[ { "P@20": 0.4667, "nDCG@20": 0.5381, "code_links": [ { "title": "Georgetown-IR-Lab/cedr", "url": "https://github.com/Georgetown-IR-Lab/cedr" }, { "title": "Georgetown-IR-Lab/contextualized-reps-for-ranking", "url": "https://github.com/Georgetown-IR-Lab/contextualized-reps-for-ranking" }, { "title": "Crysitna/CEDR_tpu", "url": "https://github.com/Crysitna/CEDR_tpu" } ], "date": "2019-04-15", "date2": 20190415, "model": "CEDR-KNRM", "paper": { "title": "CEDR: Contextualized Embeddings for Document Ranking", "url": "https://cknow.io/lib/b191fd0f3f255742" }, "paper_data_uoa": "b191fd0f3f255742" }, { "nDCG@20": 0.469, "code_links": [ { "title": "AdeDZY/SIGIR19-BERT-IR", "url": "https://github.com/AdeDZY/SIGIR19-BERT-IR" }, { "title": "NavePnow/Google-BERT-on-fake_or_real-news-dataset", "url": "https://github.com/NavePnow/Google-BERT-on-fake_or_real-news-dataset" } ], "date": "2019-05-22", "date2": 20190522, "model": "BERT-MaxP", "paper": { "title": "Deeper Text Understanding for IR with Contextual Neural Language Modeling", "url": "https://cknow.io/lib/b4bd1a3460f21307" }, "paper_data_uoa": "b4bd1a3460f21307" }, { "nDCG@20": 0.467, "code_links": [ { "title": "AdeDZY/SIGIR19-BERT-IR", "url": "https://github.com/AdeDZY/SIGIR19-BERT-IR" }, { "title": "NavePnow/Google-BERT-on-fake_or_real-news-dataset", "url": "https://github.com/NavePnow/Google-BERT-on-fake_or_real-news-dataset" } ], "date": "2019-05-22", "date2": 20190522, "model": "BERT-SumP", "paper": { "title": "Deeper Text Understanding for IR with Contextual Neural Language Modeling", "url": "https://cknow.io/lib/b4bd1a3460f21307" }, "paper_data_uoa": "b4bd1a3460f21307" }, { "MAP": 0.271, "P@20": 0.389, "nDCG@20": 0.464, "code_links": [ { "title": "nlpaueb/deep-relevance-ranking", "url": "https://github.com/nlpaueb/deep-relevance-ranking" } ], "date": "2018-09-05", "date2": 20180905, "model": "POSIT-DRMM-MV", "paper": { "title": "Deep Relevance Ranking Using Enhanced Document-Query Interactions", "url": "https://cknow.io/lib/9ec94571acaf6a69" }, "paper_data_uoa": "9ec94571acaf6a69" }, { "P@20": 0.4042, "nDCG@20": 0.4541, "code_links": [ { "title": "Georgetown-IR-Lab/cedr", "url": "https://github.com/Georgetown-IR-Lab/cedr" }, { "title": "Georgetown-IR-Lab/contextualized-reps-for-ranking", "url": "https://github.com/Georgetown-IR-Lab/contextualized-reps-for-ranking" }, { "title": "Crysitna/CEDR_tpu", "url": "https://github.com/Crysitna/CEDR_tpu" } ], "date": "2019-04-15", "date2": 20190415, "model": "Vanilla BERT", "paper": { "title": "CEDR: Contextualized Embeddings for Document Ranking", "url": "https://cknow.io/lib/b191fd0f3f255742" }, "paper_data_uoa": "b191fd0f3f255742" }, { "MAP": 0.2904, "P@20": 0.4064, "nDCG@20": 0.4502, "code_links": [ { "title": "ucasir/NPRF", "url": "https://github.com/ucasir/NPRF" } ], "date": "2018-10-30", "date2": 20181030, "model": "NPRF-DRMM", "paper": { "title": "NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval", "url": "https://cknow.io/lib/b8be2a0d2408fd4f" }, "paper_data_uoa": "b8be2a0d2408fd4f" }, { "MAP": 0.258, "P@20": 0.374, "nDCG@20": 0.445, "code_links": [ { "title": "nlpaueb/deep-relevance-ranking", "url": "https://github.com/nlpaueb/deep-relevance-ranking" } ], "date": "2018-09-05", "date2": 20180905, "model": "PACRR", "paper": { "title": "Deep Relevance Ranking Using Enhanced Document-Query Interactions", "url": "https://cknow.io/lib/9ec94571acaf6a69" }, "paper_data_uoa": "9ec94571acaf6a69" }, { "nDCG@20": 0.444, "code_links": [ { "title": "AdeDZY/SIGIR19-BERT-IR", "url": "https://github.com/AdeDZY/SIGIR19-BERT-IR" }, { "title": "NavePnow/Google-BERT-on-fake_or_real-news-dataset", "url": "https://github.com/NavePnow/Google-BERT-on-fake_or_real-news-dataset" } ], "date": "2019-05-22", "date2": 20190522, "model": "BERT-FirstP", "paper": { "title": "Deeper Text Understanding for IR with Contextual Neural Language Modeling", "url": "https://cknow.io/lib/b4bd1a3460f21307" }, "paper_data_uoa": "b4bd1a3460f21307" }, { "MAP": 0.2971, "P@20": 0.3948, "nDCG@20": 0.4391, "code_links": [ { "title": "hamed-zamani/snrm", "url": "https://github.com/hamed-zamani/snrm" } ], "date": "2018-10-22", "date2": 20181022, "model": "SNRM-PRF", "paper": { "title": "From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing", "url": "https://cknow.io/lib/8c29ada10b04044b" }, "paper_data_uoa": "8c29ada10b04044b" }, { "MAP": 0.2846, "P@20": 0.3926, "nDCG@20": 0.4327, "code_links": [ { "title": "ucasir/NPRF", "url": "https://github.com/ucasir/NPRF" } ], "date": "2018-10-30", "date2": 20181030, "model": "NPRF-KNRM", "paper": { "title": "NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval", "url": "https://cknow.io/lib/b8be2a0d2408fd4f" }, "paper_data_uoa": "b8be2a0d2408fd4f" }, { "MAP": 0.2856, "P@20": 0.3766, "nDCG@20": 0.431, "code_links": [ { "title": "hamed-zamani/snrm", "url": "https://github.com/hamed-zamani/snrm" } ], "date": "2018-10-22", "date2": 20181022, "model": "SNRM", "paper": { "title": "From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing", "url": "https://cknow.io/lib/8c29ada10b04044b" }, "paper_data_uoa": "8c29ada10b04044b" }, { "MAP": 0.279, "P@20": 0.382, "nDCG@20": 0.431, "code_links": [ { "title": "faneshion/DRMM", "url": "https://github.com/faneshion/DRMM" }, { "title": "sebastian-hofstaetter/neural-ranking-drmm", "url": "https://github.com/sebastian-hofstaetter/neural-ranking-drmm" } ], "date": "2017-11-23", "date2": 20171123, "model": "DRMM", "paper": { "title": "A Deep Relevance Matching Model for Ad-hoc Retrieval", "url": "https://cknow.io/lib/232d051c86d49ca9" }, "paper_data_uoa": "232d051c86d49ca9" }, { "MAP": 0.2464, "P@20": 0.351, "nDCG@20": 0.3989, "code_links": [ { "title": "ucasir/NPRF", "url": "https://github.com/ucasir/NPRF" } ], "date": "2018-10-30", "date2": 20181030, "model": "KNRM", "paper": { "title": "NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval", "url": "https://cknow.io/lib/b8be2a0d2408fd4f" }, "paper_data_uoa": "b8be2a0d2408fd4f" }, { "MAP": 0.3278, "P@20": 0.4287, "code_links": [ { "title": "castorini/birch", "url": "https://github.com/castorini/birch" } ], "date": "2019-03-26", "date2": 20190326, "model": "BERT FT(Microblog)", "paper": { "title": "Simple Applications of BERT for Ad Hoc Document Retrieval", "url": "https://cknow.io/lib/20394322a0397085" }, "paper_data_uoa": "20394322a0397085" }, { "MAP": 0.302, "P@20": 0.4012, "code_links": [ { "title": "castorini/Anserini", "url": "https://github.com/castorini/Anserini" } ], "date": "2018-12-01", "date2": 20181201, "model": "Anserini BM25+RM3", "paper": { "title": "The Neural Hype and Comparisons Against Weak Baselines", "url": "https://cknow.io/lib/3c29a274c2abe0ad" }, "paper_data_uoa": "3c29a274c2abe0ad" }, { "MAP": 0.2837, "code_links": [ { "title": "mikvrax/TrecingLab", "url": "https://github.com/mikvrax/TrecingLab" } ], "date": "2017-04-28", "date2": 20170428, "model": "FNRM-RankProb_Embed", "paper": { "title": "Neural Ranking Models with Weak Supervision", "url": "https://cknow.io/lib/4c6d9fa64489b099" }, "paper_data_uoa": "4c6d9fa64489b099" }, { "MAP": 0.2811, "code_links": [ { "title": "mikvrax/TrecingLab", "url": "https://github.com/mikvrax/TrecingLab" } ], "date": "2017-04-28", "date2": 20170428, "model": "FNRM-Rank_Embed", "paper": { "title": "Neural Ranking Models with Weak Supervision", "url": "https://cknow.io/lib/4c6d9fa64489b099" }, "paper_data_uoa": "4c6d9fa64489b099" }, { "MAP": 0.2499, "code_links": [ { "title": "hamed-zamani/snrm", "url": "https://github.com/hamed-zamani/snrm" } ], "date": "2018-10-22", "date2": 20181022, "model": "QL", "paper": { "title": "From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing", "url": "https://cknow.io/lib/8c29ada10b04044b" }, "paper_data_uoa": "8c29ada10b04044b" } ]