Logo des Repositoriums

Auflistung nach Schlagwort "Natural language processing"

1 - 10 von 14
  • Zeitschriftenartikel
    A Brief Tutorial on How to Extract Information from User-Generated Content (UGC)
    (KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Egger, Marc; Lang, André
    In this brief tutorial, we provide an overview of investigating text-based user-generated content for information that is relevant in the corporate context. We structure the overall process along three stages: collection, analysis, and visualization. Corresponding to the stages we outline challenges and basic techniques to extract information of different levels of granularity.
  • Zeitschriftenartikel
    An Interactive Narrative Format for Clinical Guidelines
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Cavazza, Marc; Charles, Fred; Lindsay, Alan; Siddle, Jonathan; Georg, Gersende
    Clinical guidelines are standardised documents, which summarise best practice in complex medical situations. Their target audience comprises health professionals, or in some cases patient groups, for whom they constitute important sources of patient education. These documents are characterised by a rich knowledge content, which also relies on a complex, largely implicit background. At the heart of guidelines is a set of recommendations describing expected behaviour throughout specific, evolving contexts. Such complex documents can be challenging to assimilate, in particular their patient education versions. The need to contextualise information and visualise behaviours and their consequences suggests the use of virtual environments, as in serious gaming. However, knowledge representation in serious games are often limited and the overall implementation mainly empirical. On the other hand, interactive narratives technologies have demonstrated their ability to embed complex behavioural knowledge and support principled behaviour responding to dynamic contexts. This is why they support the exploration of complex situations, their rehearsal, and the understanding of expected behaviour through what-if interaction. The narrative perspective also provides better user guidance than a pure simulation system, allowing mixed-initiative access to information. The translation of medical protocols as interactive narratives is faced with a number of knowledge representation challenges, in particular for the representation of non-compli-ance and the consequences of incorrect behaviour. Another technical issue is the need to represent both common sense and domain knowledge, and articulate their representation with the Planning domain that forms the backbone of the interactive narrative. As part of the Open FET project MUSE (FP7-296703), we are developing a proof-of-concept prototype exploring the above aspects, and embedding the logical structure of guidelines into a real-time interactive narrative, which provides a principled simulation of the situations faced by patients, which preserves causal and deontic constraints. This paper describes the knowledge engineering process supporting the development of this prototype, from the analysis of patient guidelines to the use of planning representations supporting the interactive narrative.
  • Zeitschriftenartikel
    Chatbot – Der digitale Helfer im Unternehmen: Praxisbeispiele der Schweizerischen Post
    (HMD Praxis der Wirtschaftsinformatik: Vol. 55, No. 4, 2018) Stucki, Toni; D’Onofrio, Sara; Portmann, Edy
    Chatbots gewinnen an Bedeutung. Sie ermöglichen uns Menschen, in natürlicher Sprache mit Computersystemen zu kommunizieren. Im einfachsten Fall extrahiert der Chatbot aus der Äusserung eines Benutzers dessen Intention, fragt fehlende Informationen in einer Wissensbank ab und bereitet dem Benutzer eine Antwort auf. Somit stellen Chatbots eine Schnittstelle zwischen Informationen und Nutzern dar. In einem Unternehmen können dadurch mehrere Vorteile generiert werden. Der Wissensfluss kann sowohl innerhalb des Unternehmens als auch in der Interaktion mit dem Kunden optimiert werden. Benutzer, seien es nun Mitarbeiter oder Kunden, erhalten von Chatbots schnell und in gleichbleibend hoher Qualität die gesuchten Informationen. Damit der menschliche Benutzer den sprechenden oder schreibenden Chatbot akzeptiert, sollte dieser angemessen kommunizieren. Was dies bedeutet, wie dies möglich wird und welche Potentiale Chatbots bieten, soll dieser Artikel anhand Praxisbeispielen der Schweizerischen Post diskutieren. Die kritische Reflexion der gewonnenen Erkenntnisse runden den Artikel ab. Chatbots are becoming increasingly important. They enable us humans to communicate with computer systems in natural language. In the simplest case, the chatbot extracts a user’s intention from his or her expression, asks for missing information in a knowledge base and prepares an answer for the user. Chatbots represent an interface between information and users. Several advantages can thus be generated in a company. The knowledge flow can be optimized within the company and in interaction with the customers. Users, whether they are employees or customers, quickly receive the information they are looking for from chatbots in consistently high quality. To accept the talking or writing chatbot, it should communicate appropriately. This article will discuss what this means, how this becomes possible and what potentials chatbots can offer, based on practical examples from Swiss Post. The article ends with a critical reflection on the lessons learned.
  • Zeitschriftenartikel
    Designing a Uniform Meaning Representation for Natural Language Processing
    (KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Van Gysel, Jens E. L.; Vigus, Meagan; Chun, Jayeol; Lai, Kenneth; Moeller, Sarah; Yao, Jiarui; O’Gorman, Tim; Cowell, Andrew; Croft, William; Huang, Chu-Ren; Hajič, Jan; Martin, James H.; Oepen, Stephan; Palmer, Martha; Pustejovsky, James; Vallejos, Rosa; Xue, Nianwen
    In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries.
  • Zeitschriftenartikel
    Dissertation Abstract:Learning High Precision Lexical Inferences
    (KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Shwartz, Vered
    The fundamental goal of natural language processing is to build models capable of human-level understanding of natural language. One of the obstacles to building such models is lexical variability , i.e. the ability to express the same meaning in various ways. Existing text representations excel at capturing relatedness (e.g. blue / red ), but they lack the fine-grained distinction of the specific semantic relation between a pair of words. This article is a summary of a Ph.D. dissertation submitted to Bar-Ilan University in 2019, under the supervision of Professor Ido Dagan of the Computer Science Department. The dissertation explored methods for recognizing and extracting semantic relationships between concepts ( cat is a type of animal ), the constituents of noun compounds (baby oil is oil for babies), and verbal phrases (‘X died at Y’ means the same as ‘X lived until Y’ in certain contexts). The proposed models outperform highly competitive baselines and improve the state-of-the-art in several benchmarks. The dissertation concludes in discussing two challenges in the way of human-level language understanding: developing more accurate text representations and learning to read between the lines.
  • Zeitschriftenartikel
    Grounding the Interaction: Knowledge Management for Interactive Robots
    (KI - Künstliche Intelligenz: Vol. 27, No. 2, 2013) Lemaignan, Séverin
    The dissertation tackles the broad question of knowledge representation and manipulation for companion robots. It first builds a taxonomy of the knowledge manipulation skills required by service robots, then proposes a novel active knowledge base that integrates into large cognitive architectures, and finally explores several applications, including natural language grounding.
  • Zeitschriftenartikel
    How are We Doing Today? Using Natural Speech Analysis to Assess Older Adults’ Subjective Well-Being
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Finze, Nikola; Jechle, Deinera; Faußer, Stefan; Gewald, Heiko
    The research presents the development and test of a machine learning (ML) model to assess the subjective well-being of older adults based solely on natural speech. The use of such technologies can have a positive impact on healthcare delivery: the proposed ML model is patient-centric and securely uses user-generated data to provide sustainable value not only in the healthcare context but also to address the global challenge of demographic change, especially with respect to healthy aging. The developed model unobtrusively analyzes the vocal characteristics of older adults by utilizing natural language processing but without using speech recognition capabilities and adhering to the highest privacy standards. It is based on theories of subjective well-being, acoustic phonetics, and prosodic theories. The ML models were trained with voice data from volunteer participants and calibrated through the World Health Organization Quality of Life Questionnaire (WHOQOL), a widely accepted tool for assessing the subjective well-being of human beings. Using WHOQOL scores as a proxy, the developed model provides accurate numerical estimates of individuals’ subjective well-being. Different models were tested and compared. The regression model proves beneficial for detecting unexpected shifts in subjective well-being, whereas the support vector regression model performed best and achieved a mean absolute error of 10.90 with a standard deviation of 2.17. The results enhance the understanding of the subconscious information conveyed through natural speech. This offers multiple applications in healthcare and aging, as well as new ways to collect, analyze, and interpret self-reported user data. Practitioners can use these insights to develop a wealth of innovative products and services to help seniors maintain their independence longer, and physicians can gain much greater insight into changes in their patients’ subjective well-being.
  • Zeitschriftenartikel
    Identifying Landscape Relevant Natural Language using Actively Crowdsourced Landscape Descriptions and Sentence-Transformers
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Baer, Manuel F.; Purves, Ross S.
    Natural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora ( Geograph and WikiHow ) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data.
  • Zeitschriftenartikel
    Intelligent User Assistance for Automated Data Mining Method Selection
    (Business & Information Systems Engineering: Vol. 62, No. 3, 2020) Zschech, Patrick; Horn, Richard; Höschele, Daniel; Janiesch, Christian; Heinrich, Kai
    In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results' practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.
  • Zeitschriftenartikel
    Internet Corpora: A Challenge for Linguistic Processing
    (Datenbank-Spektrum: Vol. 15, No. 1, 2015) Horbach, Andrea; Thater, Stefan; Steffen, Diana; Fischer, Peter M.; Witt, Andreas; Pinkal, Manfred
    Natural language processing tools are mostly developed for and optimized on newspaper texts, and often show a substantial performance drop when applied to other types of texts such as Twitter feeds, chat data or Internet forum posts. We explore a range of easy-to-implement methods of adapting existing part-of-speech taggers to improve their performance on Internet texts. Our results show that these methods can improve tagger performance substantially.