A Question-Answering Approach to Key Value Pair Extraction from Form-Like Document Images

From Document Retrieval to Question Answering From Document Retrieval to Question Answering

A Question Answering Approach to Emotion Cause Extraction

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural ...

Question Answering from Large Document Collections

We present a question answering system with a hybrid design, combining techniques from knowledge representation, information retrieval, and natural language processing. This combination enables domain independence and robustness in the face of text variability, both in the question and in the raw text documents used as knowledge sources. We describe the specific design of our current prototype,...

A Question Answering Approach for Emotion Cause Extraction

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural ...

A Multi-agent Approach to Question Answering

In this paper we present a multi-agent approach to question answering for the Portuguese language. Our proposal is composed by three modules: (1) document and query processing; (2) ontology construction; and (3) answer generation. Each module is composed by multiple cooperative agents which adopt distinct strategies to generate its outputs and cooperate to create a global result. This approach ...