Giters

l294265421 / ASOTE

Aspect-Sentiment-Opinion Triplet Extraction (ASOTE)

Home Page:https://arxiv.org/pdf/2103.15255.pdf

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

aspect-based-sentiment-analysisaspect-sentiment-triplet-extractionaspect-sentiment-opinion-tripletasoteastetoweaspect-opinion-pair-extraction

The code and data for the paper "A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task"

ASOTE

Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) extracts aspect term, sentiment and opinion term triplets from sentences. In the triplet extracted by ASOTE the sentiment is the sentiment of the aspect term and opinion term pair. For example, given the sentence, "The atmosphere is attractive , but a little uncomfortable.", ASOTE extracts two triplets, ("atmosphere", positive, "attractive") and ("atmosphere", negative, "uncomfortable").

Differences between ASOTE and ASTE

Aspect Sentiment Triplet Extraction (ASTE) (Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis) also extracts triplets from sentences. Each triplet extracted by ASTE contains an aspect term, the sentiment that the sentence expresses toward the aspect term, and one opinion term associated with the aspect. For example, asote_vs_aste_details In the third sentence, the negative sentiment toward the aspect term “food” is expressed without an annotatable opinion.

Data

Dataset Construction

We build four datasets for the ASOTE task: 14res, 14lap, 15res, 16res.

Specifically, we first construct four ASTE datasets by merging four ATSA datasets from three SemEval tasks (SemEval-2014 Task 4, SemEval-2015 Task 12, SemEval-2016 Task 5) and four TOWE datasets like Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis, then manually annotate the sentiment of the aspect and opinion pair in the ASTE datasets.

For example, given the sentence, "The atmosphere is attractive, but a little uncomfortable.", its annotations in SemEval-2014 Task 4 are as follows:

<sentence id="32464601#418474#1">
    <text>The atmosphere is attractive, but a little uncomfortable.</text>
    <aspectTerms>
        <aspectTerm term="atmosphere" polarity="conflict"  from="4" to="14"/>
    </aspectTerms>
</sentence>

its annotations in TOWE datasets are as follows:

32464601#418474#1	The atmosphere is attractive , but a little uncomfortable .	The\O atmosphere\B is\O attractive\O ,\O but\O a\O little\O uncomfortable\O .\O	The\O atmosphere\O is\O attractive\B ,\O but\O a\O little\O uncomfortable\B .\O

We align the above annotations of the sentence and get an ASTE sample:

{
    "sentence": "The atmosphere is attractive , but a little uncomfortable .", 
    "words": [
        "The", 
        "atmosphere", 
        "is", 
        "attractive", 
        ",", 
        "but", 
        "a", 
        "little", 
        "uncomfortable", 
        "."
    ], 
    "polarity": "conflict", 
    "opinions": [
        {
            "aspect_term": {
                "start": 1, 
                "end": 2, 
                "term": "atmosphere"
            }, 
            "opinion_term": {
                "start": 3, 
                "end": 4, 
                "term": "attractive"
            }
        }, 
        {
            "aspect_term": {
                "start": 1, 
                "end": 2, 
                "term": "atmosphere"
            }, 
            "opinion_term": {
                "start": 8, 
                "end": 9, 
                "term": "uncomfortable"
            }
        }
    ], 
    "aspect_term": {
        "start": 1, 
        "end": 2, 
        "term": "atmosphere"
    }
}

We then annotate the sentiments of the aspect term and opinion term pairs in the ASTE sample and get an ASOTE sample:

{
    "sentence": "The atmosphere is attractive , but a little uncomfortable .", 
    "words": [
        "The", 
        "atmosphere", 
        "is", 
        "attractive", 
        ",", 
        "but", 
        "a", 
        "little", 
        "uncomfortable", 
        "."
    ], 
    "polarity": "conflict", 
    "opinions": [
        {
            "aspect_term": {
                "start": 1, 
                "end": 2, 
                "term": "atmosphere"
            }, 
            "opinion_term": {
                "start": 3, 
                "end": 4, 
                "term": "attractive"
            }, 
            "polarity": "positive"
        }, 
        {
            "aspect_term": {
                "start": 1, 
                "end": 2, 
                "term": "atmosphere"
            }, 
            "opinion_term": {
                "start": 8, 
                "end": 9, 
                "term": "uncomfortable"
            }, 
            "polarity": "negative"
        }
    ], 
    "aspect_term": {
        "start": 1, 
        "end": 2, 
        "term": "atmosphere"
    }
}

Note that, in our ASOTE datasets, triplets belonging to different aspect terms in the same sentence are in different json lines.

Data version

v1

14res, 14lap, 15res, 16res

The v1 datasets only includes sentences which contain at least one aspect term.

v2

14res, 14lap, 15res, 16res

The v2 datasets includes both sentences which contain aspect terms and sentences which do not contain aspect terms. That is, the v2 datasets include all sentences in the corresponding ATSA datasets from the three SemEval tasks (SemEval-2014 Task 4, SemEval-2015 Task 12, SemEval-2016 Task 5). For example, although the sentence, "It gets very crowded so I would suggest that you get their early .", do not contain aspect terms, it also is included in the v2 datasets. We think datasets including these sentences can better evaluate the performance of methods, since methods can encounter this kind of sentences in real-world scenarios.

Is it necessary to annotate the sentiments of the aspect term and opinion term pairs in the ASTE datasets for obtaining our ASOTE datasets?

Wang et al. (2016, 2017) have annotated the opinions and thier sentiments of the sentences in the restaurant and laptop datasets from SemEval-2014 Task 4 and the restaurant dataset from SemEval-2015 Task 12. Is it necessary to annotate the sentiments of the aspect term and opinion term pairs in the ASTE datasets for obtaining our ASOTE datasets? The answer is yes. The reasons are as follows:

  • The sentiments of aspect term and opinion term pairs are different from the sentiments of opinions.
  • Wang et al. (2016, 2017) only annotated the opinions with positive sentiments or negative sentiments. That is, the opinions with neutral sentiments were not annotated.
  • The opinions annotated by Wang et al. (2016, 2017) are different from the opinions annotated in the TOWE datasets which are used to construct our ASOTE datasets. For example, given this sentence, "those rolls were big , but not good and sashimi wasn't fresh.", the opinions and their sentiments annotated by Wang et al. (2016, 2017) are big +1, good +1, and fresh +1 (+1 indicates positive sentiment), while the opinions annotated in the TOWE datasets are "big", "not good" and "wasn't fresh". We think the opinions annotated in the TOWE datasets are more appropriate for aspect-sentiment-opinion triplets.

The datasets provided by Wang et al. (2016, 2017) can be found here:

Baselines

Some models have been proposed to extract opinion triplets from sentences. However, these models were only evaluated on the ASTE task. We have evaluated the performances of a few opinion triplet extraction models on the ASOTE task:

We will evaluate the performances of other models on the ASOTE task:

  • more models...

Our Models

Requirements

  • Python 3.6.8
  • torch==1.2.0
  • pytorch-transformers==1.1.0
  • allennlp==0.9.0

Instructions:

Before excuting the following commands, replace glove.840B.300d.txt(http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip), bert-base-uncased.tar.gz(https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz) and vocab.txt(https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt) with the corresponding absolute paths in your computer.

PBF

ATE

sh repeat_non_bert.sh 7 4-ASOTEDataRest14-0,4-ASOTEDataRest14-1,4-ASOTEDataRest14-2,4-ASOTEDataRest14-3,4-ASOTEDataRest14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/ate_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest14 --data_type common_bert --model_name NerBert --train True --evaluate True --predict False --predict_test True --crf False --fixed_bert False > ate.4-ASOTEDataRest14-0.log 2>&1 &

sh repeat_non_bert.sh 2 4-ASOTEDataLapt14-0,4-ASOTEDataLapt14-1,4-ASOTEDataLapt14-2,4-ASOTEDataLapt14-3,4-ASOTEDataLapt14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/ate_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataLapt14 --data_type common_bert --model_name NerBert --train True --evaluate True --predict False --predict_test True --crf False --fixed_bert False > ate.4-ASOTEDataLapt14-0.log 2>&1 &

sh repeat_non_bert.sh 3 4-ASOTEDataRest15-0,4-ASOTEDataRest15-1,4-ASOTEDataRest15-2,4-ASOTEDataRest15-3,4-ASOTEDataRest15-4 nlp_tasks/absa/mining_opinions/sequence_labeling/ate_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest15 --data_type common_bert --model_name NerBert --train True --evaluate True --predict False --predict_test True --crf False --fixed_bert False > ate.4-ASOTEDataRest15-0.log 2>&1 &

sh repeat_non_bert.sh 3 4-ASOTEDataRest16-0,4-ASOTEDataRest16-1,4-ASOTEDataRest16-2,4-ASOTEDataRest16-3,4-ASOTEDataRest16-4 nlp_tasks/absa/mining_opinions/sequence_labeling/ate_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest16 --data_type common_bert --model_name NerBert --train True --evaluate True --predict False --predict_test True --crf False --fixed_bert False > ate.4-ASOTEDataRest16-0.log 2>&1 &

TOWE

sh repeat_non_bert.sh 0 101-ASOTEDataRest14-0,101-ASOTEDataRest14-1,101-ASOTEDataRest14-2,101-ASOTEDataRest14-3,101-ASOTEDataRest14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-ASOTEDataRest14-0.log 2>&1 &

sh repeat_non_bert.sh 1 101-ASOTEDataLapt14-0,101-ASOTEDataLapt14-1,101-ASOTEDataLapt14-2,101-ASOTEDataLapt14-3,101-ASOTEDataLapt14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataLapt14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-ASOTEDataLapt14-0.log 2>&1 &

sh repeat_non_bert.sh 2 101-ASOTEDataRest15-0,101-ASOTEDataRest15-1,101-ASOTEDataRest15-2,101-ASOTEDataRest15-3,101-ASOTEDataRest15-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest15 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-ASOTEDataRest15-0.log 2>&1 &

sh repeat_non_bert.sh 3 101-ASOTEDataRest16-0,101-ASOTEDataRest16-1,101-ASOTEDataRest16-2,101-ASOTEDataRest16-3,101-ASOTEDataRest16-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest16 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-ASOTEDataRest16-0.log 2>&1 &

TOWE inference

sh repeat_non_bert.sh 0 101-ASOTEDataRest14-0,101-ASOTEDataRest14-1,101-ASOTEDataRest14-2,101-ASOTEDataRest14-3,101-ASOTEDataRest14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train False --evaluate False --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True --add_predicted_aspect_term True --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest14/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest14-%d/1571400646/models/result_of_predicting_test.txt

sh repeat_non_bert.sh 1 101-ASOTEDataLapt14-0,101-ASOTEDataLapt14-1,101-ASOTEDataLapt14-2,101-ASOTEDataLapt14-3,101-ASOTEDataLapt14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataLapt14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train False --evaluate False --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True --add_predicted_aspect_term True --ate_result_filepath_template model_data/absa/ate/ASOTEDataLapt14/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataLapt14-%d/1571400646/models/result_of_predicting_test.txt

sh repeat_non_bert.sh 6 101-ASOTEDataRest15-0,101-ASOTEDataRest15-1,101-ASOTEDataRest15-2,101-ASOTEDataRest15-3,101-ASOTEDataRest15-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest15 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train False --evaluate False --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True --add_predicted_aspect_term True --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest15/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest15-%d/1571400646/models/result_of_predicting_test.txt

sh repeat_non_bert.sh 7 101-ASOTEDataRest16-0,101-ASOTEDataRest16-1,101-ASOTEDataRest16-2,101-ASOTEDataRest16-3,101-ASOTEDataRest16-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest16 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train False --evaluate False --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True --add_predicted_aspect_term True --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest16/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest16-%d/1571400646/models/result_of_predicting_test.txt

AOPSC

sh repeat_non_bert.sh 3 21-ASOTEDataRest14-0,21-ASOTEDataRest14-1,21-ASOTEDataRest14-2,21-ASOTEDataRest14-3,21-ASOTEDataRest14-4 nlp_tasks/absa/mining_opinions/tosc/tosc_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest14 --data_type bert_second_sentence_21 --model_name bert --train True --evaluate True --batch_size 32 --sample_mode multi --aspect_term_aware True --term True --cls False --pair False --syntax False --same_special_token False --task_name osc --consider_target False --second_sentence True --position_and_second_sentence True > osc_second_sentence.21-ASOTEDataRest14-0.log 2>&1 &

sh repeat_non_bert.sh 5 21-ASOTEDataLapt14-0,21-ASOTEDataLapt14-1,21-ASOTEDataLapt14-2,21-ASOTEDataLapt14-3,21-ASOTEDataLapt14-4 nlp_tasks/absa/mining_opinions/tosc/tosc_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataLapt14 --data_type bert_second_sentence_21 --model_name bert --train True --evaluate True --batch_size 32 --sample_mode multi --aspect_term_aware True --term True --cls False --pair False --syntax False --same_special_token False --task_name osc --consider_target False --second_sentence True --position_and_second_sentence True > osc_second_sentence.21-ASOTEDataLapt14-0.log 2>&1 &

sh repeat_non_bert.sh 6 21-ASOTEDataRest15-0,21-ASOTEDataRest15-1,21-ASOTEDataRest15-2,21-ASOTEDataRest15-3,21-ASOTEDataRest15-4 nlp_tasks/absa/mining_opinions/tosc/tosc_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest15 --data_type bert_second_sentence_21 --model_name bert --train True --evaluate True --batch_size 32 --sample_mode multi --aspect_term_aware True --term True --cls False --pair False --syntax False --same_special_token False --task_name osc --consider_target False --second_sentence True --position_and_second_sentence True > osc_second_sentence.21-ASOTEDataRest15-0.log 2>&1 &

sh repeat_non_bert.sh 7 21-ASOTEDataRest16-0,21-ASOTEDataRest16-1,21-ASOTEDataRest16-2,21-ASOTEDataRest16-3,21-ASOTEDataRest16-4 nlp_tasks/absa/mining_opinions/tosc/tosc_bootstrap.py --embedding_filepath glove.840B.300d.txt --bert_file_path bert-base-uncased.tar.gz --bert_vocab_file_path uncased_L-12_H-768_A-12/vocab.txt --current_dataset ASOTEDataRest16 --data_type bert_second_sentence_21 --model_name bert --train True --evaluate True --batch_size 32 --sample_mode multi --aspect_term_aware True --term True --cls False --pair False --syntax False --same_special_token False --task_name osc --consider_target False --second_sentence True --position_and_second_sentence True > osc_second_sentence.21-ASOTEDataRest16-0.log 2>&1 &

evaluate

sh run.sh nlp_tasks/absa/mining_opinions/sequence_labeling/asote_pipeline_evaluation_bootstrap.py --current_dataset ASOTEDataRest14 --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest14/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest14-%d/1571400646/models/result_of_predicting_test.txt --towe_result_filepath_template model_data/absa/towe/ASOTEDataRest14/common_bert_with_second_sentence_101/model_name_TermBertWithSecondSentence-include_conflict_False.101-ASOTEDataRest14-%d/1571400646/models/result_of_predicting_test.txt.add_predicted_aspect_term --tosc_result_filepath_template model_data/absa/osc/ASOTEDataRest14/bert_second_sentence_21/model_name_bert.21-ASOTEDataRest14-%d/1571400646/models/result_of_predicting_test.txt

sh run.sh nlp_tasks/absa/mining_opinions/sequence_labeling/asote_pipeline_evaluation_bootstrap.py --current_dataset ASOTEDataLapt14 --ate_result_filepath_template model_data/absa/ate/ASOTEDataLapt14/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataLapt14-%d/1571400646/models/result_of_predicting_test.txt --towe_result_filepath_template model_data/absa/towe/ASOTEDataLapt14/common_bert_with_second_sentence_101/model_name_TermBertWithSecondSentence-include_conflict_False.101-ASOTEDataLapt14-%d/1571400646/models/result_of_predicting_test.txt.add_predicted_aspect_term --tosc_result_filepath_template model_data/absa/osc/ASOTEDataLapt14/bert_second_sentence_21/model_name_bert.21-ASOTEDataLapt14-%d/1571400646/models/result_of_predicting_test.txt

sh run.sh nlp_tasks/absa/mining_opinions/sequence_labeling/asote_pipeline_evaluation_bootstrap.py --current_dataset ASOTEDataRest15 --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest15/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest15-%d/1571400646/models/result_of_predicting_test.txt --towe_result_filepath_template model_data/absa/towe/ASOTEDataRest15/common_bert_with_second_sentence_101/model_name_TermBertWithSecondSentence-include_conflict_False.101-ASOTEDataRest15-%d/1571400646/models/result_of_predicting_test.txt.add_predicted_aspect_term --tosc_result_filepath_template model_data/absa/osc/ASOTEDataRest15/bert_second_sentence_21/model_name_bert.21-ASOTEDataRest15-%d/1571400646/models/result_of_predicting_test.txt

sh run.sh nlp_tasks/absa/mining_opinions/sequence_labeling/asote_pipeline_evaluation_bootstrap.py --current_dataset ASOTEDataRest16 --ate_result_filepath_template model_data/absa/ate/ASOTEDataRest16/common_bert/model_name_NerBert-include_conflict_False.4-ASOTEDataRest16-%d/1571400646/models/result_of_predicting_test.txt --towe_result_filepath_template model_data/absa/towe/ASOTEDataRest16/common_bert_with_second_sentence_101/model_name_TermBertWithSecondSentence-include_conflict_False.101-ASOTEDataRest16-%d/1571400646/models/result_of_predicting_test.txt.add_predicted_aspect_term --tosc_result_filepath_template model_data/absa/osc/ASOTEDataRest16/bert_second_sentence_21/model_name_bert.21-ASOTEDataRest16-%d/1571400646/models/result_of_predicting_test.txt

PBF on TOWE-data

sh repeat_non_bert.sh 1 101-rest14-0,101-rest14-1,101-rest14-2,101-rest14-3,101-rest14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath /data/ceph/11006/data-lyc/data/glove.840B.300d.txt --bert_file_path /data/ceph/11006/data-lyc/data/bert-base-uncased.tar.gz --bert_vocab_file_path /data/ceph/11006/data-lyc/data/uncased_L-12_H-768_A-12/vocab.txt --current_dataset rest14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-rest14-0.log 2>&1 &

sh repeat_non_bert.sh 2 101-lapt14-0,101-lapt14-1,101-lapt14-2,101-lapt14-3,101-lapt14-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath /data/ceph/11006/data-lyc/data/glove.840B.300d.txt --bert_file_path /data/ceph/11006/data-lyc/data/bert-base-uncased.tar.gz --bert_vocab_file_path /data/ceph/11006/data-lyc/data/uncased_L-12_H-768_A-12/vocab.txt --current_dataset lapt14 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-lapt14-0.log 2>&1 &

sh repeat_non_bert.sh 3 101-rest15-0,101-rest15-1,101-rest15-2,101-rest15-3,101-rest15-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath /data/ceph/11006/data-lyc/data/glove.840B.300d.txt --bert_file_path /data/ceph/11006/data-lyc/data/bert-base-uncased.tar.gz --bert_vocab_file_path /data/ceph/11006/data-lyc/data/uncased_L-12_H-768_A-12/vocab.txt --current_dataset rest15 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-rest15-0.log 2>&1 &

sh repeat_non_bert.sh 5 101-rest16-0,101-rest16-1,101-rest16-2,101-rest16-3,101-rest16-4 nlp_tasks/absa/mining_opinions/sequence_labeling/towe_bootstrap.py --embedding_filepath /data/ceph/11006/data-lyc/data/glove.840B.300d.txt --bert_file_path /data/ceph/11006/data-lyc/data/bert-base-uncased.tar.gz --bert_vocab_file_path /data/ceph/11006/data-lyc/data/uncased_L-12_H-768_A-12/vocab.txt --current_dataset rest16 --data_type common_bert_with_second_sentence_101 --model_name TermBertWithSecondSentence --train True --evaluate True --predict False --crf False --same_special_token False --fixed_bert False --position_and_second_sentence True > towe.101-rest16-0.log 2>&1 &

Experiment

Results on v2

The Results on v2 are reported here.

Citation

@misc{li2021finegrained,
      title={A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task}, 
      author={Yuncong Li and Fang Wang and Wenjun Zhang and Sheng-hua Zhong and Cunxiang Yin and Yancheng He},
      year={2021},
      eprint={2103.15255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

About

Aspect-Sentiment-Opinion Triplet Extraction (ASOTE)

https://arxiv.org/pdf/2103.15255.pdf

aspect-based-sentiment-analysisaspect-sentiment-triplet-extractionaspect-sentiment-opinion-tripletasoteastetoweaspect-opinion-pair-extraction

Languages

Language:Python 94.9%Language:Shell 5.1%