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pro vyhledávání: '"Ye-Yi Wang"'
Autor:
Yu Zhang, Zhihong Shen, Chieh-Han Wu, Boya Xie, Junheng Hao, Ye-Yi Wang, Kuansan Wang, Jiawei Han
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are often costly to obtain and suf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2062b83cc113ca4c9b294567f01e2480
http://arxiv.org/abs/2202.05932
http://arxiv.org/abs/2202.05932
Akademický článek
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Publikováno v:
SLT
State-of-the-art targeted language understanding systems rely on deep learning methods using 1-hot word vectors or off-the-shelf word embeddings. While word embeddings can be enriched with information from semantic lexicons (such as WordNet and PPDB)
Autor:
Dilek Hakkani-Tur, Gokhan Tur, Ye-Yi Wang, Asli Celikyilmaz, Li Deng, Yun-Nung Chen, Jianfeng Gao
Publikováno v:
INTERSPEECH
Sequence-to-sequence deep learning has recently emerged as a new paradigm in supervised learning for spoken language understanding. However, most of the previous studies explored this framework for building single domain models for each task, such as
Publikováno v:
IEEE Signal Processing Magazine. 28:50-60
Over the last decade, our ability to access, store, and consume huge amount of media and infor mation on mobile devices has skyrocketed. While this has allowed people who are on the go to be more entertained, informed, and con nected, the small-form
Publikováno v:
ACM Transactions on Information Systems. 28:1-20
Topical query classification, as one step toward understanding users' search intent, is gaining increasing attention in information retrieval. Previous works on this subject primarily focused on enrichment of query features, for example, by augmentin
Publikováno v:
IEEE Transactions on Audio, Speech, and Language Processing. 16:1207-1214
Traditional methods of spoken utterance classification (SUC) adopt two independently trained phases. In the first phase, an automatic speech recognition (ASR) module returns the most likely sentence for the observed acoustic signal. In the second pha
Publikováno v:
HLT-NAACL
Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number of natural language processing tasks. However, in most previous work, the models are learned based on either unsupervised objectives, which does not di
Autor:
Alex Acero, Ye-Yi Wang
Publikováno v:
Speech Communication. 48:390-416
To facilitate the development of spoken dialog systems and speech enabled applications, we introduce SGStudio (Semantic Grammar Studio), a grammar authoring tool that enables regular software developers with little speech/linguistic background to rap
Publikováno v:
SLT
We investigate the problem of entity ranking towards descriptive queries, that aims to match entities referred in user queries to entities of a large knowledge base (KB). Entity ranking faces the primary challenge of the sparseness of entity related