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项目作者： maximedb

项目描述 ：
NLP papers applicable to financial markets
高级语言：
项目地址: git://github.com/maximedb/nlp_papers.git
创建时间： 2018-02-04T10:14:37Z
项目社区：https://github.com/maximedb/nlp_papers
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NLP Papers

Repository of NLP papers useful for applying NLP techniques to financial markets.

NLP Financial Applications

Direct applications of NLP research to financial markets.

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Repos

News

Contribution

Contributions more than welcome :-)

a_deep_reinforced_model_for_abstractive_summarization_1649916549529.pdf
ai_decodes_trading_signals_hidden_in_jargon_1649916551506.pdf
an_analysis_of_verbs_in_financial_news_articles_and_their_impact_on_stock_price_1649916551637.pdf
analyzing_stock_market_movements_using_twitter_sentiment_analysis_1649916552691.pdf
ask_me_anything_dynamic_memory_networks_for_nlp_1649916553135.pdf
convolutional_neural_networkds_for_sentence_classification_1649916553293.pdf
deep_learning_for_financial_sentiment_analysis_on_finance_news_providers_1649916584709.pdf
deep_learning_for_stock_prediction_using_numerical_and_textual_information_1649916586066.pdf
domain_adaptation_using_stock_market_prices_to_refine_sentiment_dictionaries_1649916586151.pdf
enriching_word_vectors_with_subword_information_1649916586857.pdf
fine_tuned_language_models_for_text_classification_1649916587180.pdf
from_word_embeddings_to_document_distances_1649916589020.pdf
from_word_to_time_series_embedding_1649916589414.pdf
giving_content_to_investor_sentiment_the_role_of_media_in_the_stock_market_1649916594357.pdf
impact_of_structured_event_embeddings_on_scalable_stock_forecasting_models_1649916595608.pdf
lancaster_a_at_semeval-2017_task_5_evaluation_metrics_matter_predicting_sentiment_from_financial_news_headlines_1649916596082.pdf
leverage_financial_news_to_predict_stock_price_movements_1649916596543.pdf
more_than_words_quantifying_language_1649916596727.pdf
natural_language_processing_part_1_primer_1649916598109.pdf
neural_generative_question_answering_1649916598806.pdf
neural_text_generation_a_practical_guide_1649916599214.pdf
predicting_stock_market_movement_with_deep_rnns_1649916599859.pdf
predicting_stock_movement_thorugh_executive_tweets_1649916600220.pdf
question_answering_using_deep_learning_1649916600763.pdf
reading_wikipedia_to_answer_open_domain_questions_1649916603013.pdf
sentiment_analysis_in_financial_news_1649916603646.pdf
sentiment_predictability_for_stocks_1649916604338.pdf
textual_analysis_of_stock_market_prediction_1649916604479.pdf
trading_strategies_to_exploit_blog_and_news_sentiment_1649916605013.pdf
twitter_mood_predicts_the_stock_market_1649916605513.pdf
from_word_embeddings_to_document_distances_1647580502455.pdf
from_word_to_time_series_embedding_1647580502581.pdf
giving_content_to_investor_sentiment_the_role_of_media_in_the_stock_market_1647580502846.pdf
impact_of_structured_event_embeddings_on_scalable_stock_forecasting_models_1647580503161.pdf
lancaster_a_at_semeval-2017_task_5_evaluation_metrics_matter_predicting_sentiment_from_financial_news_headlines_1647580503239.pdf
leverage_financial_news_to_predict_stock_price_movements_1647580503275.pdf
more_than_words_quantifying_language_1647580503359.pdf
natural_language_processing_part_1_primer_1647580503578.pdf
neural_generative_question_answering_1647580503773.pdf
neural_text_generation_a_practical_guide_1647580503874.pdf
predicting_stock_market_movement_with_deep_rnns_1647580503964.pdf
predicting_stock_movement_thorugh_executive_tweets_1647580504135.pdf
question_answering_using_deep_learning_1647580504228.pdf
reading_wikipedia_to_answer_open_domain_questions_1647580504513.pdf
sentiment_analysis_in_financial_news_1647580504686.pdf
sentiment_predictability_for_stocks_1647580504753.pdf
textual_analysis_of_stock_market_prediction_1647580504860.pdf
trading_strategies_to_exploit_blog_and_news_sentiment_1647580504987.pdf
twitter_mood_predicts_the_stock_market_1647580505126.pdf
a_deep_reinforced_model_for_abstractive_summarization_1647580498672.pdf
ai_decodes_trading_signals_hidden_in_jargon_1647580498794.pdf
an_analysis_of_verbs_in_financial_news_articles_and_their_impact_on_stock_price_1647580498927.pdf
analyzing_stock_market_movements_using_twitter_sentiment_analysis_1647580498983.pdf
ask_me_anything_dynamic_memory_networks_for_nlp_1647580499041.pdf
convolutional_neural_networkds_for_sentence_classification_1647580499083.pdf
deep_learning_for_financial_sentiment_analysis_on_finance_news_providers_1647580500164.pdf
deep_learning_for_stock_prediction_using_numerical_and_textual_information_1647580501975.pdf
domain_adaptation_using_stock_market_prices_to_refine_sentiment_dictionaries_1647580502082.pdf
enriching_word_vectors_with_subword_information_1647580502149.pdf
fine_tuned_language_models_for_text_classification_1647580502341.pdf