1,380 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
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Model (CDM) to standardize necessary data elements. It utilizes
UMLS to perform domain adaptation when integrating generic domain NLP tools. It
also features stand-off annotations that are specified by positional reference
to the original document. We built an interval tree based search engine to
efficiently query and retrieve the stand-off annotations by specifying
positional requirements. We also developed a utility to convert an inline
annotation format to stand-off annotations to enable the reuse of clinical text
datasets with inline annotations. We experimented with our system on several
NLP facilitated tasks including computational phenotyping for lymphoma patients
and semantic relation extraction for clinical notes. These experiments
showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape
A Formal Framework for Linguistic Annotation
`Linguistic annotation' covers any descriptive or analytic notations applied
to raw language data. The basic data may be in the form of time functions --
audio, video and/or physiological recordings -- or it may be textual. The added
notations may include transcriptions of all sorts (from phonetic features to
discourse structures), part-of-speech and sense tagging, syntactic analysis,
`named entity' identification, co-reference annotation, and so on. While there
are several ongoing efforts to provide formats and tools for such annotations
and to publish annotated linguistic databases, the lack of widely accepted
standards is becoming a critical problem. Proposed standards, to the extent
they exist, have focussed on file formats. This paper focuses instead on the
logical structure of linguistic annotations. We survey a wide variety of
existing annotation formats and demonstrate a common conceptual core, the
annotation graph. This provides a formal framework for constructing,
maintaining and searching linguistic annotations, while remaining consistent
with many alternative data structures and file formats.Comment: 49 page
CH-Bench: a user-oriented benchmark for systems for efficient distant reading (design, performance, and insights)
Data science deals with the discovery of information from large volumes of data. The data studied by scientists in the humanities include large textual corpora. An important objective is to study the ideas and expectations of a society regarding specific concepts, like âfreedomâ or âdemocracy,â both for todayâs society and even more for societies of the past. Studying the meaning of words using large corpora requires efficient systems for text analysis, so-called distant reading systems. Making such systems efficient calls for a specification of the necessary functionality and clear expectations regarding typical work loads. But this currently is unclear, and there is no benchmark to evaluate distant reading systems. In this article, we propose such a benchmark, with the following innovations: As a first step, we collect and structure various information needs of the target users. We then formalize the notion of word context to facilitate the analysis of specific concepts. Using this notion, we formulate queries in line with the information needs of users. Finally, based on this, we propose concrete benchmark queries. To demonstrate the benefit of our benchmark, we conduct an evaluation, with two objectives. First, we aim at insights regarding the content of different corpora, i.e., whether and how their size and nature (e.g., popular and broad literature or specific expert literature) affect results. Second, we benchmark different data management technologies. This has allowed us to identify performance bottlenecks
Towards a query language for annotation graphs
The multidimensional, heterogeneous, and temporal nature of speech databases
raises interesting challenges for representation and query. Recently,
annotation graphs have been proposed as a general-purpose representational
framework for speech databases. Typical queries on annotation graphs require
path expressions similar to those used in semistructured query languages.
However, the underlying model is rather different from the customary graph
models for semistructured data: the graph is acyclic and unrooted, and both
temporal and inclusion relationships are important. We develop a query language
and describe optimization techniques for an underlying relational
representation.Comment: 8 pages, 10 figure
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Temporal Information Processing: A Survey
Temporal Information Processing is a subfield of Natural Language Processing, valuable in many tasks like Question Answering and Summarization. Temporal Information Processing is broadened, ranging from classical theories of time and language to current computational approaches for Temporal Information Extraction. This later trend consists on the automatic extraction of events and temporal expressions. Such issues have attracted great attention especially with the development of annotated corpora and annotations schemes mainly TimeBank and TimeML. In this paper, we give a survey of Temporal Information Extraction from Natural Language texts
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