Now showing items 1-18 of 18

    • Analytics for Everyone 

      El Gebaly, Kareem (University of Waterloo, 2018-05-23)
      Analyzing relational data typically involves tasks that facilitate gaining familiarity or insights and coming up with findings or conclusions based on the data. This process is usually practiced by data experts, such as ...
    • Cross-Domain Sentence Modeling for Relevance Transfer with BERT 

      Akkalyoncu Yilmaz, Zeynep (University of Waterloo, 2019-12-16)
      Standard bag-of-words term-matching techniques in document retrieval fail to exploit rich semantic information embedded in the document texts. One promising recent trend in facilitating context-aware semantic matching has ...
    • Cross-Lingual Entity Matching for Knowledge Graphs 

      Yang, Hsiu-Wei (University of Waterloo, 2020-12-14)
      Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity matching is to align entities in a source language with their counterparts in target ...
    • dstlr: Scalable Knowledge Graph Construction from Text Collections 

      Clancy, Ryan (University of Waterloo, 2020-04-06)
      In recent years, the amount of data being generated for consumption by enterprises has increased exponentially. Enterprises typically work with structured data, but oftentimes the data being generated is semi-structured ...
    • End-to-end Neural Information Retrieval 

      Yang, Wei (University of Waterloo, 2019-04-30)
      In recent years we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search ...
    • An Experimental Analysis of Multi-Perspective Convolutional Neural Networks 

      Tu, Zhucheng (University of Waterloo, 2018-05-16)
      Modelling the similarity of sentence pairs is an important problem in natural language processing and information retrieval, with applications in tasks such as paraphrase identification and answer selection in question ...
    • Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits 

      Keshav Ram, Achyudh Ram (University of Waterloo, 2020-07-15)
      Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects and are known to suffer from inconsistent quality. Over the last two years, there has been ...
    • Generalization on Text-based Games using Structured Belief Representations 

      Adhikari, Ashutosh Devendrakumar (University of Waterloo, 2020-12-23)
      Text-based games are complex, interactive simulations where a player is asked to process the text describing the underlying state of the world to issue textual commands for advancing in a game. Playing these games can be ...
    • Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting 

      Lee, Jaejun (University of Waterloo, 2019-12-19)
      Used for simple voice commands and wake-word detection, keyword spotting (KWS) is the task of detecting pre-determined keywords in a stream of utterances. A common implementation of KWS involves transmitting audio samples ...
    • Learning to Rank in the Age of Muppets 

      Hu, Chengcheng (University of Waterloo, 2022-04-26)
      The emergence of BERT in 2018 has brought a huge boon to retrieval effectiveness in many tasks across various domains and led the recent research landscape of IR to transformer-related technologies. While researchers ...
    • The Limited Effectiveness of Neural Networks for Simple Question Answering on Knowledge Graphs 

      Mohammed, Salman (University of Waterloo, 2017-12-08)
      Simple factoid question answering (QA) is a task, where the questions can be answered by looking up a single fact in the knowledge base (KB). However, this QA task is difficult, since retrieving a single supporting fact ...
    • Machine Learning for Streamflow Prediction 

      Gauch, Martin (University of Waterloo, 2020-04-16)
      Accurate prediction of streamflow—the amount of water flowing past a stream section at a given time—is a long-standing challenge in hydrology. Not only do researchers strive to understand the natural processes at play, the ...
    • Neural Text Generation from Structured and Unstructured Data 

      Shahidi, Hamidreza (University of Waterloo, 2019-08-28)
      A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least ...
    • Serverless Data Analytics with Flint 

      Kim, Youngbin (University of Waterloo, 2018-08-30)
      Serverless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. ...
    • Simple Convolutional Neural Networks with Linguistically-Annotated Input for Answer Selection in Question Answering 

      Sequiera, Royal (University of Waterloo, 2018-08-10)
      With the advent of deep learning methods, researchers have been increasingly preferring deep learning methods over decades-old feature-engineering-inspired work in Natural Language Processing (NLP). The research community ...
    • Total Relation Recall: High-Recall Relation Extraction 

      Liu, Xinyu (University of Waterloo, 2021-04-28)
      As Knowledge Graphs (KGs) become important in a wide range of applications, including question-answering and recommender systems, more and more enterprises have recognized the value of constructing KGs with their own data. ...
    • Towards Effective Utilization of Pretrained Language Models — Knowledge Distillation from BERT 

      Liu, Linqing (University of Waterloo, 2020-09-02)
      In the natural language processing (NLP) literature, neural networks are becoming increasingly deeper and more complex. Recent advancements in neural NLP are large pretrained language models (e.g. BERT), which lead to ...
    • Unsupervised Syntactic Structure Induction in Natural Language Processing 

      Deshmukh, Anup Anand (University of Waterloo, 2021-09-07)
      This work addresses unsupervised chunking as a task for syntactic structure induction, which could help understand the linguistic structures of human languages especially, low-resource languages. In chunking, words of a ...

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