Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

-Recently, search engine got challenge deal with such a natural language questions. Sometimes, these questions are complex questions. A complex question is a question that consists several clauses, several intentions or need long answer. In this work we proposed that finding structured features and unstructured features of questions and using structured data and unstructured data could improve the search result of complex questions. According to those, we will use two approaches, IR approach and structured retrieval, QA template. Our framework consists of three parts. Question analysis, Resource Discovery and Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and tried to find structured and unstructured features of the questions. Structured feature refers to Structured data and unstructured feature refers to unstructured data. In the resource discovery we integrated structured data (relational database) and unstructured data (webpage) to take the advantaged of two kinds of data to improve and reach the relevant answer. We will find the best top fragments from context of the webpage In the Relevant Answer part, we made a score matching between the result from structured data and unstructured data, then finally used QA template to reformulate the question. In the experiment result, it shows that using structured feature and unstructured feature and using both structured and unstructured data, using approach IR and QA template could improve the search result of complex questions

A Factoid Question Answering System for Vietnamese

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.Comment: In the proceedings of the HQA'18 workshop, The Web Conference Companion, Lyon, Franc

Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

A comparative approach to Question Answering Systems

In this paper I will analyze three different algorithms and approaches to implement Question Answering Systems (QA-Systems). I will analyze the efficiency, strengths, and weaknesses of multiple algorithms by explaining them in detail and comparing them with each other. The overarching aim of this thesis is to explore ideas that can be used to create a truly open context QA-System. Open context QA-Systems remain an open problem. The various algorithms and approaches presented in this work will be focused on complex questions. Complex questions are usually verbose and the context of the question is equally important to answer the query as is the question itself. Such questions represent an interesting problem in the field because they can be answered and written in a number of distinct ways. Also, the answer structure is not always the same and the QA-System needs to compensate for this. The analysis of complex questions differ between contexts. Contexts are the topic to which a complex question belongs, e.g. Biology, literature, etc… The analysis of the answer also differs according to the corpus used. Corpus is a set of documents, belonging to a specific context, where we can find the answer to a specified question. I will start by explaining various algorithms and approaches. I will then analyze its different parts. Finally, I will present some ideas on how to implement QA-Systems

A Diagram Is Worth A Dozen Images

Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs