This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

Last update: Dec 22, 2022

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Overview

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

To appear on KDD'21...[pdf]

This project provides an unsupervised framework for mining and tagging quality phrases on text corpora. In this work, we recognize the power of pretrained language models in identifying the structure of a sentence. The attention matrices generated by a Transformer model are informative to distinguish quality phrases from ordinary spans, as illustrated in the following example.

With a lightweight CNN model to capture inter-word relationships from various ranges, we can effectively tackle the task of phrase tagging as a multi-channel image classifiaction problem.

For model training, we seek to alleviate the need for human annotation and external knowledge bases. Instead, we show that sufficient supervision can be directly mined from large-scale unlabeled corpus. Specifically, we mine frequent max patterns with each document as context, since by definition, high-quality phrases are sequences that are consistently used in context. Compared with labels generated by distant supervision, silver labels mined from the corpus itself preserve better diversity, coverage, and contextual completeness. The superiority is supported by comparison on two public datasets.

We compare our method with existing ones on the KP20k dataset (publication data from CS domain) and the KPTimes dataset (news articles). UCPhrase significantly outperforms prior arts without supervision. Compared with off-the-shelf phrase tagging tools, UCPhrase also shows unique advantages, especially in its ability to generalize to specific domains without reliance on manually curated labels or KBs. We provide comprehensive case studies to demonstrate the comparison among different tagging methods. We also have some interesting findings in the discussion sections.

We aim to build UCPhrase as a practical tool for phrase tagging, though it is certainly far from perfect. Please feel free to try on your own corpus and give us feedbacks if you have any ideas that can help build better phrase tagging tools!

Facts: UCPhrase is a joint work by researchers from UI at Urbana Champaign, and University of California San Diago.

Quick Start

Step 1: Download and unzip the data folder

wget https://www.dropbox.com/s/1bv7dnjawykjsji/data.zip?dl=0 -O data.zip
unzip -n data.zip

Step 2: Install and compile dependencies

bash build.sh

Step 3: Run experiments

cd src
python exp.py --gpu 0 --dir_data ../data/devdata

Model checkpoint and output files will be stored under the generated "experiments" folder.

Citation

If you find the implementation useful, please consider citing the following paper:

Xiaotao Gu*, Zihan Wang*, Zhenyu Bi, Yu Meng, Liyuan Liu, Jiawei Han, Jingbo Shang, "UCPhrase: Unsupervised Context-aware Quality Phrase Tagging", in Proc. of 2021 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'21), Aug. 2021

Comments

Missing file for '/standard/kp20k.tagging.human_2.json'

When I download the data from https://www.dropbox.com/s/1bv7dnjawykjsji/data.zip?dl=0 and unzip the package, no file of '/standard/kp20k.tagging.human_2.json'.

opened by possible1402 4
How to extract phrase by using stanfordcoreNLP tool?

Hi,

Thanks for opening your code for this paper. I read your paper and I found that you compared your results with pretrained model such as stanfordNLP. I am curious about how to extract phrases/mentions by stanfordNLP. Do you use "coref" annotator to get coreferent mentions as extracted phrase? Or there are other tools based on stanfordCoreNLP?

Wait for your reply.

Thanks in advance.

opened by yangjingyi 3
results interpretation

This might be a repeat, but I didn't find an answer. Could you please specify where can I find actual resulting phrases after running the code? I scanned the code and all the configs multiple times, but i don't see it. Unless it's the spans in Attmap.cs_roberta_base.3layers, but in that case i'm lost at trying to interpret it. Cheers!

Edit: I checked devdata-cs_roberta_base-core.CNN.3layers/model and these spans make much more sense, still could you please confirm those are the ones and how do I interpret them?

opened by icanfast 2
-

This PR inserts the list of products as silver labels in the candidate generation step. In addition, the prepare_dataset.py script has been improved to filter out reviews that are in another language and to remove html tags. Also remove the different patterns identified for each review_source. It also includes some logs to debug the training process.

This needs another PR to improve model training and clean up the repository.

opened by alejandrods 0
what the labels of CNN?

Hi! Excuse! What the labels of the CNN model ? The input of the CNN is 'attention map' and the output of the model is 'silver labels' ? However, the sliver labels are not all correct ?

opened by ZYuliang 0
Getting "Killed" error when run on kp20k Dataset

When I run python exp.py --gpu 0 --dir_data ../data/kp20k, I get the Killed error. While debugging I found the error happens at the return torch.tensor(padded_tensor, dtype=torch.float32) (line 66 of src/model_att/model.py)

Is there a way to fix this?

opened by nanthamanish 5

This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

Related tags

Overview

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

To appear on KDD'21...[pdf]

Quick Start

Step 1: Download and unzip the data folder

Step 2: Install and compile dependencies

Step 3: Run experiments

Citation

Comments

Missing file for '/standard/kp20k.tagging.human_2.json'

How to extract phrase by using stanfordcoreNLP tool?

results interpretation

-

what the labels of CNN?

Getting "Killed" error when run on kp20k Dataset

Owner

Xiaotao Gu

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