In this paper we present a graph-based approach to question answering. The method assumes a graph representation of question sentences and text sentences. Question answering rules are automatically learnt from a training corpus of questions and answer sentences with the answer annotated. The method is independent from the graph representation formalism chosen. A particular example is presented that uses a specific graph representation of the logical contents of sentences.8 page(s
Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to com...
The role of machine learning algorithms in natural language processing (NLP) tasks has become increa...
Research on Question Answering is focused mainly on classifying the question type and finding the an...
In this paper we present a graph-based approach to question answering. The method assumes a graph re...
This paper describes our approach for the Community Question Answering Task, which was presented at ...
A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to tran...
A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to tran...
Many exciting things happened since I left the ExtrAns project at the University of Zurich. In this ...
This paper presents a system which learns to answer questions on a broad range of topics from a know...
Abstract. This paper presents a system which learns to answer ques-tions on a broad range of topics ...
Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural lang...
Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent year...
Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural lang...
Simple questions are the most common type of questions used for evaluating a knowledge graph questio...
This paper proposes to improve visual question answering (VQA) with structured representations of bo...
Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to com...
The role of machine learning algorithms in natural language processing (NLP) tasks has become increa...
Research on Question Answering is focused mainly on classifying the question type and finding the an...
In this paper we present a graph-based approach to question answering. The method assumes a graph re...
This paper describes our approach for the Community Question Answering Task, which was presented at ...
A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to tran...
A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to tran...
Many exciting things happened since I left the ExtrAns project at the University of Zurich. In this ...
This paper presents a system which learns to answer questions on a broad range of topics from a know...
Abstract. This paper presents a system which learns to answer ques-tions on a broad range of topics ...
Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural lang...
Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent year...
Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural lang...
Simple questions are the most common type of questions used for evaluating a knowledge graph questio...
This paper proposes to improve visual question answering (VQA) with structured representations of bo...
Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to com...
The role of machine learning algorithms in natural language processing (NLP) tasks has become increa...
Research on Question Answering is focused mainly on classifying the question type and finding the an...