课程名称︰资讯检索与撷取 课程性质︰资工系选修 课程教师︰陈信希 开课学院：电机资讯学院 开课系所︰资讯工程学系 考试日期（年月日）︰2022/01/06 考试时限（分钟）：180 试题 : 1. The following lists 5 tasks and 10 evaluation metrics. Tasks: Web Search, Question Answering, Named Entity Recognition, Relation Ex- traction, Entity Retrieval Evaluation Metrics: Accuracy, Precision, Recall, F1, F0.5, F2, MAP, NDCG, MRR, Kendall Tau Coefficient (a) Please discuss and explain which evaluation metrics are suitable for each task. If there are no suitable metrics for a task, please give your sug- gestions. (25 points) (b) If there are no suitable tasks for an evaluation metric, please discuss in what situation the evaluation metric will be adopted. (10 points) 2. Long documents may contain mixture of topics. Query matches may be spread over the whole document. Please describe how a neural document ranking model aggregates the relevant metches from different parts of a long document. (10 points) 3. Knowledge Base Acceleration (KBA) task is defined as follows. This task aims to filter a time-ordered corpus for documents that are highly relevant to a predefined list of entities. Total 27 people and 2 organiza- tions are selected. A stream corpus spanning 4,973 consecutive hours is con- structed. It contains over 400M documents. Each document has a timestamp that places it in the stream. The 29 target entities were mentioned infrequently enough in the corpus. Judgments for documents from before stream corpus con- struction time were provided as training data for filtering documents from the remaining hours. You are instructed to apply your system to each hourly directory of corpus data in chronological order. For each hour, before pro- cessing the next hour, systems are expected to emit a list of assertions con- necting documents and entities. The goal is to identify only central-rated documents. (a) Please show your idea to deal with the KBA task. (10 points) (b) Please discuss how this task is related to Knowledge Base Completion (KBC), which involves in discovering missing facts. (5 points) 4. Knowledge base is useful for document retrieval. Please explain the latent factor modeling approach and the deep learning approach to introduce know- ledge base to enhance the performance of document retrieval. (14 points) 5. Entity relationship explanation is a textual description to describe how a given pair of entities is related. Please show how to deal with this task by using knowledge graph. (6 points) 6. Traditional IE predict their relation from a predefined set such as "Birth- Place" and "Spouse." By contrast, open information extraction (Open IE) aims to extract the triples that consist of a pair of argument phrases and their relation phrase from textual data. For example, one can extract the following two triples from the sentence "Albert Einstein was born in Ulm and died in Princeton." (Albert Einstein, was born in, Ulm) (Albert Einstein, died in, Princeton) Please answer the following questions about open IE. (a) Compared with traditional IE, give an advantage and a disadvantage of open IE. (6 points) (b) Give two downstream applications of open IE. (6 points) (c) Given a collection of news articles, please provide a feasible method to construct an open IE system without the need of labeled data. (8 points) 7. The following lists the presentation topics presented by the team members. Team 1: Learning an End-to-End Structure for Retrieval in Large-Scale Recommendations Team 2: 1. EmbedKGQA: Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings 2. TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph 3. Improving Multi-hop Knowledge Base Question Answering by Learning Interme- diate Supervision Signals Team 3: Inductive Topic Variational Graph Auto-Encoder for Text Classification Team 4: Dense Passage Retrieval for Open-Domain Question Answering Team 5: "Did you buy it already?", Detecting Users Purchase-State From Their Product- Related Questions Team 6: UnitedQA: A Hybrid Approach for Open Domain Question Answering Team 7: 1. A Reinforcement Learning Framework for Relevance Feedback 2. Generating Images Instead of Retrieving Them: Relevance Feedback on Gene- rative Adversarial Networks Team 8: 1. AutoDebias: Learning to Debias for Recommendation 2. Casual Intervention for Leveraging Popularity Bias in Recommendation Team 9: Self-Supervised Reinforcement Learning for Recommender Systems Team 10: 1. Multi-behavior Recommendation with Graph Convolutional Networks. 2. Graph Heterogeneous Multi-Relational Recommendation. Team 11: Self-supervised Graph Learning for Recommentation. Team 12: Personalized Search-based Query Rewrite System for Conversational AI Team 13: Group based Personalized Search by Integrating Search Behaviour and Friend Network Team 14: Answering Any-hop Open-domain Questions with Iterative Document Reranking Team 15: 1. Time Matters: Sequential Recommendation with Complex Temporal Information 2. Motif-aware Sequential Recommendation Team 16: 1. Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste 2. Fairness among New Items in Cold Start Recommender Systems 3. A Heterogeneous Graph Neural Model for Cold-start Recommendation Team 17: 1. Estimation-Action-Reflection: Towards Deep Interaction Between Conversa- tional and Recommender Systems 2. Time Interval Aware Self-Attention for Sequential Recommendation Please write down your team id first, and then select the most exciting topic you learned from the other team. Please write down this team id and specify the idea you learned from their presentation in brief. (10 points) -- 第01话 似乎在课堂上听过的样子 第02话 那真是太令人绝望了 第03话 已经没什麽好期望了 第04话 被当、21都是存在的 第05话 怎麽可能会all pass 第06话 这考卷绝对有问题啊 第07话 你能面对真正的分数吗 第08话 我，真是个笨蛋 第09话 这样成绩，教授绝不会让我过的 第10话 再也不依靠考古题 第11话 最後留下的补考 第12话 我最爱的学分 --

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