Talk at SIREN 2008 on Speech Term Clouds

Manos Tsagkias

02 December 2008
Keywords: talk, siren, speech, information retrieval

I took part in SIREN 2008, a research event in the Netherlands, presenting our work with Martha Larson and Maarten de Rijke, on Term Clouds as Surrogates for User Generated Speech [1]; see post

References

[1] Manos Tsagkias, Martha Larson, and Maarten de Rijke. 2008. Term clouds as surrogates for user generated speech. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ‘08). Association for Computing Machinery, New York, NY, USA, 773–774. ACM Link PDF

Related Posts

  1. Abstract

    User generated spoken audio remains a challenge for Automatic Speech Recognition (ASR) technology and content-based audio surrogates derived from ASR-transcripts must be error robust. An investigation of the use of term clouds as surrogates for podcasts demonstrates that ASR term clouds closely approximate term clouds derived from human-generated transcripts across a range of cloud sizes. A user study confirms the conclusion that ASR-clouds are viable surrogates for depicting the content of podcasts.

    References

    [1] Manos Tsagkias, Martha Larson, and Maarten de Rijke. 2008. Term clouds as surrogates for user generated speech. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ‘08). Association for Computing Machinery, New York, NY, USA, 773–774. ACM Link PDF

    65.00% similar — Paper at SIGIR 2008 — Term Clouds as Surrogates for User Generated Speech

  2. References

    [1] Marguerite Fuller, Manos Tsagkias, Eamonn Newman, Jana Besser, Martha Larson, Gareth J.F. Jones, and Maarten de Rijke. 2008. Using Term Clouds to Represent Segment-Level Semantic Content of Podcasts. In Proceedings of the 2nd SIGIR Workshop on Searching Spontaneous Conversational Speech (SSCS 2008). UvA Link PDF

    53.82% similar — Paper at SSCS 2008 — Using Term Clouds to Represent Segment-Level Semantic Content of Podcasts

  3. Abstract

    We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subse- quently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.

    Happy to share the news about my first joint pubication with the Siri Speech team at Apple. Our short paper Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants with Christophe van Gysel, myself, Ernie Pusateri, and Ilya Oparin, is accepted at SIGIR 2020.

    29.49% similar — Paper at SIGIR 2020 — Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

  4. At the stage of AI2Future 1

    Figure 1. At the stage of AI2Future.

    AI2Future is a local, Croatian, initiative for disseminating the importance and for grounding the use of Artificial Intelligence. The conference brings together researchers, A.I. start ups, and large corporates from all around Croatia to share experiences and learn from each other on what A.I. can and cannot do.

    I was invited to give one of the two keynote talks. The first keynote focused on conversational agents and natural language processing and mine followed up with insights how search powers a large spectrum of applications from search, question and answering, and recommendations. I focused on the work we do at 904Labs and illustrated the principles of online learning to rank through real-world examples from our experimences with our customers.

    23.31% similar — Keynote speech at AI2Future

  5. I moved to Barcelona in the first week of September for a three-month internship at Yahoo! Research Labs. I’m very excited about it, and I’m looking forward to getting to know the people here and the problems they are working on.

    Update: My work at Yahoo! Research Labs resulted in a publication at SIGIR 2012 [1]; see [post] for the abstract(20120623-sigir2012-language-intent-models.md).

    References

    [1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF

    21.32% similar — Internship at Yahoo! Research Labs

  6. "Detecting Algorithmic Discrimination" by Carlos Castillo, presented at DIR 2016. Delft, the Netherlands.

    Figure 1. “Detecting Algorithmic Discrimination” by Carlos Castillo, presented at DIR 2016. Delft, the Netherlands.

    19.46% similar — Attending DIR 2016