Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey <s>  <s> Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications. <s> BIB001 </s> Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey <s>  <s> Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions. To this end, we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks. We conduct our study within the framework of Memory Networks (Weston et al., 2015) because this perspective allows us to eventually scale up to more complex reasoning, and show that Memory Networks can be successfully trained to achieve excellent performance. <s> BIB002 </s> Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey <s>  <s> Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks. <s> BIB003
question. Freebase is one of the most popular KBs BIB001 which has been widely used as a benchmark in many recent works on knowledgebased QA. KBs include entities, relations, and facts. Facts in knowledge base are stored in (subject, predicate, object) format where the subject and the object are entities and the predicate is a relation, indicating the relation between the object and the subject. For example, answer of the question 'Which forest is Fires Creek in?' could be stored in a fact like '(fires creek, containedby, nantahala national forest)' BIB002 . In this task there are two types of questions: single-relation and multi-relation questions. A simple question is answered by one fact in KB, while the answer of a multi-relation question is found by reasoning over more than one fact in the KB BIB003 . SimpleQuestions and WebQSP are the major datasets of the single-relation and multi-relation questions, respectively. In text-based QA, answer of a candidate question is obtained by finding the most similar answer text between candidate answer texts. Consider the question Q and set of answers {A 1 , A 2 , ..., A n }, the goal of this system is finding the best answer among these answers. Recent works have proposed different deep neural models in text-based QA which compares two segments of texts and produces a similarity score. In this paper, we focus on this type of QA and review the available methods on text-based QA. This paper organizes as follows: In section 2, we review the most popular QA datasets. In section 3, we introduce evaluation metrics used in QA. In section 4, we present the architecture of QA systems. We discuss about information retrieval-based models used for question answer similarity in section 5 and deep learning models in section 6, respectively. In section 7 we report and compare results of reviewed models. We conclude this paper in section 8.