Paper at ERD 2014 — Semanticizing Search Engine Queries

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1. Our performance in the Entity track was downright disappointing; the use of co-occurrence models led to poor results; an initial analysis shows that while our approach is able to find correct entity names, we fail to find homepages for these entities. For the relevance feedback track we find that a topical diversity approach provides good feedback documents. Further, we find that our relevance feedback algorithm seems to help most when there are sufficient relevant documents available.

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2.   For our experimental evaluation, we use data from Twitter, Digg, Delicious, the New York Times Community, Wikipedia, and the blogosphere to generate query models. We show that different query models, based on different data sources, provide complementary information and manage to retrieve different social media utterances from our target index. As a consequence, data fusion methods manage to significantly boost retrieval performance over individual approaches. Our graph-based term selection method is shown to help improve both effectiveness and efficiency.

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3. Today at 904Labs Search we’ve been having fun with the next version of our Query Intent/Understanding engine.

   If you don’t know what a Query Intent/Understanding engine is, here’s some background; otherwise keep reading.

   Our Query Intent/Understanding engine is closing to one year old, and it’s pretty cool: it can quickly boostrap a knowledge graph and continuously update it straight from our customer’s data. Since its introduction in September 2017, it has been tested in many different domains and languages and has shown substantial revenue uplift across customers.

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4. Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search.  

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5. 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.

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6. Abstract

   Offering access to information in microblog posts requires successful language identification. Language identification on sparse and noisy data can be challenging. In this paper we explore the performance of a state-of-the-art n-gram-based language identifier, and we introduce two semi-supervised priors to enhance performance at microblog post level: (i) blogger-basedprior, using previous posts by the same blogger, and (ii) link-based prior, using the pages linked to from the post. We test our models on five languages (Dutch, English, French, German, and Spanish), and a set of 1,000 tweets per language. Results show that our priors improve accuracy, but that there is still room for improvement.

   References

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7. This year I talked at the industry track where I shared my experiences from running a search startup in the Netherlands. I received positive feedback, and it was good to catch up with old friends and meet new people. If you want a short recap from the latest and greatest trends in search technology, read on my summary on 904Labs blog.

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