15,780 research outputs found
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
Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation
We argue for a performance-based design of natural language grammars and
their associated parsers in order to meet the constraints imposed by real-world
NLP. Our approach incorporates declarative and procedural knowledge about
language and language use within an object-oriented specification framework. We
discuss several message-passing protocols for parsing and provide reasons for
sacrificing completeness of the parse in favor of efficiency based on a
preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines
to quickly grasp the informative and distinctive information of an entity.
Entities in most knowledge graphs (KGs) still lack such descriptions, thus
calling for automatic methods to supplement such information. However, existing
generative methods either overlook the grammatical structure or make factual
mistakes in generated texts. To solve these problems, we propose a
head-modifier template-based method to ensure the readability and data fidelity
of generated type descriptions. We also propose a new dataset and two automatic
metrics for this task. Experiments show that our method improves substantially
compared with baselines and achieves state-of-the-art performance on both
datasets.Comment: ACL 201
Building a Generation Knowledge Source using Internet-Accessible Newswire
In this paper, we describe a method for automatic creation of a knowledge
source for text generation using information extraction over the Internet. We
present a prototype system called PROFILE which uses a client-server
architecture to extract noun-phrase descriptions of entities such as people,
places, and organizations. The system serves two purposes: as an information
extraction tool, it allows users to search for textual descriptions of
entities; as a utility to generate functional descriptions (FD), it is used in
a functional-unification based generation system. We present an evaluation of
the approach and its applications to natural language generation and
summarization.Comment: 8 pages, uses eps
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary
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Knowledge dependencies in fuzzy information systems evaluation
Experience and research within the field of Information Systems Evaluation (ISE), has traditionally centered on providing tools and techniques for investment justification and appraisal, based upon explicit knowledge which encodes financial and other direct situational factors (such as accounting, costing and risk metrics). However, such approaches tend not to include additional causal interdependencies that are based upon tacit knowledge and are inherent within such a decision-making task. The authors show the results of applying a cognitive mapping approach, in the guise of a Fuzzy Cognitive Mapping (FCM) simulation, i.e. Fuzzy Information Systems Evaluation (F-ISE), in order to highlight the usefulness of applying such a technique. The authors highlight those contingent and necessary knowledge dependencies, in an exploratory sense, which relate to the investment appraisal decision-making task, in terms of the interplay between tacit and explicit knowledge, in this regard
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