author = {Stoilos, Giorgos and Papasarantopoulos, Nikos and Vougiouklis, Pavlos and Bansky, Patrik},
  title = {Type Linking for Query Understanding and Semantic Search},
  year = {2022},
  isbn = {9781450393850},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3534678.3539067},
  doi = {10.1145/3534678.3539067},
  abstract = {Huawei is currently undertaking an effort to build map and web search services using query understanding and semantic search techniques. We present our efforts to built a low-latency type mention detection and linking service for map search. In addition to latency challenges, we only had access to low quality and biased training data plus we had to support 13 languages. Consequently, our service is based mostly on unsupervised term- and vector-based methods. Nevertheless, we trained a Transformer-based query tagger which we integrated with the rest of the pipeline using a reward and penalisation approach. We present techniques that we designed in order to address challenges with the type dictionary, incompatibilities in scoring between the term-based and vector-based methods as well as over-segmentation issues in Thai, Chinese, and Japanese. We have evaluated our approach on the Huawei map search use case as well as on community Question Answering benchmarks.},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {3931–3940},
  numpages = {10},
  keywords = {semantic search, deep learning, type linking, query annotation},
  location = {Washington DC, USA},
  series = {KDD '22}