This repository contains the code for reproducing the results reported in the paper "Explainable Zero-Shot Topic Extraction with Common-Sense Knowledge Graph" (pdf) at the LDK 2021 Conference.
A user-friendly demo is available at: http://zeste.tools.eurecom.fr/
Based on ConceptNet's common sense knowledge graph and embeddings, ZeSTE generates explainable predictions for a document topical category (e.g. politics, sports, video_games ..) without reliance on training data. The following is a high-level illustration of the approach:
ZeSTE can also be accessed via a RESTful API for easy deployment and use. For further information, please refer to the documentation: https://zeste.tools.eurecom.fr/doc
Before running any code in this repo, please install the following dependencies which can be found in requirements.txt.
This repo is organized as follows:
generate_cache.py: this script processes the raw ConceptNet dump to produce cached files for each node in ConceptNet to accelerate the label neighborhood generation. It also transforms ConceptNet Numberbatch text file into a Gensim word embedding that we pickle for quick loading.zeste.py: this is the main script for evaluation. It takes as argument the dataset to process as well as model configuration parameters such as neighborhood depth (see below). The results (classification report, confusion matrix, and classification metrics) are persisted into text files.util.py: contains the functions that are used inzeste.pylabels_mapping: contains the tab-separated mappings for the studied datasets.
⚠️ If you don't want to use a different version of ConceptNet or Numberbatch (e.g. for a newer version or a different language), you can skip steps 1 and 2 and download the outputs of this script directly from Zenodo: https://zenodo.org/record/5920669#.Yfb78pHMJhE (unzipping may take a while)
The two following files need to be downloaded to bypass the use of ConceptNet's web API: the dump of ConceptNet triples, and the ConceptNet Numberbatch pre-computed word embeddings (english). You can also download them from ConceptNet's and Numberbatch's repos, respectively.
wget https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz
wget https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-en-19.08.txt.gz
gzip -d conceptnet-assertions-5.7.0.csv.gz
gzip -d numberbatch-en-19.08.txt.gz
rm *.gz
This script takes as input the two just-downloaded files and the cache path to where precomputed 1-hop label neighborhoods will be saved. This can take up to 7.2G of storage space.
usage: generate_cache.py [-h] [-cnp CONCEPTNET_ASSERTIONS_PATH] [-nbp CONCEPTNET_NUMBERBATCH_PATH] [-zcp ZESTE_CACHE_PATH]
Zero-Shot Topic Extraction
optional arguments:
-h, --help show this help message and exit
-cnp CONCEPTNET_ASSERTIONS_PATH, --conceptnet_assertions_path CONCEPTNET_ASSERTIONS_PATH
Path to CSV file containing ConceptNet assertions dump
-nbp CONCEPTNET_NUMBERBATCH_PATH, --conceptnet_numberbatch_path CONCEPTNET_NUMBERBATCH_PATH
Path to W2V file for ConceptNet Numberbatch
-zcp ZESTE_CACHE_PATH, --zeste_cache_path ZESTE_CACHE_PATH
Path to the repository where the generated files will be saved
This script uses the precomputed 1-hop label neighborhoods to recursively generate label neighborhoods of any given depth (-d). It takes also as parameters the path to the dataset CSV file (which should have two columns: text and label). The rest of the arguments are for model experimentation.
⚠️ If using a personalized dataset, make sure to create a properlabels_mappingfile (multiword labels are comma-separated).
⚠️ Make sure to create the paths for caching and prefetching before running the script. The script will save the neighborhoods construction (using the parameters given) to be easily prefetched for future use/itartions. If you want to use a different configuration, specify a different path (using the-ppparameter) or empty the contents of the defaultprefetch_path
usage: zeste.py [-h] [-cp CACHE_PATH] [-pp PREFETCH_PATH]
[-nb NUMBERBATCH_PATH] [-dp DATASET_PATH] [-lm LABELS_MAPPING]
[-rp RESULTS_PATH] [-d DEPTH] [-f FILTER]
[-s {simple,compound,depth,harmonized}] [-n USE_NGRAMS]
[-ar ALLOWED_RELS]
Zero-Shot Topic Extraction
optional arguments:
-h, --help show this help message and exit
-cp CACHE_PATH, --cache_path CACHE_PATH
Path to where the 1-hop word neighborhoods are cached
-pp PREFETCH_PATH, --prefetch_path PREFETCH_PATH
Path to where the precomputed n-hop neighborhoods are
cached
-nb NUMBERBATCH_PATH, --numberbatch_path NUMBERBATCH_PATH
Path to the pickled Numberbatch
-dp DATASET_PATH, --dataset_path DATASET_PATH
Path to the dataset to process
-lm LABELS_MAPPING, --labels_mapping LABELS_MAPPING
Path to the mapping between the dataset labels and
ZeSTE labels (multiword labels are comma-separated)
-rp RESULTS_PATH, --results_path RESULTS_PATH
Path to the directory where to store the results
-d DEPTH, --depth DEPTH
How many hops to generate the neighborhoods
-f FILTER, --filter FILTER
Filtering method: `top[N]`, `top[P]%`, `thresh[T]`,
`all`
-s {simple,compound,depth,harmonized}, --similarity {simple,compound,depth,harmonized}
-n USE_NGRAMS, --use_ngrams USE_NGRAMS
Whether or not to use n-grams (vs only simple wprds)
-ar ALLOWED_RELS, --allowed_rels ALLOWED_RELS
Which relationships to use (comma-separated or `all`)
@InProceedings{harrando_et_al_zeste_2021,
author ={Harrando, Ismail and Troncy, Rapha\"{e}l},
title ={{Explainable Zero-Shot Topic Extraction Using a Common-Sense Knowledge Graph}},
booktitle ={3rd Conference on Language, Data and Knowledge (LDK 2021)},
pages ={17:1--17:15},
year ={2021},
volume ={93},
publisher ={Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
URL ={https://drops.dagstuhl.de/opus/volltexte/2021/14553},
URN ={urn:nbn:de:0030-drops-145532},
doi ={10.4230/OASIcs.LDK.2021.17},
}