1,282 research outputs found
Cyberthreat Detection from Twitter using Deep Neural Networks
To be prepared against cyberattacks, most organizations resort to security
information and event management systems to monitor their infrastructures.
These systems depend on the timeliness and relevance of the latest updates,
patches and threats provided by cyberthreat intelligence feeds. Open source
intelligence platforms, namely social media networks such as Twitter, are
capable of aggregating a vast amount of cybersecurity-related sources. To
process such information streams, we require scalable and efficient tools
capable of identifying and summarizing relevant information for specified
assets. This paper presents the processing pipeline of a novel tool that uses
deep neural networks to process cybersecurity information received from
Twitter. A convolutional neural network identifies tweets containing
security-related information relevant to assets in an IT infrastructure. Then,
a bidirectional long short-term memory network extracts named entities from
these tweets to form a security alert or to fill an indicator of compromise.
The proposed pipeline achieves an average 94% true positive rate and 91% true
negative rate for the classification task and an average F1-score of 92% for
the named entity recognition task, across three case study infrastructures
Syllable-based Neural Named Entity Recognition for Myanmar Language
Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar
natural language processing research work. In this work, NER for Myanmar
language is treated as a sequence tagging problem and the effectiveness of deep
neural networks on NER for Myanmar language has been investigated. Experiments
are performed by applying deep neural network architectures on syllable level
Myanmar contexts. Very first manually annotated NER corpus for Myanmar language
is also constructed and proposed. In developing our in-house NER corpus,
sentences from online news website and also sentences supported from
ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian
Language Treebank (ALT) project under ASEAN IVO. This paper contributes the
first evaluation of neural network models on NER task for Myanmar language. The
experimental results show that those neural sequence models can produce
promising results compared to the baseline CRF model. Among those neural
architectures, bidirectional LSTM network added CRF layer above gives the
highest F-score value. This work also aims to discover the effectiveness of
neural network approaches to Myanmar textual processing as well as to promote
further researches on this understudied language.Comment: Myanmar NE
Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets
Objective: After years of research, Twitter posts are now recognized as an
important source of patient-generated data, providing unique insights into
population health. A fundamental step to incorporating Twitter data in
pharmacoepidemiological research is to automatically recognize medication
mentions in tweets. Given that lexical searches for medication names may fail
due to misspellings or ambiguity with common words, we propose a more advanced
method to recognize them. Methods: We present Kusuri, an Ensemble Learning
classifier, able to identify tweets mentioning drug products and dietary
supplements. Kusuri ("medication" in Japanese) is composed of two modules.
First, four different classifiers (lexicon-based, spelling-variant-based,
pattern-based and one based on a weakly-trained neural network) are applied in
parallel to discover tweets potentially containing medication names. Second, an
ensemble of deep neural networks encoding morphological, semantical and
long-range dependencies of important words in the tweets discovered is used to
make the final decision. Results: On a balanced (50-50) corpus of 15,005
tweets, Kusuri demonstrated performances close to human annotators with 93.7%
F1-score, the best score achieved thus far on this corpus. On a corpus made of
all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26%
mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior
drug extraction system that compares running on such an extremely unbalanced
dataset. Conclusion: The system identifies tweets mentioning drug names with
performance high enough to ensure its usefulness and ready to be integrated in
larger natural language processing systems.Comment: This is a pre-copy-editing, author-produced PDF of an article
accepted for publication in JAMIA following peer review. The definitive
publisher-authenticated version is "D. Weissenbacher, A. Sarker, A. Klein, K.
O'Connor, A. Magge, G. Gonzalez-Hernandez, Deep neural networks ensemble for
detecting medication mentions in tweets, Journal of the American Medical
Informatics Association, ocz156, 2019
Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts
Despite the long history of named-entity recognition (NER) task in the
natural language processing community, previous work rarely studied the task on
conversational texts. Such texts are challenging because they contain a lot of
word variations which increase the number of out-of-vocabulary (OOV) words. The
high number of OOV words poses a difficulty for word-based neural models.
Meanwhile, there is plenty of evidence to the effectiveness of character-based
neural models in mitigating this OOV problem. We report an empirical evaluation
of neural sequence labeling models with character embedding to tackle NER task
in Indonesian conversational texts. Our experiments show that (1) character
models outperform word embedding-only models by up to 4 points, (2)
character models perform better in OOV cases with an improvement of as high as
15 points, and (3) character models are robust against a very high OOV
rate.Comment: Accepted in EMNLP 2018 Workshop on Noisy User-generated Text (W-NUT
Neural Adaptation Layers for Cross-domain Named Entity Recognition
Recent research efforts have shown that neural architectures can be effective
in conventional information extraction tasks such as named entity recognition,
yielding state-of-the-art results on standard newswire datasets. However,
despite significant resources required for training such models, the
performance of a model trained on one domain typically degrades dramatically
when applied to a different domain, yet extracting entities from new emerging
domains such as social media can be of significant interest. In this paper, we
empirically investigate effective methods for conveniently adapting an
existing, well-trained neural NER model for a new domain. Unlike existing
approaches, we propose lightweight yet effective methods for performing domain
adaptation for neural models. Specifically, we introduce adaptation layers on
top of existing neural architectures, where no re-training using the source
domain data is required. We conduct extensive empirical studies and show that
our approach significantly outperforms state-of-the-art methods.Comment: 11 pages, accepted as a long paper in EMNLP 201
Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
Adverse drug reactions (ADRs) are one of the leading causes of mortality in
health care. Current ADR surveillance systems are often associated with a
substantial time lag before such events are officially published. On the other
hand, online social media such as Twitter contain information about ADR events
in real-time, much before any official reporting. Current state-of-the-art in
ADR mention extraction uses Recurrent Neural Networks (RNN), which typically
need large labeled corpora. Towards this end, we propose a multi-task learning
based method which can utilize a similar auxiliary task (adverse drug event
detection) to enhance the performance of the main task, i.e., ADR extraction.
Furthermore, in the absence of auxiliary task dataset, we propose a novel joint
multi-task learning method to automatically generate weak supervision dataset
for the auxiliary task when a large pool of unlabeled tweets is available.
Experiments with 0.48M tweets show that the proposed approach outperforms the
state-of-the-art methods for the ADR mention extraction task by 7.2% in terms
of F1 score.Comment: Accepted at ECIR18 as full paper (12 pages
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone.Comment: The first two authors contributed equally to this wor
Detecting Cybersecurity Events from Noisy Short Text
It is very critical to analyze messages shared over social networks for cyber
threat intelligence and cyber-crime prevention. In this study, we propose a
method that leverages both domain-specific word embeddings and task-specific
features to detect cyber security events from tweets. Our model employs a
convolutional neural network (CNN) and a long short-term memory (LSTM)
recurrent neural network which takes word level meta-embeddings as inputs and
incorporates contextual embeddings to classify noisy short text. We collected a
new dataset of cyber security related tweets from Twitter and manually
annotated a subset of 2K of them. We experimented with this dataset and
concluded that the proposed model outperforms both traditional and neural
baselines. The results suggest that our method works well for detecting cyber
security events from noisy short text.Comment: Accepted February 2019 to North American Chapter of the Association
for Computational Linguistics (NAACL) 201
Learning Task-specific Representation for Novel Words in Sequence Labeling
Word representation is a key component in neural-network-based sequence
labeling systems. However, representations of unseen or rare words trained on
the end task are usually poor for appreciable performance. This is commonly
referred to as the out-of-vocabulary (OOV) problem. In this work, we address
the OOV problem in sequence labeling using only training data of the task. To
this end, we propose a novel method to predict representations for OOV words
from their surface-forms (e.g., character sequence) and contexts. The method is
specifically designed to avoid the error propagation problem suffered by
existing approaches in the same paradigm. To evaluate its effectiveness, we
performed extensive empirical studies on four part-of-speech tagging (POS)
tasks and four named entity recognition (NER) tasks. Experimental results show
that the proposed method can achieve better or competitive performance on the
OOV problem compared with existing state-of-the-art methods.Comment: This work has been accepted by IJCAI 201
A multimodal deep learning approach for named entity recognition from social media
Named Entity Recognition (NER) from social media posts is a challenging task.
User generated content that forms the nature of social media, is noisy and
contains grammatical and linguistic errors. This noisy content makes it much
harder for tasks such as named entity recognition. We propose two novel deep
learning approaches utilizing multimodal deep learning and Transformers. Both
of our approaches use image features from short social media posts to provide
better results on the NER task. On the first approach, we extract image
features using InceptionV3 and use fusion to combine textual and image
features. This presents more reliable name entity recognition when the images
related to the entities are provided by the user. On the second approach, we
use image features combined with text and feed it into a BERT like Transformer.
The experimental results, namely, the precision, recall and F1 score metrics
show the superiority of our work compared to other state-of-the-art NER
solutions
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