AIMC Topic: Vocabulary
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Identifying tweets of personal health experience through word embedding and LSTM neural network.
BMC bioinformatics
Jun 13, 2018
BACKGROUND: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorit...
A lexicon based method to search for extreme opinions.
PloS one
May 25, 2018
Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of ...
Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.
Cognitive science
Apr 6, 2018
Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work i...
Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.
International journal of neural systems
Feb 22, 2018
Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding ...
"What is relevant in a text document?": An interpretable machine learning approach.
PloS one
Aug 11, 2017
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate ve...
Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.
Computational intelligence and neuroscience
Oct 3, 2016
We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obt...
A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.
Computational intelligence and neuroscience
Jun 29, 2016
Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically Engl...
A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.
Computational intelligence and neuroscience
May 22, 2016
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, sin...
What can Neighbourhood Density effects tell us about word learning? Insights from a connectionist model of vocabulary development.
Journal of child language
Feb 17, 2016
In this paper, we investigate the effect of neighbourhood density (ND) on vocabulary size in a computational model of vocabulary development. A word has a high ND if there are many words phonologically similar to it. High ND words are more easily lea...
Minimalistic toy robot to analyze a scenery of speaker-listener condition in autism.
Cognitive processing
Feb 12, 2016
Atypical neural architecture causes impairment in communication capabilities and reduces the ability of representing the referential statements of other people in children with autism. During a scenery of "speaker-listener" communication, we have ana...
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