Natural Language Processing approach to NLP Meta model automation

Neuro Linguistic Programming (NLP) is one of the most utilised approaches for personality development and Meta model is one of the most important techniques in this process. Usually, when one speaks about a problem or a situation, the words that one chooses will delete, distort or generalize portions of their experience. Meta model, which is a set of specific questions or language patterns, can be used to understand and recover the information hidden behind the words used. This technique can be adopted to understand other people’s problems or enable them to understand their own issues better. Applying the Meta Model, however, requires a great level of skill and experience for correct identification of deletion, distortion and generalization. Using the appropriate recovery questions is challenging for NLP practitioners and Psychologists. Moreover, the efficiency and accuracy of existing methods on the Meta model can potentially be hindered by human errors such as personal judgment or lack of experience and skill. This research aims to automate the process of using the Meta Model in conversation in order to eliminate human errors, thereby increasing the efficiency and accuracy of this method. An intelligent software has been developed using Natural Language Processing, with the ability to apply the Meta model techniques during conversation with its user. Comparisons of this software with performance of an established NLP practitioner have shown increased accuracy in identification of the deletion and generalization processes. Recovery of information has also been more efficient in the software in comparison to an NLP practitioner

Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments

Using natural language processing to support peer‐feedback in the age of artificial intelligence: a cross‐disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Cluster analysis on radio product integration testing faults

Abstract. Nowadays, when the different software systems keep getting larger and more complex, integration testing is necessary to ensure that the different components of the system work together correctly. With the large and complex systems the analysis of the test faults can be difficult, as there are so many components that can cause the failure. Also with the increased usage of automated tests, the faults can often be caused by test environment or test automation issues. Testing data and logs collected during the test executions are usually the main source of information that are used for test fault analysis. With the usage of text mining, natural language processing and machine learning methods, the fault analysis process is possible to be automated using the data and logs collected from the tests, as multiple studies have shown in the recent years. In this thesis, an exploratory data study is done on data collected from radio product integration tests done at Nokia. Cluster analysis is used to find the different fault types that can be found from each of the collected file types. Different feature extraction methods are used and evaluated in terms of how well they separate the data for fault analysis. The study done on this thesis paves the way for automated fault analysis in the future. The introduced methods can be applied for classifying the faults and the results and findings can be used to determine what are the next steps that can be taken to enable future implementations for automated fault analysis applications.Radiotuotteiden integraatiotestauksen vikojen klusterianalyysi. Tiivistelmä. Nykypäivänä, kun erilaiset ohjelmistojärjestelmät jatkavat kasvamista ja muuttuvat monimutkaisimmaksi, integraatiotestaus on välttämätöntä, jotta voidaan varmistua siitä, että järjestelmän eri komponentit toimivat yhdessä oikein. Suurien ja monimutkaisten järjestelmien testivikojen analysointi voi olla vaikeaa, koska järjestelmissä on mukana niin monta komponenttia, jotka voivat aiheuttaa testien epäonnistumisen. Testien automatisoinnin lisääntymisen myötä testit voivat usein epäonnistua myös johtuen testiympäristön tai testiautomaation ongelmista. Testien aikana kerätty testidata ja testilogit ovat yleensä tärkein tiedonlähde testivikojen analyysissä. Hyödyntämällä tekstinlouhinnan, luonnollisen kielen käsittelyn sekä koneoppimisen menetelmiä, testivikojen analyysiprosessi on mahdollista automatisoida käyttämällä testien aikana kerättyä testidataa ja testilogeja, kuten monet tutkimukset ovat viime vuosina osoittaneet. Tässä tutkielmassa tehdään eksploratiivinen tutkimus Nokian radiotuotteiden integraatiotesteistä kerätyllä datalla. Erilaiset vikatyypit, jotka voidaan löytää kustakin kerätystä tiedostotyypistä, löydetään käyttämällä klusterianalyysiä. Ominaisuusvektorien laskentaan käytetään eri menetelmiä ja näiden menetelmien kykyä erotella dataa vika-analyysin näkökulmasta arvioidaan. Tutkielmassa tehty tutkimus avaa tietä vika-analyysien automatisoinnille tulevaisuudessa. Esitettyjä menetelmiä voidaan käyttää vikojen luokittelussa ja tuloksien perusteella voidaan määritellä, mitkä ovat seuraavia askelia, jotta vika-analyysiprosessia voidaan automatisoida tulevaisuudessa

Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit