This paper describes the Kyoto system for Temporal Intent Disambiguation (TID) subtask in the NTCIR-12 Temporal Information Access (Temporalia-2) challenge. The task is to estimate the distribution of temporal intents (Past, Recency, Future, Atemporal) of a given query. We took a supervised machine learning approach, using features of bag of words, POS and word vectors. We also incorporated kno...

Text classification in the medical domain is a real world problem with wide applicability. This paper investigates extensively the effect of text representation approaches on the performance of medical document classification. To accomplish this objective, we evaluated seven different approaches to represent real word medical documents. The text representation approaches investigated in this pa...

This study aimed to explore the effect of rote learning (word list learning) on boosting visual and auditory learners' vocabulary retention. To this end, the Oxford Placement Test (2007) was administered, in order to identify the learners' proficiency level. Then 31 subjects who were studying at the Iran Language Institute (ILI) in Bushehr, Iran, and belonged to available sampling were singled ...

This project deals with the problem of Visual Question Answering (VQA). We develop neural network-based models to answer open-ended questions that are grounded in images. We used the newly released VQA dataset (with about 750K questions) to carry out our experiments. Our model makes use of two popular neural network architecture: Convolutional Neural Nets (CNN) and Long Short Term Memory Networ...

This paper presents Aicyber's system for NLPCC 2017 shared task 2. It is formed by a voting of three deep learning based system trained on character-enhanced word vectors and a well known bag-of-word model.

An effective and efficient visual word selection method based on Bag-of-features (BoF), which can be applied to the pedestrian detection problem, is proposed in this paper. We first calculate the difference in the total appearance frequency of each visual word in pedestrian and non-pedestrian images. Visual words that exhibit greater absolute values are more efficient for pedestrian detection, ...

For most entity disambiguation systems, the secret recipes are feature representations for mentions and entities, most of which are based on Bag-of-Words (BoW) representations. Commonly, BoW has several drawbacks: (1) It ignores the intrinsic meaning of words/entities; (2) It often results in high-dimension vector spaces and expensive computation; (3) For different applications, methods of desi...

Classifying realistic human actions in video remains challenging for existing intro-variability and inter-ambiguity in action classes. Recently, Spatial-Temporal Interest Point (STIP) based local features have shown great promise in complex action analysis. However, these methods have the limitation that they typically focus on Bag-of-Words (BoW) algorithm, which can hardly discriminate actions...