This is a modified version of facebook DrQA module which supports Chinese Language. This module is currently able to answer factiod questions in the financial field.
DrQA Introduction
DrQA is a system for reading comprehension applied to open-domain
question answering. In particular, DrQA is targeted at the task of
"machine reading at scale" (MRS). In this setting, we are searching for
an answer to a question in a potentially very large corpus of
unstructured documents (that may not be redundant). Thus the system has
to combine the challenges of document retrieval (finding the relevant
documents) with that of machine comprehension of text (identifying the
answers from those documents).
Our experiments with DrQA focus on answering factoid questions while
using Wikipedia as the unique knowledge source for documents. Wikipedia
is a well-suited source of large-scale, rich, detailed information. In
order to answer any question, one must first retrieve the few
potentially relevant articles among more than 5 million, and then scan
them carefully to identify the answer.
Note that DrQA treats Wikipedia as a generic collection of articles
and does not rely on its internal graph structure. As a result, DrQA can
be straightforwardly applied to any collection of documents, as
described in the retriever README.
Installation
install python (3.5 or higer)
install pyltp following the instructions here, download model data and edit the code in MbaQA/tokenizers/__init__.py, line 11: ltp_datapath
MbaQA
├── data
├── db
│ └── mba_2000.db
├── reader
│ └── single.mdl
└── retriever
└── model
└── mba_2000-tfidf-ngram=2-hash=16777216-tokenizer=ltp-numdocs=2002.npz
Notes
This module takes the MBA Wiki articles(78459) as the unique knowledge source. The articles originally came from Datayes Corporation. According to company regulations, we built a demonstration system based on a subset of 2000 articles.