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Leveraging Large Language Models for Fuzzy String Matching in Political Science
Published 27 Mar 2024 in cs.AI | (2403.18218v1)
Abstract: Fuzzy string matching remains a key issue when political scientists combine data from different sources. Existing matching methods invariably rely on string distances, such as Levenshtein distance and cosine similarity. As such, they are inherently incapable of matching strings that refer to the same entity with different names such as ''JP Morgan'' and ''Chase Bank'', ''DPRK'' and ''North Korea'', ''Chuck Fleischmann (R)'' and ''Charles Fleischmann (R)''. In this letter, we propose to use LLMs to entirely sidestep this problem in an easy and intuitive manner. Extensive experiments show that our proposed methods can improve the state of the art by as much as 39% in terms of average precision while being substantially easier and more intuitive to use by political scientists. Moreover, our results are robust against various temperatures. We further note that enhanced prompting can lead to additional performance improvements.
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American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Longpre, S., Wang, Y., DuBois, C.: How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? Findings of the Association for Computational Linguistics: EMNLP 2020 (2020) Yu et al. [2024] Yu, J., Wang, X., Tu, S., Cao, S., Zhang-Li, D., Lv, X., Peng, H., Yao, Z., Zhang, X., Li, H., Li, C., Zhang, Z., Bai, Y., Liu, Y., Xin, A., Lin, N., Yun, K., Gong, L., Chen, J., Wu, Z., Qi, Y., Li, W., Guan, Y., Zeng, K., Qi, J., Jin, H., Liu, J., Gu, Y., Yao, Y., Ding, N., Hou, L., Liu, Z., Xu, B., Tang, J., Li, J.: Kola: Carefully benchmarking world knowledge of large language models. ICLR (2024) Meo et al. [2023] Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Yu, J., Wang, X., Tu, S., Cao, S., Zhang-Li, D., Lv, X., Peng, H., Yao, Z., Zhang, X., Li, H., Li, C., Zhang, Z., Bai, Y., Liu, Y., Xin, A., Lin, N., Yun, K., Gong, L., Chen, J., Wu, Z., Qi, Y., Li, W., Guan, Y., Zeng, K., Qi, J., Jin, H., Liu, J., Gu, Y., Yao, Y., Ding, N., Hou, L., Liu, Z., Xu, B., Tang, J., Li, J.: Kola: Carefully benchmarking world knowledge of large language models. ICLR (2024) Meo et al. [2023] Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Longpre, S., Wang, Y., DuBois, C.: How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? Findings of the Association for Computational Linguistics: EMNLP 2020 (2020) Yu et al. [2024] Yu, J., Wang, X., Tu, S., Cao, S., Zhang-Li, D., Lv, X., Peng, H., Yao, Z., Zhang, X., Li, H., Li, C., Zhang, Z., Bai, Y., Liu, Y., Xin, A., Lin, N., Yun, K., Gong, L., Chen, J., Wu, Z., Qi, Y., Li, W., Guan, Y., Zeng, K., Qi, J., Jin, H., Liu, J., Gu, Y., Yao, Y., Ding, N., Hou, L., Liu, Z., Xu, B., Tang, J., Li, J.: Kola: Carefully benchmarking world knowledge of large language models. ICLR (2024) Meo et al. [2023] Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Yu, J., Wang, X., Tu, S., Cao, S., Zhang-Li, D., Lv, X., Peng, H., Yao, Z., Zhang, X., Li, H., Li, C., Zhang, Z., Bai, Y., Liu, Y., Xin, A., Lin, N., Yun, K., Gong, L., Chen, J., Wu, Z., Qi, Y., Li, W., Guan, Y., Zeng, K., Qi, J., Jin, H., Liu, J., Gu, Y., Yao, Y., Ding, N., Hou, L., Liu, Z., Xu, B., Tang, J., Li, J.: Kola: Carefully benchmarking world knowledge of large language models. ICLR (2024) Meo et al. [2023] Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Meo, S.A., Al-Masri, A.A., Alotaibi, M., Meo, M.Z.S., Meo, M.O.S.: Chatgpt knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. Healthcare (Basel). (2023) Sennrich et al. [2016] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016) Gilardi et al. [2023] Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. 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Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
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[2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Gilardi, F., Alizadeh, M., Kubli, M.: Chatgpt outperforms crowd-workers for text-annotation tasks. Proceedings of the National Academy of Sciences (2023) Sarkar et al. [2022] Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Sarkar, S., Feng, D., Santu1, S.K.K.: Zero-shot multi-label topic inference with sentence encoders & llms. EMNLP (2022) Osnabrügge et al. [2021] Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Osnabrügge, M., Ash, E., Morelli, M.: Cross-Domain Topic Classification for Political Texts. Political Analysis (2021) Bonica [2014] Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Bonica, A.: Mapping the ideological marketplace. American Journal of Political Science 58(2), 367–386 (2014) Box-Steffensmeier et al. [2013] Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Box-Steffensmeier, J.M., Christenson, D.P., Hitt, M.P.: Quality over quantity: Amici influence and judicial decision making. American Political Science Review 107(3), 446–460 (2013) Muchlinski et al. [2016] Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Muchlinski, D., Siroky, D., He, J., Kocher, M.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis 24(1), 87–103 (2016) Wang [2019] Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019) Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
- Wang, Y.: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 21(1), 107–110 (2019)
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