136,649 research outputs found
Mapping Subsets of Scholarly Information
We illustrate the use of machine learning techniques to analyze, structure,
maintain, and evolve a large online corpus of academic literature. An emerging
field of research can be identified as part of an existing corpus, permitting
the implementation of a more coherent community structure for its
practitioners.Comment: 10 pages, 4 figures, presented at Arthur M. Sackler Colloquium on
"Mapping Knowledge Domains", 9--11 May 2003, Beckman Center, Irvine, CA,
proceedings to appear in PNA
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts
Enriching Knowledge Bases with Counting Quantifiers
Information extraction traditionally focuses on extracting relations between
identifiable entities, such as . Yet, texts
often also contain Counting information, stating that a subject is in a
specific relation with a number of objects, without mentioning the objects
themselves, for example, "California is divided into 58 counties". Such
counting quantifiers can help in a variety of tasks such as query answering or
knowledge base curation, but are neglected by prior work. This paper develops
the first full-fledged system for extracting counting information from text,
called CINEX. We employ distant supervision using fact counts from a knowledge
base as training seeds, and develop novel techniques for dealing with several
challenges: (i) non-maximal training seeds due to the incompleteness of
knowledge bases, (ii) sparse and skewed observations in text sources, and (iii)
high diversity of linguistic patterns. Experiments with five human-evaluated
relations show that CINEX can achieve 60% average precision for extracting
counting information. In a large-scale experiment, we demonstrate the potential
for knowledge base enrichment by applying CINEX to 2,474 frequent relations in
Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct
relations, which is 28% more than the existing Wikidata facts for these
relations.Comment: 16 pages, The 17th International Semantic Web Conference (ISWC 2018
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The Evolution of Student Engagement: Writing Improves Teaching in Introductory Biology
In response to calls for pedagogical reforms in undergraduate biology courses to decrease student attrition rates and increase active learning, this article describes one faculty member’s conversion from traditional teaching methods to more engaging forms of practice. Partially told as a narrative, this article illustrates a.) the way many faculty initially learn to teach by modeling the pedagogy from their own undergraduate programs; b.) the kind of support biology faculty may need to break out of traditional molds; c.) how writing can promote active learning; and d.) the impact of reformed pedagogy on student levels of engagement. The latter will be demonstrated through assessment results gathered from student surveys, reflective writing, and focus group interview. Ultimately, the study challenges misunderstandings some faculty might have regarding the value of writing in science classes and offers inspiration, urging critical reflection and persistence
Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
We participated in three of the protein-protein interaction subtasks of the
Second BioCreative Challenge: classification of abstracts relevant for
protein-protein interaction (IAS), discovery of protein pairs (IPS) and text
passages characterizing protein interaction (ISS) in full text documents. We
approached the abstract classification task with a novel, lightweight linear
model inspired by spam-detection techniques, as well as an uncertainty-based
integration scheme. We also used a Support Vector Machine and the Singular
Value Decomposition on the same features for comparison purposes. Our approach
to the full text subtasks (protein pair and passage identification) includes a
feature expansion method based on word-proximity networks. Our approach to the
abstract classification task (IAS) was among the top submissions for this task
in terms of the measures of performance used in the challenge evaluation
(accuracy, F-score and AUC). We also report on a web-tool we produced using our
approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our
approach to the full text tasks resulted in one of the highest recall rates as
well as mean reciprocal rank of correct passages. Our approach to abstract
classification shows that a simple linear model, using relatively few features,
is capable of generalizing and uncovering the conceptual nature of
protein-protein interaction from the bibliome. Since the novel approach is
based on a very lightweight linear model, it can be easily ported and applied
to similar problems. In full text problems, the expansion of word features with
word-proximity networks is shown to be useful, though the need for some
improvements is discussed
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