Page 31 - 2017 Brochure
P. 31
short messages. From this information we have developed sentiment analysis Research Description
techniques for both Chinese and English. We built one of the most popular Chinese
sentiment analysis toolkits, CSentiPackage, which includes sentiment dictionaries, Wen-Lian Hsu
scoring tools, and the deep neural network module, UTCNN. Using sentiment
analysis techniques, we have built Feelit and WordForce (web-post emotion and Distinguished Research Fellow
opinion visualization systems), EmotionPush (Android app for Facebook short
message emotion detection), RESOLVE (writing system for ESL learners to help them Hsin-Min Wang
read and express emotions), and GiveMeExample (example sentence suggestion
system for near-synonyms). Based on the excellent performance of these systems, Research Fellow
we will continue to improve and develop the newest technology to enable emotion
sensing in applications. Lun-Wei Ku
b) Semantic-Oriented Machine Translation Associate Research Fellow
We use deep syntactic structures with lexicon senses and case-label at each node.
An integrated statistical model is then used to discover the most likely combination Wei-Yun Ma
of parse-tree, lexicon senses and node-case-labels (the best path). After the desired
source semantic normal form is obtained, the corresponding target semantic normal Assistant Research Fellow
form and the target string is generated according to the patterns and parameters
automatically learned from those selected paths. For each unreachable sentence, a Ken-Yih Su
surrogate path will be created by searching the path (within the searching beam) that
possesses the maximum value of the speciļ¬ed function (of associated sentence- Research Fellow
level BLEU score and likelihood value).
c) Machine Reading
We will build a Chinese natural language understanding system based on various
analysis modules (word segmenter, parser, semantic role labeler, logic form
transformer, etc.) that we have previously built. We plan to start this long term
research project with a Chinese machine reading program, which can be evaluated
by reading comprehension tests. This project is expected to begin by reading
elementary school texts, and then gradually shift to high school-level and then real
domain-oriented applications (e.g., smart Q&A).
d) Spoken language processing
Our research topics include speaker recognition, spoken language recognition, voice
conversion, and spoken document retrieval/summarization. Recent achievements
include locally linear embedding-based approaches for voice conversion and post-
filtering, discriminative autoencoders for speech/speaker recognition, and novel
paragraph embedding methods for spoken document retrieval/ summarization.
Our group member, Dr.
Kuan-Yu Chen, received
the 2016 Postdoctoral
Academic Publication
Award from the Ministry of
Science and Technology
of Taiwan with a paper
on spoken document
summarization published
in COLING, 2016.
29
techniques for both Chinese and English. We built one of the most popular Chinese
sentiment analysis toolkits, CSentiPackage, which includes sentiment dictionaries, Wen-Lian Hsu
scoring tools, and the deep neural network module, UTCNN. Using sentiment
analysis techniques, we have built Feelit and WordForce (web-post emotion and Distinguished Research Fellow
opinion visualization systems), EmotionPush (Android app for Facebook short
message emotion detection), RESOLVE (writing system for ESL learners to help them Hsin-Min Wang
read and express emotions), and GiveMeExample (example sentence suggestion
system for near-synonyms). Based on the excellent performance of these systems, Research Fellow
we will continue to improve and develop the newest technology to enable emotion
sensing in applications. Lun-Wei Ku
b) Semantic-Oriented Machine Translation Associate Research Fellow
We use deep syntactic structures with lexicon senses and case-label at each node.
An integrated statistical model is then used to discover the most likely combination Wei-Yun Ma
of parse-tree, lexicon senses and node-case-labels (the best path). After the desired
source semantic normal form is obtained, the corresponding target semantic normal Assistant Research Fellow
form and the target string is generated according to the patterns and parameters
automatically learned from those selected paths. For each unreachable sentence, a Ken-Yih Su
surrogate path will be created by searching the path (within the searching beam) that
possesses the maximum value of the speciļ¬ed function (of associated sentence- Research Fellow
level BLEU score and likelihood value).
c) Machine Reading
We will build a Chinese natural language understanding system based on various
analysis modules (word segmenter, parser, semantic role labeler, logic form
transformer, etc.) that we have previously built. We plan to start this long term
research project with a Chinese machine reading program, which can be evaluated
by reading comprehension tests. This project is expected to begin by reading
elementary school texts, and then gradually shift to high school-level and then real
domain-oriented applications (e.g., smart Q&A).
d) Spoken language processing
Our research topics include speaker recognition, spoken language recognition, voice
conversion, and spoken document retrieval/summarization. Recent achievements
include locally linear embedding-based approaches for voice conversion and post-
filtering, discriminative autoencoders for speech/speaker recognition, and novel
paragraph embedding methods for spoken document retrieval/ summarization.
Our group member, Dr.
Kuan-Yu Chen, received
the 2016 Postdoctoral
Academic Publication
Award from the Ministry of
Science and Technology
of Taiwan with a paper
on spoken document
summarization published
in COLING, 2016.
29
