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Knowledge Graphs and Linked Big Data Resources for Conversational Understanding

Interspeech 2014 Tutorial Web Page

State-of-the-art statistical spoken language processing typically requires significant manual effort to construct domain-specific schemas (ontologies) as well as manual effort to annotate training data against these schemas. At the same time, a recent surge of activity and progress on semantic web-related concepts from the large search-engine companies represents a potential alternative to the manually intensive design of spoken language processing systems. Standards such as schema.org have been established for schemas (ontologies) that webmasters can use to semantically and uniformly markup their web pages. Search engines like Bing, Google, and Yandex have adopted these standards and are leveraging them to create semantic search engines at the scale of the web. As a result, the open linked data resources and semantic graphs covering various domains (such as Freebase [3]) have grown massively every year and contains far more information than any single resource anywhere on the Web. Furthermore, these resources contain links to text data (such as Wikipedia pages) related to the knowledge in the graph.

Recently, several studies on speech language processing started exploiting these massive linked data resources for language modeling and spoken language understanding. This tutorial will include a brief introduction to the semantic web and the linked data structure, available resources, and querying languages. An overview of related work on information extraction and language processing will be presented, where the main focus will be on methods for learning spoken language understanding models from these resources.

Publications

Other References:

(Tur et al., 2012a) Gokhan Tur, Li Deng, Dilek Hakkani-Tur, and Xiaodong He, Towards Deeper Understanding Deep Convex Networks for Semantic Utterance Classification, IEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP), March 2012.

(Deoras et al., 2013) Anoop Deoras, Gokhan Tur, Ruhi Sarikaya, and Dilek Hakkani-Tur, Joint Discriminative Decoding of Word and Semantic Tags for Spoken Language Understanding, in IEEE Transactions on Audio, Speech, and Language Processing, IEEE, 2013.

(Tur et al., 2013) Gokhan Tur, Anoop Deoras, and Dilek Hakkani-Tur, Semantic Parsing Using Word Confusion Networks With Conditional Random Fields, Annual Conference of the International Speech Communication Association (Interspeech), September 2013.

(Jeong and Lee, 2008) M. Jeong and G. G. Lee, Triangular-chain conditional random fields, IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 7, pp. 1287–1302, Sep. 2008.

(Puyang and Xu, 2013) P. Xu and R. Sarikaya, Convolutional neural network based triangular CRF for joint intent detection and slot filling, in ASRU, 2013.

(Kennington et al., 2013) C. Kennington, S. Kousidis, and D. Schlangen. Interpreting situated dialogue utterances: an update model that uses speech, gaze, and gesture information. In Proceedings of SIGDial, 2013.

(Chotimongkol & Rudnicky, 2002) Ananlada Chotimongkol and Alexander I. Rudnicky, Automatic Concept Identification in Goal-Oriented Conversations, In Proceedings of ICSLP 2002, Denver, Colorado, 2002.

(Tur, Hakkani-Tur, Chotimongkol, 2005) Gokhan Tur, Dilek Hakkani-Tür, Ananlada Chotimongkol, “Semi-Supervised Learning for Spoken Language Understanding Using Semantic Role Labeling”, In the proceedings of IEEE ASRU-2005, Puerto Rico, November, 2005.

(Chen, Wang, Rudnicky, 2013) Yun-Nung Chen, William Yang Wang, and Alexander I. Rudnicky, Unsupervised Induction and Filling of Semantic Slots for Spoken Dialogue Systems Using Frame-Semantic Parsing, in Proceedings of ASRU 2013, Olomouc, Czech Republic, Dec 9-12, 2013.

(Craswell & Szummer, 2007) N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 239-246, New York, NY, USA, 2007. ACM.

(Pound et al, 2012) J. Pound, A. K. Hudek, I. F. Ilyas, and G. Weddell. Interpreting keyword queries over Web knowledge bases. In CIKM, 2012.

(Wu & Weld, 2007) Fei Wu, and Daniel S. Weld. Autonomously semantifying wikipedia. Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. 2007.

(Mintz et al., 2009) Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pp. 1003-1011. Association for Computational Linguistics, 2009.

(Cai & Yates, 2013) Qingqing Cai and Alexander Yates. Large-scale semantic parsing via schema matching and lexicon extension. In Proceedings of ACL 2013.

(Berant et al., 2013) Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. Semantic Parsing on Freebase from Question-Answer Pairs. In Proceedings of EMNLP 2013.

(Cheung & Li, 2012) Jackie Chi Kit Cheung and Xiao Li. Sequence clustering and labeling for unsupervised query intent discovery. Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 2012.

Slides

NL-SPARQL Data sets

Neural Wikipedia Embeddings (coming soon)