{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/information-retrieval/papers/11","list_of":"/task/information-retrieval","task":"Information Retrieval","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":11,"pages_in_order":48,"rows_per_page":100,"rows":[1001,1100],"of":4740,"counts":{"archive_papers_tagged":4740,"with_a_code_link":1188,"where_syntology_ran_a_sample":191,"not_listed_spam_title":0,"listed":4740,"listed_where_code_ran":191,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":151,"every_run_a_failure_of_syntologys_instrument":40,"listed_with_a_run_with_no_instrument_failure":151,"listed_every_run_a_failure_of_syntologys_instrument":40,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/information-retrieval","prev":"/task/information-retrieval/papers/10","next":"/task/information-retrieval/papers/12","papers":[{"url":"/paper/sato-contextual-semantic-type-detection-in","slug":"sato-contextual-semantic-type-detection-in","title":"Sato: Contextual Semantic Type Detection in Tables","date":"2019-11-14","arxiv_id":"1911.06311","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/sato-contextual-semantic-type-detection-in#ran","syntology_url":"https://syntology.ai/paper/1911.06311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06311"}},"official":{"repos":["megagonlabs/sato"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/191013339","slug":"191013339","title":"Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning","date":"2019-10-29","arxiv_id":"1910.13339","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/191013339#ran","syntology_url":"https://syntology.ai/paper/1910.13339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13339"}},"official":{"repos":["sayaendo/document-set-expansion-pu"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-recent-advances-in-named-entity-2","slug":"a-survey-on-recent-advances-in-named-entity-2","title":"A Survey on Recent Advances in Named Entity Recognition from Deep Learning models","date":"2019-10-25","arxiv_id":"1910.11470","repositories_listed":1,"syntology":null},{"url":"/paper/answering-complex-open-domain-questions","slug":"answering-complex-open-domain-questions","title":"Answering Complex Open-domain Questions Through Iterative Query Generation","date":"2019-10-15","arxiv_id":"1910.07000","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/answering-complex-open-domain-questions#ran","syntology_url":"https://syntology.ai/paper/1910.07000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.07000"}},"official":{"repos":["qipeng/golden-retriever"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/distillation-approx-early-stopping-harvesting","slug":"distillation-approx-early-stopping-harvesting","title":"Distillation $\\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network","date":"2019-10-02","arxiv_id":"1910.01255","repositories_listed":1,"syntology":null},{"url":"/paper/opennre-an-open-and-extensible-toolkit-for","slug":"opennre-an-open-and-extensible-toolkit-for","title":"OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction","date":"2019-09-28","arxiv_id":"1909.13078","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/opennre-an-open-and-extensible-toolkit-for#ran","syntology_url":"https://syntology.ai/paper/1909.13078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13078"}},"official":{"repos":["thunlp/OpenNRE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/learning-dynamic-author-representations-with","slug":"learning-dynamic-author-representations-with","title":"Learning Dynamic Author Representations with Temporal Language Models","date":"2019-09-11","arxiv_id":"1909.04985","repositories_listed":1,"syntology":null},{"url":"/paper/the-cl-scisumm-shared-task-2018-results-and","slug":"the-cl-scisumm-shared-task-2018-results-and","title":"The CL-SciSumm Shared Task 2018: Results and Key Insights","date":"2019-09-02","arxiv_id":"1909.00764","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-machine-comprehension-with","slug":"interactive-machine-comprehension-with","title":"Interactive Machine Comprehension with Information Seeking Agents","date":"2019-08-27","arxiv_id":"1908.10449","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interactive-machine-comprehension-with#ran","syntology_url":"https://syntology.ai/paper/1908.10449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10449"}},"official":{"repos":["xingdi-eric-yuan/imrc_public"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bulner-bug-localization-with-word-embeddings","slug":"bulner-bug-localization-with-word-embeddings","title":"BULNER: BUg Localization with word embeddings and NEtwork Regularization","date":"2019-08-26","arxiv_id":"1908.09876","repositories_listed":1,"syntology":null},{"url":"/paper/revisit-semantic-representation-and-tree","slug":"revisit-semantic-representation-and-tree","title":"Revisiting Semantic Representation and Tree Search for Similar Question Retrieval","date":"2019-08-22","arxiv_id":"1908.08326","repositories_listed":1,"syntology":null},{"url":"/paper/amazonqa-a-review-based-question-answering","slug":"amazonqa-a-review-based-question-answering","title":"AmazonQA: A Review-Based Question Answering Task","date":"2019-08-12","arxiv_id":"1908.04364","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-merge-of-k-nn-graph","slug":"on-the-merge-of-k-nn-graph","title":"On the Merge of k-NN Graph","date":"2019-08-02","arxiv_id":"1908.00814","repositories_listed":1,"syntology":null},{"url":"/paper/kilograms-very-large-n-grams-for-malware","slug":"kilograms-very-large-n-grams-for-malware","title":"KiloGrams: Very Large N-Grams for Malware Classification","date":"2019-08-01","arxiv_id":"1908.00200","repositories_listed":1,"syntology":null},{"url":"/paper/overview-of-the-mediqa-2019-shared-task-on","slug":"overview-of-the-mediqa-2019-shared-task-on","title":"Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-translate-edit-model-for-natural-language","slug":"a-translate-edit-model-for-natural-language","title":"Text-to-SQL Generation for Question Answering on Electronic Medical Records","date":"2019-07-28","arxiv_id":"1908.01839","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-translate-edit-model-for-natural-language#ran","syntology_url":"https://syntology.ai/paper/1908.01839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01839"}},"official":{"repos":["wangpinggl/TREQS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/overview-and-results-cl-scisumm-shared-task","slug":"overview-and-results-cl-scisumm-shared-task","title":"Overview and Results: CL-SciSumm Shared Task 2019","date":"2019-07-23","arxiv_id":"1907.09854","repositories_listed":1,"syntology":null},{"url":"/paper/towards-an-lstm-based-predictive-framework","slug":"towards-an-lstm-based-predictive-framework","title":"Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction","date":"2019-07-22","arxiv_id":"1907.09395","repositories_listed":1,"syntology":null},{"url":"/paper/greedy-optimized-multileaving-for","slug":"greedy-optimized-multileaving-for","title":"Greedy Optimized Multileaving for Personalization","date":"2019-07-19","arxiv_id":"1907.08346","repositories_listed":1,"syntology":null},{"url":"/paper/learning-complex-basis-functions-for","slug":"learning-complex-basis-functions-for","title":"Learning Complex Basis Functions for Invariant Representations of Audio","date":"2019-07-13","arxiv_id":"1907.05982","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-complex-basis-functions-for#ran","syntology_url":"https://syntology.ai/paper/1907.05982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05982"}},"official":{"repos":["SonyCSLParis/cae-invar"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/reqa-an-evaluation-for-end-to-end-answer","slug":"reqa-an-evaluation-for-end-to-end-answer","title":"ReQA: An Evaluation for End-to-End Answer Retrieval Models","date":"2019-07-10","arxiv_id":"1907.04780","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/reqa-an-evaluation-for-end-to-end-answer#ran","syntology_url":"https://syntology.ai/paper/1907.04780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04780"}},"official":{"repos":["google/retrieval-qa-eval"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/extraction-and-analysis-of-fictional","slug":"extraction-and-analysis-of-fictional","title":"Extraction and Analysis of Fictional Character Networks: A Survey","date":"2019-07-05","arxiv_id":"1907.02704","repositories_listed":1,"syntology":null},{"url":"/paper/a-convolutional-approach-to-melody-line","slug":"a-convolutional-approach-to-melody-line","title":"A Convolutional Approach to Melody Line Identification in Symbolic Scores","date":"2019-06-24","arxiv_id":"1906.10547","repositories_listed":1,"syntology":null},{"url":"/paper/cleaning-noisy-and-heterogeneous-metadata-for","slug":"cleaning-noisy-and-heterogeneous-metadata-for","title":"Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets","date":"2019-06-20","arxiv_id":"1906.08470","repositories_listed":1,"syntology":null},{"url":"/paper/neural-arabic-question-answering","slug":"neural-arabic-question-answering","title":"Neural Arabic Question Answering","date":"2019-06-12","arxiv_id":"1906.05394","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-arabic-question-answering#ran","syntology_url":"https://syntology.ai/paper/1906.05394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.05394"}},"official":{"repos":["husseinmozannar/SOQAL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-based-automatic-downbeat","slug":"deep-learning-based-automatic-downbeat","title":"Deep Learning-Based Automatic Downbeat Tracking: A Brief Review","date":"2019-06-10","arxiv_id":"1906.03870","repositories_listed":1,"syntology":null},{"url":"/paper/question-answering-as-global-reasoning-over","slug":"question-answering-as-global-reasoning-over","title":"Question Answering as Global Reasoning over Semantic Abstractions","date":"2019-06-09","arxiv_id":"1906.03672","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-questions-do-not-necessitate","slug":"compositional-questions-do-not-necessitate","title":"Compositional Questions Do Not Necessitate Multi-hop Reasoning","date":"2019-06-07","arxiv_id":"1906.02900","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/compositional-questions-do-not-necessitate#ran","syntology_url":"https://syntology.ai/paper/1906.02900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02900"}},"official":{"repos":["shmsw25/single-hop-rc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-neural-named-entity-recognition-and-multi","slug":"a-neural-named-entity-recognition-and-multi","title":"A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining","date":"2019-06-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lt-expertfinder-an-evaluation-framework-for","slug":"lt-expertfinder-an-evaluation-framework-for","title":"LT Expertfinder: An Evaluation Framework for Expert Finding Methods","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-nonsymmetric-determinantal-point","slug":"learning-nonsymmetric-determinantal-point","title":"Learning Nonsymmetric Determinantal Point Processes","date":"2019-05-30","arxiv_id":"1905.12962","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-consensus-of-rankings-based-on-q","slug":"quantifying-consensus-of-rankings-based-on-q","title":"Quantifying consensus of rankings based on q-support patterns","date":"2019-05-30","arxiv_id":"1905.12966","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-significance-testing-in","slug":"statistical-significance-testing-in","title":"Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors","date":"2019-05-27","arxiv_id":"1905.11096","repositories_listed":1,"syntology":null},{"url":"/paper/matchzoo-a-learning-practicing-and-developing","slug":"matchzoo-a-learning-practicing-and-developing","title":"MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching","date":"2019-05-24","arxiv_id":"1905.10289","repositories_listed":1,"syntology":null},{"url":"/paper/antique-a-non-factoid-question-answering","slug":"antique-a-non-factoid-question-answering","title":"ANTIQUE: A Non-Factoid Question Answering Benchmark","date":"2019-05-22","arxiv_id":"1905.08957","repositories_listed":1,"syntology":null},{"url":"/paper/bert-with-history-answer-embedding-for","slug":"bert-with-history-answer-embedding-for","title":"BERT with History Answer Embedding for Conversational Question Answering","date":"2019-05-14","arxiv_id":"1905.05412","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-and-alleviating-the-language","slug":"quantifying-and-alleviating-the-language","title":"Quantifying and Alleviating the Language Prior Problem in Visual Question Answering","date":"2019-05-13","arxiv_id":"1905.04877","repositories_listed":1,"syntology":null},{"url":"/paper/faq-retrieval-using-query-question-similarity","slug":"faq-retrieval-using-query-question-similarity","title":"FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance","date":"2019-05-08","arxiv_id":"1905.02851","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-audio-signal-processing","slug":"deep-learning-for-audio-signal-processing","title":"Deep Learning for Audio Signal Processing","date":"2019-04-30","arxiv_id":"1905.00078","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-use-of-arxiv-as-a-dataset","slug":"on-the-use-of-arxiv-as-a-dataset","title":"On the Use of ArXiv as a Dataset","date":"2019-04-30","arxiv_id":"1905.00075","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-the-use-of-arxiv-as-a-dataset#ran","syntology_url":"https://syntology.ai/paper/1905.00075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.00075"}},"official":{"repos":["mattbierbaum/arxiv-public-datasets"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-effect-of-low-frequency-terms-on","slug":"on-the-effect-of-low-frequency-terms-on","title":"On the Effect of Low-Frequency Terms on Neural-IR Models","date":"2019-04-29","arxiv_id":"1904.12683","repositories_listed":1,"syntology":null},{"url":"/paper/cmir-net-a-deep-learning-based-model-for","slug":"cmir-net-a-deep-learning-based-model-for","title":"CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing","date":"2019-04-09","arxiv_id":"1904.04794","repositories_listed":1,"syntology":null},{"url":"/paper/punch-positive-unlabelled-classification","slug":"punch-positive-unlabelled-classification","title":"PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images","date":"2019-04-09","arxiv_id":"1904.04547","repositories_listed":1,"syntology":null},{"url":"/paper/recognizing-musical-entities-in-user","slug":"recognizing-musical-entities-in-user","title":"Recognizing Musical Entities in User-generated Content","date":"2019-04-01","arxiv_id":"1904.00648","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-weight-for-text-classification","slug":"learning-to-weight-for-text-classification","title":"Learning to Weight for Text Classification","date":"2019-03-28","arxiv_id":"1903.12090","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-neural-architecture-for","slug":"a-unified-neural-architecture-for","title":"A Unified Neural Architecture for Instrumental Audio Tasks","date":"2019-03-01","arxiv_id":"1903.00142","repositories_listed":1,"syntology":null},{"url":"/paper/joint-optimization-of-cascade-ranking-models","slug":"joint-optimization-of-cascade-ranking-models","title":"Joint Optimization of Cascade Ranking Models","date":"2019-02-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/elki-a-large-open-source-library-for-data","slug":"elki-a-large-open-source-library-for-data","title":"ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 \"Heidelberg\"","date":"2019-02-10","arxiv_id":"1902.03616","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/elki-a-large-open-source-library-for-data#ran","syntology_url":"https://syntology.ai/paper/1902.03616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.03616"}},"official":{"repos":["elki-project/elki"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/word-embeddings-for-entity-annotated-texts","slug":"word-embeddings-for-entity-annotated-texts","title":"Word Embeddings for Entity-annotated Texts","date":"2019-02-06","arxiv_id":"1902.02078","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-open-domain-question-answering","slug":"end-to-end-open-domain-question-answering","title":"End-to-End Open-Domain Question Answering with BERTserini","date":"2019-02-05","arxiv_id":"1902.01718","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-open-domain-question-answering#ran","syntology_url":"https://syntology.ai/paper/1902.01718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01718"}},"official":{"repos":["rsvp-ai/bertserini"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/create-cohort-retrieval-enhanced-by-analysis","slug":"create-cohort-retrieval-enhanced-by-analysis","title":"CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model","date":"2019-01-22","arxiv_id":"1901.07601","repositories_listed":1,"syntology":null},{"url":"/paper/the-music-streaming-sessions-dataset","slug":"the-music-streaming-sessions-dataset","title":"The Music Streaming Sessions Dataset","date":"2018-12-31","arxiv_id":"1901.09851","repositories_listed":1,"syntology":null},{"url":"/paper/merge-double-thompson-sampling-for-large","slug":"merge-double-thompson-sampling-for-large","title":"MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation","date":"2018-12-11","arxiv_id":"1812.04412","repositories_listed":1,"syntology":null},{"url":"/paper/asynchronous-training-of-word-embeddings-for","slug":"asynchronous-training-of-word-embeddings-for","title":"Asynchronous Training of Word Embeddings for Large Text Corpora","date":"2018-12-07","arxiv_id":"1812.03825","repositories_listed":1,"syntology":null},{"url":"/paper/improving-retrieval-based-question-answering","slug":"improving-retrieval-based-question-answering","title":"Improving Retrieval-Based Question Answering with Deep Inference Models","date":"2018-12-07","arxiv_id":"1812.02971","repositories_listed":1,"syntology":null},{"url":"/paper/binary-document-image-super-resolution-for","slug":"binary-document-image-super-resolution-for","title":"Binary Document Image Super Resolution for Improved Readability and OCR Performance","date":"2018-12-06","arxiv_id":"1812.02475","repositories_listed":1,"syntology":null},{"url":"/paper/alignment-analysis-of-sequential-segmentation","slug":"alignment-analysis-of-sequential-segmentation","title":"Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate Detection","date":"2018-11-20","arxiv_id":"1811.08129","repositories_listed":1,"syntology":null},{"url":"/paper/hpi-dhc-at-trec-2018-precision-medicine-track","slug":"hpi-dhc-at-trec-2018-precision-medicine-track","title":"HPI-DHC at TREC 2018 Precision Medicine Track","date":"2018-11-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deeptilebars-visualizing-term-distribution","slug":"deeptilebars-visualizing-term-distribution","title":"DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval","date":"2018-11-01","arxiv_id":"1811.00606","repositories_listed":1,"syntology":null},{"url":"/paper/improving-information-retrieval-results-for","slug":"improving-information-retrieval-results-for","title":"Improving Information Retrieval Results for Persian Documents using FarsNet","date":"2018-11-01","arxiv_id":"1811.00854","repositories_listed":1,"syntology":null},{"url":"/paper/openke-an-open-toolkit-for-knowledge","slug":"openke-an-open-toolkit-for-knowledge","title":"OpenKE: An Open Toolkit for Knowledge Embedding","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mulan-a-blind-and-off-grid-method-for","slug":"mulan-a-blind-and-off-grid-method-for","title":"MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval","date":"2018-10-31","arxiv_id":"1810.13338","repositories_listed":1,"syntology":null},{"url":"/paper/nprf-a-neural-pseudo-relevance-feedback","slug":"nprf-a-neural-pseudo-relevance-feedback","title":"NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval","date":"2018-10-30","arxiv_id":"1810.12936","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/nprf-a-neural-pseudo-relevance-feedback#ran","syntology_url":"https://syntology.ai/paper/1810.12936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12936"}},"official":{"repos":["ucasir/NPRF"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/spectrogram-channels-u-net-a-source","slug":"spectrogram-channels-u-net-a-source","title":"Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source","date":"2018-10-26","arxiv_id":"1810.11520","repositories_listed":1,"syntology":null},{"url":"/paper/from-neural-re-ranking-to-neural-ranking","slug":"from-neural-re-ranking-to-neural-ranking","title":"From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing","date":"2018-10-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/texttovec-deep-contextualized-neural","slug":"texttovec-deep-contextualized-neural","title":"textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior","date":"2018-10-09","arxiv_id":"1810.03947","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/texttovec-deep-contextualized-neural#ran","syntology_url":"https://syntology.ai/paper/1810.03947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03947"}},"official":{"repos":["pgcool/textTOvec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hyte-hyperplane-based-temporally-aware","slug":"hyte-hyperplane-based-temporally-aware","title":"HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/listwise-temporal-ordering-of-events-in","slug":"listwise-temporal-ordering-of-events-in","title":"Listwise temporal ordering of events in clinical notes","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ranking-paragraphs-for-improving-answer","slug":"ranking-paragraphs-for-improving-answer","title":"Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering","date":"2018-10-01","arxiv_id":"1810.00494","repositories_listed":1,"syntology":null},{"url":"/paper/lener-br-a-dataset-for-named-entity","slug":"lener-br-a-dataset-for-named-entity","title":"LeNER-Br: a Dataset for Named Entity Recognition in Brazilian Legal Text","date":"2018-09-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/skeleton-to-response-dialogue-generation","slug":"skeleton-to-response-dialogue-generation","title":"Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory","date":"2018-09-14","arxiv_id":"1809.05296","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/skeleton-to-response-dialogue-generation#ran","syntology_url":"https://syntology.ai/paper/1809.05296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05296"}},"official":{"repos":["jcyk/skeleton-to-response"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/edge2vec-representation-learning-using-edge","slug":"edge2vec-representation-learning-using-edge","title":"edge2vec: Representation learning using edge semantics for biomedical knowledge discovery","date":"2018-09-07","arxiv_id":"1809.02269","repositories_listed":1,"syntology":null},{"url":"/paper/trick-me-if-you-can-adversarial-writing-of","slug":"trick-me-if-you-can-adversarial-writing-of","title":"Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering","date":"2018-09-07","arxiv_id":"1809.02701","repositories_listed":1,"syntology":null},{"url":"/paper/towards-automated-customer-support","slug":"towards-automated-customer-support","title":"Towards Automated Customer Support","date":"2018-09-02","arxiv_id":"1809.00303","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-cover-song-detection-in-digital","slug":"large-scale-cover-song-detection-in-digital","title":"Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features","date":"2018-08-30","arxiv_id":"1808.10351","repositories_listed":1,"syntology":null},{"url":"/paper/deep-randomized-ensembles-for-metric-learning","slug":"deep-randomized-ensembles-for-metric-learning","title":"Deep Randomized Ensembles for Metric Learning","date":"2018-08-13","arxiv_id":"1808.04469","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-randomized-ensembles-for-metric-learning#ran","syntology_url":"https://syntology.ai/paper/1808.04469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04469"}},"official":{"repos":["littleredxh/DREML"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-feature-selection-and-evaluation-of","slug":"on-feature-selection-and-evaluation-of","title":"On feature selection and evaluation of transportation mode prediction strategies","date":"2018-08-09","arxiv_id":"1808.03096","repositories_listed":1,"syntology":null},{"url":"/paper/identifying-emergent-research-trends-by-key","slug":"identifying-emergent-research-trends-by-key","title":"Identifying Emergent Research Trends by Key Authors and Phrases","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-target-specific-representations-of","slug":"learning-target-specific-representations-of","title":"Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lyrics-segmentation-textual-macrostructure","slug":"lyrics-segmentation-textual-macrostructure","title":"Lyrics Segmentation: Textual Macrostructure Detection using Convolutions","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/novelty-goes-deep-a-deep-neural-solution-to","slug":"novelty-goes-deep-a-deep-neural-solution-to","title":"Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/task-oriented-word-embedding-for-text","slug":"task-oriented-word-embedding-for-text","title":"Task-oriented Word Embedding for Text Classification","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-morphology-learning-with","slug":"unsupervised-morphology-learning-with","title":"Unsupervised Morphology Learning with Statistical Paradigms","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-sense-disambiguation-based-on-word","slug":"word-sense-disambiguation-based-on-word","title":"Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/combining-a-context-aware-neural-network-with","slug":"combining-a-context-aware-neural-network-with","title":"Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities","date":"2018-07-16","arxiv_id":"1807.06414","repositories_listed":1,"syntology":null},{"url":"/paper/natural-language-processing-for-information","slug":"natural-language-processing-for-information","title":"Natural Language Processing for Information Extraction","date":"2018-07-06","arxiv_id":"1807.02383","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-distantly-supervised-open-domain","slug":"denoising-distantly-supervised-open-domain","title":"Denoising Distantly Supervised Open-Domain Question Answering","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-latent-meanings-of","slug":"incorporating-latent-meanings-of","title":"Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/jack-the-reader-a-a-machine-reading-framework","slug":"jack-the-reader-a-a-machine-reading-framework","title":"Jack the Reader -- A Machine Reading Framework","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-historical-significance-of-textual","slug":"the-historical-significance-of-textual","title":"The Historical Significance of Textual Distances","date":"2018-06-30","arxiv_id":"1807.00181","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-the-query-set-on-the-evaluation-of","slug":"impact-of-the-query-set-on-the-evaluation-of","title":"Impact of the Query Set on the Evaluation of Expert Finding Systems","date":"2018-06-28","arxiv_id":"1806.10813","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-on-the-names-of-points-of","slug":"an-empirical-study-on-the-names-of-points-of","title":"An empirical study on the names of points of interest and their changes with geographic distance","date":"2018-06-21","arxiv_id":"1806.08040","repositories_listed":1,"syntology":null},{"url":"/paper/named-entity-recognition-with-extremely","slug":"named-entity-recognition-with-extremely","title":"Named Entity Recognition with Extremely Limited Data","date":"2018-06-12","arxiv_id":"1806.04411","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-search-in-long-documents-using","slug":"learning-to-search-in-long-documents-using","title":"Learning to Search in Long Documents Using Document Structure","date":"2018-06-09","arxiv_id":"1806.03529","repositories_listed":1,"syntology":null},{"url":"/paper/jtav-jointly-learning-social-media-content","slug":"jtav-jointly-learning-social-media-content","title":"JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features","date":"2018-06-05","arxiv_id":"1806.01483","repositories_listed":1,"syntology":null},{"url":"/paper/tree-structured-dirichlet-processes-for","slug":"tree-structured-dirichlet-processes-for","title":"Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/entity-duet-neural-ranking-understanding-the-1","slug":"entity-duet-neural-ranking-understanding-the-1","title":"Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval","date":"2018-05-19","arxiv_id":"1805.07591","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-cluster-unary-loss-for-efficient","slug":"semantic-cluster-unary-loss-for-efficient","title":"Semantic Cluster Unary Loss for Efficient Deep Hashing","date":"2018-05-15","arxiv_id":"1805.08705","repositories_listed":1,"syntology":null},{"url":"/paper/nash-toward-end-to-end-neural-architecture","slug":"nash-toward-end-to-end-neural-architecture","title":"NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing","date":"2018-05-14","arxiv_id":"1805.05361","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-document-retrieval-using","slug":"cross-lingual-document-retrieval-using","title":"Cross-lingual Document Retrieval using Regularized Wasserstein Distance","date":"2018-05-11","arxiv_id":"1805.04437","repositories_listed":1,"syntology":null}],"record_sha256":"73cc14a9f4f40f670f8dd9663fcc7b23ad455909dad6645da142cbb1da48b8de","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}