http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
이동준(Dongjun Lee),임유빈(Yubin Lim),권태경(Ted “Taekyoung” Kwon) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.5
Previous word embedding models such as word2vec and Glove are not able to learn the internal structure of words. This is a serious limitation for agglutinative languages with morphology such as Korean. In this paper, we propose a new model which is an expansion of the previous skip-gram model. This defines each word vector as a sum of its morpheme vectors and hence, learns the vectors of morphemes. To test the efficiency of our embedding, we conducted a word similarity test and a word analogy test. Furthermore, using our trained vectors on other NLP tasks, we tested how much performance actually had been enhanced.
Boosting Image Caption Generation with Parts of Speech
Philgoo Kang(강필구),Yubin Lim(임유빈),Hyoungjoo Kim(김형주) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.3
With the integration of smart devices and reliance on AI into our daily lives, the ability to generate image caption is becoming increasingly important in various fields such as guidance for visually-impaired individuals, human-computer interaction and so on. In this paper, we propose a novel approach based on parts of speech (POS), such as nouns and verbs extracted from image to enhance the image caption generation. The proposed model exploits multiple CNN encoders, which were specifically trained to identify features related to POS, and feed them into an LSTM decoder to generate image captions. We conducted experiments involving both Flickr30k and MS-COCO datasets using several text metrics and additional human surveys to validate the practical effectiveness of the proposed model.