mathématicien utilise tous les jours dansses articles et ses livres. C'est une langue naturelle avec desexpressions symboliques et des notations spécifiques. Cette langue està la fois flexible et structurée mais reste sémantiquementintelligible par tous les mathématiciens.Cependant, il est très difficile de formaliser automatiquement cettelangue. Les raisons principales sont: la complexité et l'ambiguïté deslangues naturelles en général, le mélange inhabituel entre languenaturelle et notations symboliques tout aussi ambiguë et les sautsdans le raisonnement qui sont pour l'instant bien au-delà descapacités des prouveurs de théorèmes automatiques ou interactifs.Pour contourner ce problème, les assistants de preuves actuelsutilisent des langages formels précis dans un système logique biendéterminé, imposant ainsi de fortes restrictions par rapport auxlangues naturelles. En général ces langages ressemblent à des langagesde programmation avec un nombre limité de constructions possibles etune absence d'ambiguïté.Ainsi, le monde des mathématiques est séparé en deux, la vastemajorité qui utilise la langue naturelle et un petit nombre utilisantaussi des méthodes formelles. Cette seconde communauté est elle-mêmesubdivisée en autant de groupes qu'il y a d'assistants de preuves. Onperd alors l'intelligibilité des preuves pour tous les mathématiciens.Pour résoudre ce problème, on peut se demander:est-il possible d'écrire un programme qui comprend la langue naturellemathématique et qui la traduit vers un langage formel afin depermettre sa validation?Ce problème se subdivise naturellement en deux sous-problèmes tous lesdeux très difficiles:1. l'analyse grammaticale des textes mathématiques et leur traductiondans un langage formel,2. la validation des preuves écrites dans ce langage formel.Le but du projet MathNat (Mathematics in controlled Natural languages)est de faire un premier pas pour répondre à cette question trèsdifficile, en se concentrant essentiellement sur la première question.Pour cela, nous développons CLM (Controlled Language for Mathematics)qui est un sous-ensemble de l'anglais avec une grammaire et un lexiquerestreint, mais qui inclut tout de même quelques ingrédientsimportants des langues naturelles comme les pronoms anaphoriques, lesréférences, la possibilité d'écrire la même chose de plusieursmanières, des adjectifs distributifs ou non, ...Le second composant de MathNath est MathAbs (Mathematical Abstractlanguage). C'est un langage formel, indépendant du choix d'un systèmelogique permettant de représenter la sémantique des textes enpréservant leur structure et le fil du raisonnement. MathAbs est conçucomme un langage intermédiaire entre CLM et un système logique formelpermettant la vérification des preuves.Nous proposons un système qui permet de traduire CLM vers MathAbsdonnant ainsi une sémantique précise à CLM. Nous considèrons que cetravail est déjà un progrès notable, même si pour l'instant on estloin de pouvoir vérifier formellement toutes les preuves en MathAbsainsi générées.Pour le second problème, nous avons réalisé une petite expérience entraduisant MathAbs vers une liste de formules en logique du premierordre dont la validité garantit la correction de la preuve. Nous avonsensuite essayé de vérifier ces formules avec des prouveurs dethéorèmes automatiques validant ainsi quelques exemples. / There is a wide gap between the language of mathematics and itsformalized versions. The term "language of mathematics" or"mathematical language" refers to prose that the mathematician uses inauthoring textbooks and publications. It mainly consists of naturallanguage, symbolic expressions and notations. It is flexible,structured and semantically well-understood by mathematicians.However, it is very difficult to formalize it automatically. Some ofthe main reasons are: complex and rich linguistic features of naturallanguage and its inherent ambiguity; intermixing of natural languagewith symbolic mathematics causing problems which are unique of itskind, and therefore, posing more ambiguity; and the possibility ofcontaining reasoning gaps, which are hard to fill using the currentstate of art theorem provers (both automated and interactive).One way to work around this problem is to abandon the use of thelanguage of mathematics. Therefore in current state of art of theoremproving, mathematics is formalized manually in very precise, specificand well-defined logical systems. The languages supported by thesesystems impose strong restrictions. For instance, these languages havenon-ambiguous syntax with a limited number of possible syntacticconstructions.This enterprise divides the world of mathematics in two groups. Thefirst group consists of a vast majority of mathematicians whose relyon the language of mathematics only. In contrast, the second groupconsists of a minority of mathematicians. They use formal systems suchas theorem provers (interactive ones mostly) in addition to thelanguage of mathematics.To bridge the gap between the language of mathematics and itsformalized versions, we may ask the following gigantic question:Can we build a program that understands the language of mathematicsused by mathematicians and can we mechanically verify its correctness?This problem can naturally be divided in two sub-problems, both very hard:1. Parsing mathematical texts (mainly proofs) and translating thoseparse trees to a formal language after resolving linguistic issues.2. Verification of this formal version of mathematics.The project MathNat (Mathematics in controlled Natural language) aimsat being the first step towards solving this problem, focusing mainlyon the first question.First, we develop a Controlled Language for Mathematics (CLM) which isa precisely defined subset of English with restricted grammar anddictionary. To make CLM natural and expressive, we support some richlinguistic features such as anaphoric pronouns and references,rephrasing of a sentence in multiple ways and the proper handling ofdistributive and collective readings.Second, we automatically translate CLM to a system independent formaldescription language (MathAbs), with a hope to make MathNat accessibleto any proof checking system. Currently, we translate MathAbs intoequivalent first order formulas for verification.

Made available in DSpace on 2014-12-17T15:47:51Z (GMT). No. of bitstreams: 1 RaphaelMOCpdf.pdf: 3833849 bytes, checksum: fc73f8956766c49a68d58575132da8f2 (MD5) Previous issue date: 2009-06-29 / Researches in Requirements Engineering have been growing in the latest few years. Researchers are concerned with a set of open issues such as: communication between several user profiles involved in software engineering; scope definition; volatility and traceability issues. To cope with these issues a set of works are concentrated in (i) defining processes to collect client s specifications in order to solve scope issues; (ii) defining models to represent requirements to address communication and traceability issues; and (iii) working on mechanisms and processes to be applied to requirements modeling in order to facilitate requirements evolution and maintenance, addressing volatility and traceability issues. We propose an iterative Model-Driven process to solve these issues, based on a double layered CIM to communicate requirements related knowledge to a wider amount of stakeholders. We also present a tool to help requirements engineer through the RE process. Finally we present a case study to illustrate the process and tool s benefits and usage / Pesquisas em Engenharia de Requisitos tem crescido ao longo dos ?ltimos anos. Pesquisadores est?o preocupados com uma s?rie de problemas em aberto como: comunica??o entre diversos perf&#305;s envolvidos na engenharia de requisito; problemas na defini??o de escopo; problemas de volatilidade e ratreabilidade de requisitos. Para lidar com este conjunto de problemas em aberto, um conjunto de trabalhos est?o concentrados em (i) definir processos para coletar especifica??es de clientes para lidar com prolemas de escopo; (ii) definir modelos para representar requisitos para lidar com problemas de comunica??o e rastreabilidade; e (iii) defini??o de mecanismos e processos para serem aplicados a modelagem de requisitos para facilitar a evolu??o e manuten??o de requisitos, lidando com problemas de volatilidade e rastreabilidade em requisitos. Neste trabalho ? proposto um processo dirigido por modelo para solucionar estes problemas em aberto. Este processo ? baseado na ideia de um CIM dividido em duas camadas de forma que se consiga difundir o conhecimento relacionado ao requisitos para um n?mero maior de stakeholders. Durante este trabalho tamb?m foi desenvolvida uma ferramenta que tem como objetivo auxiliar a execu??o de tal processo. Por fim apresentamos um estudo de caso para ilustrar os benef?cios do uso da ferramenta e do processo