1,260,997 research outputs found
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
Revisiting Unsupervised Relation Extraction
Unsupervised relation extraction (URE) extracts relations between named
entities from raw text without manually-labelled data and existing knowledge
bases (KBs). URE methods can be categorised into generative and discriminative
approaches, which rely either on hand-crafted features or surface form.
However, we demonstrate that by using only named entities to induce relation
types, we can outperform existing methods on two popular datasets. We conduct a
comparison and evaluation of our findings with other URE techniques, to
ascertain the important features in URE. We conclude that entity types provide
a strong inductive bias for URE.Comment: 8 pages, 1 figure, 2 tables. Accepted in ACL 202
Electron-phonon Interaction close to a Mott transition
The effect of Holstein electron-phonon interaction on a Hubbard model close
to a Mott-Hubbard transition at half-filling is investigated by means of
Dynamical Mean-Field Theory. We observe a reduction of the effective mass that
we interpret in terms of a reduced effective repulsion. When the repulsion is
rescaled to take into account this effect, the quasiparticle low-energy
features are unaffected by the electron-phonon interaction. Phonon features are
only observed within the high-energy Hubbard bands. The lack of electron-phonon
fingerprints in the quasiparticle physics can be explained interpreting the
quasiparticle motion in terms of rare fast processes.Comment: 4 pages, 3 color figures. Slightly revised text and references. Kondo
effect result added in Fig. 2 for comparison with DMFT dat
Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network
Automatically detecting sound units of humpback whales in complex
time-varying background noises is a current challenge for scientists. In this
paper, we explore the applicability of Convolution Neural Network (CNN) method
for this task. In the evaluation stage, we present 6 bi-class classification
experimentations of whale sound detection against different background noise
types (e.g., rain, wind). In comparison to classical FFT-based representation
like spectrograms, we showed that the use of image-based pretrained CNN
features brought higher performance to classify whale sounds and background
noise.Comment: arXiv admin note: text overlap with arXiv:1702.02741 by other author
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