Abstract Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches semantic (SQA) systems, although different prototype designed. SQA use a formal query language like SPARQL knowledge specific vocabulary. This paper analyses frameworks, architectures, or perform cl...

Visual Question Answering (VQA) has attracted much attention recently in both natural language processing and computer vision communities, as it offers insight into the relationships between two relevant sources of information. Tremendous advances are seen field VQA due to success deep learning. Based upon improvements, Affective Network (AVQAN) enriches understanding analysis models by making ...

1. Intellexer NL parser 1.1) Tokenizer 1.2) Statistical tagger 1.3) Rule-based tagging corrector (RBTC) 1.4) Chunker 1.5) Lexicalized parser 1.6) Paraphraser 1.7) Generation of terms and pairs 2. Indexing and answering 2.1) Indexing 2.2) Resolving anaphora in questions 2.3) Matching Factoid questions 2.4) Matching List questions 2.5) Matching Other questions 2.6) Difference between runs A, B and C

Recently, we can acquire immence amount of information thanks to the spread of a computer and internet. Therefore, technology for finding the information that a user desires becomes more and more important. A question answering (QA) system answers a question written by natural language in contrast to conventional information retrieval systems where a user expresses his information need by keywo...

We present a new AI task – Embodied Question Answering (EmbodiedQA) – where an agent is spawned at a random location in a 3D environment and asked a question (‘What color is the car?’). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question (‘orange’). This challenging ...

A Question Answering (QA) system should provide a short and precise answer to a question in natural language, by searching a large knowledge base consisting of natural language text. The sources of the knowledge base are widely available, for written natural language text is a preferential form of human communication. The information ranges from the more traditional edited texts, for example en...

This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, both in natural language. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to access an embedded knowledge-base through an attention-like mechani...

This paper describes the architecture, operation and results obtained with the Question Answering prototype for Spanish developed in the Department of Language Processing and Information Systems at the University of Alicante for CLEF-2003 Spanish monolingual QA evaluation task. Our system has been fully developed from scratch and it combines shallow natural language processing tools with statis...

A common problem in Question Answering – and Information Retrieval in general – is information overload, i.e. an excessive amount of data from which to search for relevant information. This results in the risk of high recall but low precision of the information returned to the user. In turn, this affects the relevance of answers with respect to the users’ needs, as queries can be ambiguous and ...