Automatic glossary term extraction from large-scale requirements specifications

Creating glossaries for large corpora of requirments is an important but expensive task. Glossary term extraction methods often focus on achieving a high recall rate and, therefore, favor linguistic proecssing for extracting glossary term candidates and neglect the benefits from reducing the number of candidates by statistical filter methods. However, especially for large datasets a reduction of the likewise large number of candidates may be crucial. This paper demonstrates how to automatically extract relevant domain-specific glossary term candidates from a large body of requirements, the CrowdRE dataset. Our hybrid approach combines linguistic processing and statistical filtering for extracting and reducing glossary term candidates. In a twofold evaluation, we examine the impact of our approach on the quality and quantity of extracted terms. We provide a ground truth for a subset of the requirements and show that a substantial degree of recall can be achieved. Furthermore, we advocate requirements coverage as an additional quality metric to assess the term reduction that results from our statistical filters. Results indicate that with a careful combination of linguistic and statistical extraction methods, a fair balance between later manual efforts and a high recall rate can be achieved

Supporting the Development of Cyber-Physical Systems with Natural Language Processing: A Report

Software has become the driving force for innovations in any technical system that observes the environment with different sensors and influence it by controlling a number of actuators; nowadays called Cyber-Physical System (CPS). The development of such systems is inherently inter-disciplinary and often contains a number of independent subsystems. Due to this diversity, the majority of development information is expressed in natural language artifacts of all kinds. In this paper, we report on recent results that our group has developed to support engineers of CPSs in working with the large amount of information expressed in natural language. We cover the topics of automatic knowledge extraction, expert systems, and automatic requirements classification. Furthermore, we envision that natural language processing will be a key component to connect requirements with simulation models and to explain tool-based decisions. We see both areas as promising for supporting engineers of CPSs in the future

What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements Engineering (RE'17

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Ontology Population via NLP Techniques in Risk Management

In this paper we propose an NLP-based method for Ontology Population from texts and apply it to semi automatic instantiate a Generic Knowledge Base (Generic Domain Ontology) in the risk management domain. The approach is semi-automatic and uses a domain expert intervention for validation. The proposed approach relies on a set of Instances Recognition Rules based on syntactic structures, and on the predicative power of verbs in the instantiation process. It is not domain dependent since it heavily relies on linguistic knowledge. A description of an experiment performed on a part of the ontology of the PRIMA project (supported by the European community) is given. A first validation of the method is done by populating this ontology with Chemical Fact Sheets from Environmental Protection Agency . The results of this experiment complete the paper and support the hypothesis that relying on the predicative power of verbs in the instantiation process improves the performance.Information Extraction, Instance Recognition Rules, Ontology Population, Risk Management, Semantic Analysis

Automatic definition extraction using parser combinators

The automatic extraction of definitions from natural language texts has various applications such as the creation of glossaries and question-answering systems. In this paper we look at the extraction of definitions from non-technical texts using parser combinators in Haskell. We argue that this approach gives a general and compositional way of characterising natural language definitions. The parsers we develop are shown to be highly effective in the identification of definitions. Furthermore, we show how we can also automatically transform these parsers into other formats to be readily available for use within an eLearning system.peer-reviewe

Conclusions and implications of automation in space

Space facilities and programs are reviewed. Space program planning is discussed