https://github.com/zihangdai/CFO

Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
https://github.com/zihangdai/CFO

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Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

• Host: GitHub
• URL: https://github.com/zihangdai/CFO
• Owner: zihangdai
• Created: 2016-10-24T00:53:43.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-08-09T12:56:33.000Z (about 6 years ago)
• Last Synced: 2024-06-29T06:30:41.973Z (3 months ago)
• Language: Lua
• Size: 459 KB
• Stars: 83
• Watchers: 9
• Forks: 34
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# CFO

Code repo for [Conditional Focused Neural Question Answering with Large-scale Knowledge Bases](https://www.aclweb.org/anthology/P/P16/P16-1076.pdf)

# Installation and Preprocessing

1. Refer to Virtuoso.md to install and confiture the software

2. Make sure [torch7](http://torch.ch/) is installed together with the following dependencies

   - logroll: `luarocks install logroll`

   - nngraph: `luarocks install nngraph`

3. After the installation and configuration of **Virtuoso**, run `bash data_preprocess.sh` to finish preprocessing

# Training

1. Focused Lableing

   ```

   cd FocusedLabeling

   th train_crf.lua

   ```

2. Entity Type Vector

   ```

   cd EntityTypeVec

   th train_ent_typevec.lua

   ```

3. RNN based Relation Network

   ```

   cd RelationRNN

   th train_rel_rnn.lua

   ```

# Inference

In the following, define `SPLIT='valid' or 'test'`.

1. Run focused labeling on validation/test data

   ```

   cd FocusedLabeling

   

   python generate_inference_data.py --split ${SPLIT}

   

   th process_inference.lua -testSplit ${SPLIT}

   th infer_crf.lua \

       -testData inference-data/label.${SPLIT}.t7 \

       -modelFile "path-to-pretrained-model"

   ```

   - `python generate_inference_data.py --split ${SPLIT}` will create the file `label.${SPLIT}.txt` in the folder `FocusedLabeling/inference-data`;

   - `th process_inference.lua` will turn the text file `label.${SPLIT}.txt` into `label.${SPLIT}.t7` in torch format (both in the folder `FocusedLabeling/inference-data`);

   - `th infer_crf.lua ...`  will generate the file `label.result.${SPLIT}` in the folder `FocusedLabeling`.

2. Query candidates based on focused labeling

   ```

   cd Inference

   mkdir ${SPLIT} && cd ${SPLIT}

   python ../query_candidates.py 6 \

          ../../PreprocessData/QAData.${SPLIT}.pkl \

          ../../FocusedLabeling/label.result.${SPLIT} \

          ../../KnowledgeBase/type.top-500.pkl

   ```

   This step will generate the file `QAData.label.${SPLIT}.cpickle` in the folder `Inference/${SPLIT}`.

3. Generate score data based on the query results

   ```

   cd Inference/${SPLIT}

   python ../generate_score_data.py QAData.label.${SPLIT}.cpickle

   ```

   This step will generate the following files in the same folder `Inference/${SPLIT}`:

   - `rel.single.${SPLIT}.txt` (candidate relations for those with only a single candidate subject)

   - `rel.multi.${SPLIT}.txt`   (candidate relations for those with only multiple candidate subject)

   - `type.multi.${SPLIT}.txt` (candidate entities for those with multiple candidate subjects)

   - `single.${SPLIT}.cpickle`

   - `multi.${SPLIT}.cpickle`

4. Run relation inference

   ```

   cd RelationRNN

   mkdir inference-data

   th process_inference.lua -testSplit ${SPLIT}

   th infer_rel_rnn.lua -testData inference-data/rel.single.${SPLIT}.t7

   th infer_rel_rnn.lua -testData inference-data/rel.multi.${SPLIT}.t7

   ```

   This step will generate the files `score.rel.single.${SPLIT}` and `score.rel.multi.${SPLIT}` in the folder `RelationRNN`.

5. Run entity inference

   ```

   cd EntityTypeVec

   mkdir inference-data

   th process_inference.lua -testSplit ${SPLIT}

   th infer_ent_typevec.lua -testData inference-data/ent.${SPLIT}.t7

   ```

   This step will generate the file `score.ent.multi.multi.${SPLIT}` in the folder `EntityTypeVec`.

6. Run joint disambiguation

   ```

   cd Inference/${SPLIT}

   python ../joint_disambiguation.py multi.${SPLIT}.cpickle \

          ../../RelationRNN/score.rel.multi.${SPLIT} \

          ../../EntityTypeVec/score.ent.multi.multi.${SPLIT}

   ```