138,325 research outputs found
A Textual Case-Based Mobile Phone Diagnosis Support System
Java Cases and Ontology Libraries Integration for Building Reasoning Infrastructures (jCOLIBRI) is a framework which makes the development of Textual Case-Based Reasoning (CBR)
applications easier by providing the preprocessing of text methods, textual similarity methods and appropriate representation for textual cases which are the major techniques needed in any CBR systems. In this paper, a Mobile Phone Diagnosis Support System is presented as an extension to jCOLIBRI which accepts a problem and reasons with cases to provide a solution related to a new given problem. Experimental evaluation using some set of problems shows that the developed system predicts the solution that is relatively closer to the user given mobile phone problem. The solution also provide the user valuable advise on how to go about solving the new problem
A knowledge acquisition tool to assist case authoring from texts.
Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system
On legal texts and cases
Textual Case-Based Reasoning: Papers from the 1998 Workshop, Technical Report WS-98-12: pp. 40-50.The search employed by judicial
professionals when seeking for past similar
legal decisions is known as jurisprudence
research. Humans employ analogical
reasoning when comparing a given actual
situation with past decisions, noting the
affinities between them. In the process of
being reminded of a similar situation when
faced to a new one, Case-Based Reasoning
(CBR) systems simulate analogical
reasoning. Judicial professionals have two
sources of jurisprudence research: books
and database systems. The search in books
is time-consuming and imprecise due to the
limitations of humans' memory. Available
text database systems do not guarantee the
retrieval of useful documents. PRUDENTIA is
the case-based reasoner tailored to the
Brazilian system that confers efficiency to
jurisprudence research. Judicial cases are
described with natural language text,
comprising a collection of textual
documents. These texts are the experiences
that require case engineering to be modeled
in a structured representation of cases. We
have developed an automatic means of
performing the case engineering, that is,
converting legal texts into structured
representation of cases. Examples of
PRUDENTIA demonstrate the power of
similarity-based retrieval in a textual CBR
system against text database applications
improving the usefulness of the documents
retrieved
Knowledge Extraction and Summarization for Textual Case-Based Reasoning: A Probabilistic Task Content Modeling Approach
Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) technique that
has been successfully used for building knowledge systems for tasks/domains where different knowledge sources are easily available, particularly in the form of problem solving situations, known as cases. Cases generally display a clear
distinction between different components of problem solving, for instance, components of the problem description and of the problem solution. Thus, an existing and explicit structure of cases is presumed. However, when problem solving experiences are stored in the form of textual narratives (in natural language), there is no explicit case structure, so that CBR cannot be applied directly.
This thesis presents a novel approach for authoring cases from episodic textual
narratives and organizing these cases in a case base structure that permits a
better support for user goals. The approach is based on the following fundamental ideas:
- CBR as a problem solving technique is goal-oriented and goals are realized by
means of task strategies.
- Tasks have an internal structure that can be represented in terms of
participating events and event components.
- Episodic textual narratives are not random containers of domain concept
terms. Rather, the text can be considered as generated by the underlying
task structure whose content they describe.
The presented case base authoring process combines task knowledge with Natural
Language Processing (NLP) techniques to perform the needed knowledge extraction
and summarization
Reasoning on transition from manipulative strategies to general procedures in solving counting problems
We describe the procedures used by 11- to 12-year-old students for solving basic counting problems in order to analyse the transition from manipulative strategies involving direct counting to the use of the multiplication principle as a general procedure in combinatorial problems.
In this transition, the students sometimes spontaneously use tree diagrams and sometimes use numerical thinking strategies. We relate the findings of our research to recent research on the representational formats on the
learning of combinatorics, and reflect on the didactic implications of these investigations
Automatic case acquisition from texts for process-oriented case-based reasoning
This paper introduces a method for the automatic acquisition of a rich case
representation from free text for process-oriented case-based reasoning. Case
engineering is among the most complicated and costly tasks in implementing a
case-based reasoning system. This is especially so for process-oriented
case-based reasoning, where more expressive case representations are generally
used and, in our opinion, actually required for satisfactory case adaptation.
In this context, the ability to acquire cases automatically from procedural
texts is a major step forward in order to reason on processes. We therefore
detail a methodology that makes case acquisition from processes described as
free text possible, with special attention given to assembly instruction texts.
This methodology extends the techniques we used to extract actions from cooking
recipes. We argue that techniques taken from natural language processing are
required for this task, and that they give satisfactory results. An evaluation
based on our implemented prototype extracting workflows from recipe texts is
provided.Comment: Sous presse, publication pr\'evue en 201
Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases
We propose a comprehensive study of one-stage elicitation techniques for
querying a large pre-trained generative transformer (GPT-3.5-turbo) in the
rhetorical role prediction task of legal cases. This task is known as requiring
textual context to be addressed. Our study explores strategies such as zero-few
shots, task specification with definitions and clarification of annotation
ambiguities, textual context and reasoning with general prompts and specific
questions. We show that the number of examples, the definition of labels, the
presentation of the (labelled) textual context and specific questions about
this context have a positive influence on the performance of the model. Given
non-equivalent test set configurations, we observed that prompting with a few
labelled examples from direct context can lead the model to a better
performance than a supervised fined-tuned multi-class classifier based on the
BERT encoder (weighted F1 score of = 72%). But there is still a gap to reach
the performance of the best systems = 86%) in the LegalEval 2023 task which, on
the other hand, require dedicated resources, architectures and training
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