Improving the robustness to input errors on touch-based self-service kiosks and transportation apps

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Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context

An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data

Scalable Text Mining with Sparse Generative Models

The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods. This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines. The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places

Efficient Methods for Knowledge Base Construction and Query

Recently, knowledge bases have been widely used in search engines, question-answering systems, and many other applications. The abundant entity profiles and relational information in knowledge bases help the downstream applications learn more about the user queries. However, in automated knowledge base construction, ambiguity in data sources is one of the main challenges. Given a constructed knowledge base, it is hard to efficiently find entities of interest and extract their relatedness information from the knowledge base due to its large capacity. In this thesis, we adopt natural language processing tools, machine learning and graph/text query techniques to deal with such challenges. First, we introduce a machine-learning based framework for efficient entity linking to deal with the ambiguity issue in documents. For entity linking, deep-learning-based methods have outperformed traditional machine-learning-based ones but demand a large amount of data and have a high cost on the training time. We propose a lightweight, customisable and time-efficient method, which is based on traditional machine learning techniques. Our approach achieves comparable performances to the state-of-the-art deep learning-based ones while being significantly faster to train. Second, we adopt deep learning to deal with the Entity Resolution (ER) problem, which aims to reduce the data ambiguity in structural data sources. The existing BERT-based method has set new state-of-the-art performance on the ER task, but it suffers from the high computational cost due to the large cardinality to match. We propose to use Bert in a siamese network to encode the entities separately and adopt the blocking-matching scheme in a multi-task learning framework. The blocking module filters out candidate entity pairs that are unlikely to be matched, while the matching module uses an enhanced alignment network to decide if a pair is a match. Experiments show that our approach outperforms state-of-the-art models in both efficiency and effectiveness. Third, we proposed a flexible Query auto-completion (QAC) framework to support efficient error-tolerant QAC for entity queries in the knowledge base. Most existing works overlook the quality of the suggested completions, and the efficiency needs to be improved. Our framework is designed on the basis of a noisy channel model, which consists of a language model and an error model. Thus, many QAC ranking methods and spelling correction methods can be easily plugged into the framework. To address the efficiency issue, we devise a neighbourhood generation method accompanied by a trie index to quickly find candidates for the error model. The experiments show that our method improves the state of the art of error-tolerant QAC. Last but not least, we designed a visualisation system to facilitate efficient relatedness queries in a large-scale knowledge graph. Given a pair of entities, we aim to efficiently extract a succinct sub-graph to explain the relatedness of the pair of entities. Existing methods, either graph-based or list-based, all have some limitations when dealing with large complex graphs. We propose to use Bi-simulation to summarise the sub-graph, where semantically similar entities are combined. Our method exhibits the most prominent patterns while keeping them in an integrated graph

Rich Linguistic Structure from Large-Scale Web Data

The past two decades have shown an unexpected effectiveness of Web-scale data in natural language processing. Even the simplest models, when paired with unprecedented amounts of unstructured and unlabeled Web data, have been shown to outperform sophisticated ones. It has been argued that the effectiveness of Web-scale data has undermined the necessity of sophisticated modeling or laborious data set curation. In this thesis, we argue for and illustrate an alternative view, that Web-scale data not only serves to improve the performance of simple models, but also can allow the use of qualitatively more sophisticated models that would not be deployable otherwise, leading to even further performance gains.Engineering and Applied Science