By Gokhan Tur, Renato De Mori
Spoken language figuring out (SLU) is an rising box in among speech and language processing, investigating human/ computer and human/ human conversation by means of leveraging applied sciences from sign processing, trend acceptance, desktop studying and synthetic intelligence. SLU structures are designed to extract the which means from speech utterances and its purposes are substantial, from voice seek in cellular units to assembly summarization, attracting curiosity from either advertisement and educational sectors.
either human/machine and human/human communications can enjoy the program of SLU, utilizing differing initiatives and methods to higher comprehend and make the most of such communications. This ebook covers the state of the art techniques for the preferred SLU initiatives with chapters written through recognized researchers within the respective fields. Key gains contain:
- Presents a completely built-in view of the 2 special disciplines of speech processing and language processing for SLU initiatives.
- Defines what's attainable at the present time for SLU as an allowing expertise for company (e.g., buyer care facilities or corporation meetings), and patron (e.g., leisure, cellular, motor vehicle, robotic, or shrewdpermanent environments) functions and descriptions the foremost examine components.
- Provides a different resource of distilled info on equipment for laptop modeling of semantic info in human/machine and human/human conversations.
This ebook might be effectively used for graduate classes in electronics engineering, computing device technological know-how or computational linguistics. additionally, technologists drawn to processing spoken communications will locate it an invaluable resource of collated details of the subject drawn from the 2 specific disciplines of speech processing and language processing less than the recent zone of SLU.Content:
Chapter 1 creation (pages 1–7): Gokhan Tur and Renato De Mori
Chapter 2 historical past of information and tactics for Spoken Language figuring out (pages 9–40): Renato De Mori
Chapter three Semantic Frame?Based Spoken Language knowing (pages 41–91): Ye?Yi Wang, Li Deng and Alex Acero
Chapter four reason decision and Spoken Utterance type (pages 93–118): Gokhan Tur and Li Deng
Chapter five Voice seek (pages 119–146): Ye?Yi Wang, Dong Yu, Yun?Cheng Ju and Alex Acero
Chapter 6 Spoken query Answering (pages 147–170): Sophie Rosset, Olivier Galibert and Lori Lamel
Chapter 7 SLU in advertisement and learn Spoken discussion structures (pages 171–194): David Suendermann and Roberto Pieraccini
Chapter eight energetic studying (pages 195–224): Dilek Hakkani?Tur and Giuseppe Riccardi
Chapter nine Human/Human dialog realizing (pages 225–255): Gokhan Tur and Dilek Hakkani?Tur
Chapter 10 Named Entity reputation (pages 257–290): Frederic Bechet
Chapter eleven subject Segmentation (pages 291–317): Matthew Purver
Chapter 12 subject identity (pages 319–356): Timothy J. Hazen
Chapter thirteen Speech Summarization (pages 357–396): Yang Liu and Dilek Hakkani?Tur
Chapter 14 Speech Analytics (pages 397–416): I. Dan Melamed and Mazin Gilbert
Chapter 15 Speech Retrieval (pages 417–446): Ciprian Chelba, Timothy J. Hazen, Bhuvana Ramabhadran and Murat Saraclar
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Extra resources for Spoken Language Understanding: Systems for Extracting Semantic Information from Speech
Vq,d,k = ε) ∨ (vq,e,k = ε) ∨ (vq,d,k = vq,e,k )] where symbol ε represents the fact that the corresponding slot is empty. A more complex condition asserting the possibility of composing two values in a logic formula can also be considered. slq,e ) The predicate supp(x) is true when a pattern πm (x), associated with it matches the available hypotheses or data. Different values of m correspond to different template patterns to be matched with the data in order to assert the support. There may be multiple possible fusions, some of which may not have a support in the data.
In Pradhan et al. (2004), the parsing problem is formulated as a multi-class classification problem using a support vector machine (SVM) classifier whose scores are converted to probabilities with a sigmoid function. A parser based on tagging actions producing non-overlapping shallow tree structures is described in Hacioglu (2004). A sentence is considered to be a sequence of base phrases at a syntactic level. At a semantic level, predicate arguments have associated sequences of base phrases. The parse strategy is based on tagging actions and consists of three components that are sequentially applied to the input text for a chosen predicate to determine its arguments.
Shallow semantic parsing was mostly applied to written text processing with the purpose of creating a domain independent, predicate/argument representation of meaning (Gildea and Jurafsky, 2002). Most of the approaches to shallow parsing for written text use features for performing classification. In Hacioglu and Ward (2003), a classification is proposed in which the syntactic tree representation of a sentence is linearized into a sequence of non-terminal syntactic constituents. Each constituent is classified into semantic roles using context features.