Global ETD Search

1	利用隱含回饋提供搜尋引擎的自動查詢修正 / Automatic Query Refinement in Web Search Engines using Implicit Feedback 彭冠誌, Peng，Kuan-Chih Unknown Date (has links) 隨著全球資訊網蓬勃的發展，可以幫助使用者根據關鍵字搜尋相關資訊的搜尋引擎也已變成使用者不可或缺的工具之一。但對於搜尋引擎生手而言，往往不知道該如何地輸入適當的關鍵字，導致搜尋結果不如預期。如果搜尋引擎可以提供自動查詢修正(Automatic Query Refinement)的功能，將可以有效地幫助生手在網路上找尋到其想要的資訊。因此，如何得知使用者的資訊需求，如何自動化地達到查詢修正，則成為重要的課題之一。本研究利用使用者的隱含回饋(Implicit Feedback)來分析使用者的資訊需求，並探勘過去具有相同資訊需求的使用者經驗，以幫助搜尋引擎生手有效地搜尋網頁，以達到自動查詢修正的目的。本研究中，在長期情境資訊方面，我們從查詢日誌中去辨別出以往使用者所查詢的關鍵字以及點選過的網頁，接著，在短期情境資訊的部份，我們也記錄下目前使用者的查詢關鍵字以及未點選之網頁。最後，我們在長期情境中濾除掉搜尋引擎生手的查詢過程，同時探勘出與目前使用者有相似資訊需求的以往經驗使用者之查詢過程關鍵字集合，藉以推薦給目前使用者，完成自動查詢修正。 / World Wide Web search engines can help users to search information by their queries, but novice search engines users usually don’t know how to represent their information need. If search engines can offer query refinement automatically, it will help novice search engine users to satisfy their information need effectively. How to find users’ information need, and how to perform query refinement automatically, have become important research issues. In this thesis, we develop the method to help novice search engine users for satisfying their information need effectively by implicit feedback. Implicit feedback in this research is referring to short-term context and long-term context. In this research, first, long-term context is obtained by identifying each user’s queries and extracting conceptual keywords of clickthrough data in each query session from query logs. Then, we also identify current user’s queries and extract conceptual keywords of non-clickthrough data for short-term context identification. Finally, we filter novice sessions from long-term context, and mine frequent itemsets of past experienced users’ search behavior to suggest the most appropriate new query to current user according to their information need. 查詢修正隱含回饋搜尋引擎生手長期情境短期情境 Query Refinement Implicit Feedback Novice User Long-term Context Short-term Context
2	Large-Context Question Answering with Cross-Lingual Transfer Sagen, Markus January 2021 (has links) Models based around the transformer architecture have become one of the most prominent for solving a multitude of natural language processing (NLP)tasks since its introduction in 2017. However, much research related to the transformer model has focused primarily on achieving high performance and many problems remain unsolved. Two of the most prominent currently are the lack of high performing non-English pre-trained models, and the limited number of words most trained models can incorporate for their context. Solving these problems would make NLP models more suitable for real-world applications, improving information retrieval, reading comprehension, and more. All previous research has focused on incorporating long-context for English language models. This thesis investigates the cross-lingual transferability between languages when only training for long-context in English. Training long-context models in English only could make long-context in low-resource languages, such as Swedish, more accessible since it is hard to find such data in most languages and costly to train for each language. This could become an efficient method for creating long-context models in other languages without the need for such data in all languages or pre-training from scratch. We extend the models’ context using the training scheme of the Longformer architecture and fine-tune on a question-answering task in several languages. Our evaluation could not satisfactorily confirm nor deny if transferring long-term context is possible for low-resource languages. We believe that using datasets that require long-context reasoning, such as a multilingual TriviaQAdataset, could demonstrate our hypothesis’s validity. Long-Context Multilingual Model Longformer XLM-R Longformer Long-term Context Extending Context Extend Context Large-Context Long-Context Large Context Long Context Cross-Lingual Multi-Lingual Cross Lingual Multi Lingual QA Question-Answering Question Answering Transformer model Machine Learning Transfer Learning SQuAD Memory Transfer Learning Long-Context Long Context Efficient Monolingual Multilingual QA model Language Model Huggingface BERT RoBERTa XLM-R mBERT Multilingual BERT Efficient Transformers Reformer Linformer Performer Transformer-XL Wikitext-103 TriviaQA HotpotQA WikiHopQA VINNOVA Peltarion AI LM MLM Deep Learning Natural Language Processing NLP Attention Transformers Transfer Learning Datasets Computer and Information Sciences Data- och informationsvetenskap

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利用隱含回饋提供搜尋引擎的自動查詢修正 / Automatic Query Refinement in Web Search Engines using Implicit Feedback

Large-Context Question Answering with Cross-Lingual Transfer