Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Improve and Implement an Open Source Question Answering System

A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when it comes to language structure, characters etc. We have implemented the Question Answer System so that it supports localization and improved Part of Speech tagging performance by storing the lexicon in the database instead of a file based lexicon. We have implemented a brill tagger variant for Part of Speech tagging of Hindi phrases and grammar rules for triplet extraction. We also improve Yioop’s lexical data handling support by allowing the user to add named entities. Our improvements to Yioop were then evaluated by comparing the retrieved answers against a dataset of answers known to be true. The test data for the question answering system included creating 2 indexes, 1 each for English and Hindi. These were created by configuring Yioop to crawl 200,000 wikipedia pages for each crawl. The crawls were configured to be domain specific so that English index consists of pages restricted to English text and Hindi index is restricted to pages with Hindi text. We then used a set of 50 questions on the English and Hindi systems. We recored, Hindi system to have an accuracy of about 55% for simple factoid questions and English question answer system to have an accuracy of 63%

Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources

The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes indicate that the proposed method helps to solve the problem of ambiguities in meaning through the integration of natural language processing tools and semantic resources that can help to overcome several problems related to the natural language structure. This method also helps improve the capability to understand students’ needs and, consequently, to retrieve the most suitable answers

Analysis of errors in the automatic translation of questions for translingual QA systems

Purpose – This study aims to focus on the evaluation of systems for the automatic translation of questions destined to translingual question-answer (QA) systems. The efficacy of online translators when performing as tools in QA systems is analysed using a collection of documents in the Spanish language. Design/methodology/approach – Automatic translation is evaluated in terms of the functionality of actual translations produced by three online translators (Google Translator, Promt Translator, and Worldlingo) by means of objective and subjective evaluation measures, and the typology of errors produced was identified. For this purpose, a comparative study of the quality of the translation of factual questions of the CLEF collection of queries was carried out, from German and French to Spanish. Findings – It was observed that the rates of error for the three systems evaluated here are greater in the translations pertaining to the language pair German-Spanish. Promt was identified as the most reliable translator of the three (on average) for the two linguistic combinations evaluated. However, for the Spanish-German pair, a good assessment of the Google online translator was obtained as well. Most errors (46.38 percent) tended to be of a lexical nature, followed by those due to a poor translation of the interrogative particle of the query (31.16 percent). Originality/value – The evaluation methodology applied focuses above all on the finality of the translation. That is, does the resulting question serve as effective input into a translingual QA system? Thus, instead of searching for “perfection”, the functionality of the question and its capacity to lead one to an adequate response are appraised. The results obtained contribute to the development of improved translingual QA systems

Matching Queries to Frequently Asked Questions: Search Functionality for the MRSA Web-Portal

As part of the long-term EUREGIO MRSA-net project a system was developed which enables health care workers and the general public to quickly find answers to their questions regarding the MRSA pathogen. This paper focuses on how these questions can be answered using Information Retrieval (IR) and Natural Language Processing (NLP) techniques on a Frequently-Asked-Questions-style (FAQ) database

Joint Video and Text Parsing for Understanding Events and Answering Queries

We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results