5,767 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
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The NOMAD system : expectation-based detection and correction of errors during understanding of syntactically and semantically ill-formed text
Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form (for example, newspaper stories and other edited texts). However, a great deal of natural language text (for example, memos, messages, rough drafts, conversation transcripts, etc.) have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, unclear or ambiguous interpretation, missing crucial punctuation, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word senses of words with multiple meanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora). This method of using expectations to aid the understanding of "scruffy" texts has bee incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy ship-to-shore messages
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Stealthy Plaintext
Correspondence through email has become a very significant way of communication at workplaces. Information of most kinds such as text, video and audio can be shared through email, the most common being text. With confidential data being easily sharable through this method most companies monitor the emails, thus invading the privacy of employees. To avoid secret information from being disclosed it can be encrypted. Encryption hides the data effectively but this makes the data look important and hence prone to attacks to decrypt the information. It also makes it obvious that there is secret information being transferred. The most effective way would be to make the information seem harmless by concealing the information in the email but not encrypting it. We would like the information to pass through the analyzer without being detected. This project aims to achieve this by “encrypting” plain text by replacing suspicious keywords with non-suspicious English words, trying to keep the grammatical syntax of the sentences intact
A Topic-Agnostic Approach for Identifying Fake News Pages
Fake news and misinformation have been increasingly used to manipulate
popular opinion and influence political processes. To better understand fake
news, how they are propagated, and how to counter their effect, it is necessary
to first identify them. Recently, approaches have been proposed to
automatically classify articles as fake based on their content. An important
challenge for these approaches comes from the dynamic nature of news: as new
political events are covered, topics and discourse constantly change and thus,
a classifier trained using content from articles published at a given time is
likely to become ineffective in the future. To address this challenge, we
propose a topic-agnostic (TAG) classification strategy that uses linguistic and
web-markup features to identify fake news pages. We report experimental results
using multiple data sets which show that our approach attains high accuracy in
the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019
World Wide Web Conference (WWW'19 Companion). Presented in the 2019
International Workshop on Misinformation, Computational Fact-Checking and
Credible Web (MisinfoWorkshop2019). 6 page
Generating natural language specifications from UML class diagrams
Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semistructured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet,a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
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