{"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/articles/papers/11","list_of":"/task/articles","task":"Articles","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":41,"rows_per_page":100,"rows":[1001,1100],"of":4012,"counts":{"archive_papers_tagged":4012,"with_a_code_link":1123,"where_syntology_ran_a_sample":126,"not_listed_spam_title":0,"listed":4012,"listed_where_code_ran":126,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":107,"every_run_a_failure_of_syntologys_instrument":19,"listed_with_a_run_with_no_instrument_failure":107,"listed_every_run_a_failure_of_syntologys_instrument":19,"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/articles","prev":"/task/articles/papers/10","next":"/task/articles/papers/12","papers":[{"url":"/paper/multilingual-sentence-level-bias-detection-in","slug":"multilingual-sentence-level-bias-detection-in","title":"Multilingual sentence-level bias detection in Wikipedia","date":"2019-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/earlier-isnt-always-better-sub-aspect","slug":"earlier-isnt-always-better-sub-aspect","title":"Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization","date":"2019-08-30","arxiv_id":"1908.11723","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/earlier-isnt-always-better-sub-aspect#ran","syntology_url":"https://syntology.ai/paper/1908.11723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.11723"}},"official":null}},{"url":"/paper/detecting-toxicity-in-news-articles","slug":"detecting-toxicity-in-news-articles","title":"Detecting Toxicity in News Articles: Application to Bulgarian","date":"2019-08-26","arxiv_id":"1908.09785","repositories_listed":1,"syntology":null},{"url":"/paper/dc3-a-diagnostic-case-challenge-collection","slug":"dc3-a-diagnostic-case-challenge-collection","title":"DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support","date":"2019-08-22","arxiv_id":"1908.08581","repositories_listed":1,"syntology":null},{"url":"/paper/silcendice-mining-suspicious-multi-attribute","slug":"silcendice-mining-suspicious-multi-attribute","title":"SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs","date":"2019-08-19","arxiv_id":"1908.07087","repositories_listed":1,"syntology":null},{"url":"/paper/dpgmedia2019-a-dutch-news-dataset-for","slug":"dpgmedia2019-a-dutch-news-dataset-for","title":"DpgMedia2019: A Dutch News Dataset for Partisanship Detection","date":"2019-08-06","arxiv_id":"1908.02322","repositories_listed":1,"syntology":null},{"url":"/paper/the-myths-of-our-time-fake-news","slug":"the-myths-of-our-time-fake-news","title":"The Myths of Our Time: Fake News","date":"2019-08-05","arxiv_id":"1908.01760","repositories_listed":1,"syntology":null},{"url":"/paper/building-english-to-serbian-machine","slug":"building-english-to-serbian-machine","title":"Building English-to-Serbian Machine Translation System for IMDb Movie Reviews","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-figure-captions-and-descriptive","slug":"incorporating-figure-captions-and-descriptive","title":"Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/abstractive-document-summarization-without","slug":"abstractive-document-summarization-without","title":"Abstractive Document Summarization without Parallel Data","date":"2019-07-30","arxiv_id":"1907.12951","repositories_listed":1,"syntology":null},{"url":"/paper/what-is-this-article-about-extreme","slug":"what-is-this-article-about-extreme","title":"What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks","date":"2019-07-19","arxiv_id":"1907.08722","repositories_listed":1,"syntology":null},{"url":"/paper/the-dynamic-embedded-topic-model","slug":"the-dynamic-embedded-topic-model","title":"The Dynamic Embedded Topic Model","date":"2019-07-12","arxiv_id":"1907.05545","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-dynamic-embedded-topic-model#ran","syntology_url":"https://syntology.ai/paper/1907.05545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05545"}},"official":{"repos":["adjidieng/DETM"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-short-text-classification-through","slug":"improving-short-text-classification-through","title":"Improving short text classification through global augmentation methods","date":"2019-07-07","arxiv_id":"1907.03752","repositories_listed":1,"syntology":null},{"url":"/paper/coherent-comments-generation-for-chinese","slug":"coherent-comments-generation-for-chinese","title":"Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-commentator-automatic-machine","slug":"cross-modal-commentator-automatic-machine","title":"Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inducing-document-structure-for-aspect-based","slug":"inducing-document-structure-for-aspect-based","title":"Inducing Document Structure for Aspect-based Summarization","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/katecheo-a-portable-and-modular-system-for","slug":"katecheo-a-portable-and-modular-system-for","title":"Katecheo: A Portable and Modular System for Multi-Topic Question Answering","date":"2019-07-01","arxiv_id":"1907.00854","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-coherence-of-fake-news-articles","slug":"on-the-coherence-of-fake-news-articles","title":"On the Coherence of Fake News Articles","date":"2019-06-26","arxiv_id":"1906.11126","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-research-trends-with-semantic-and","slug":"predicting-research-trends-with-semantic-and","title":"Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics","date":"2019-06-17","arxiv_id":"1906.06843","repositories_listed":1,"syntology":null},{"url":"/paper/biset-bi-directional-selective-encoding-with","slug":"biset-bi-directional-selective-encoding-with","title":"BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization","date":"2019-06-12","arxiv_id":"1906.05012","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/word-embeddings-for-the-armenian-language","slug":"word-embeddings-for-the-armenian-language","title":"Word Embeddings for the Armenian Language: Intrinsic and Extrinsic Evaluation","date":"2019-06-07","arxiv_id":"1906.03134","repositories_listed":1,"syntology":null},{"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/coherent-comment-generation-for-chinese","slug":"coherent-comment-generation-for-chinese","title":"Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model","date":"2019-06-04","arxiv_id":"1906.01231","repositories_listed":1,"syntology":null},{"url":"/paper/multi-news-a-large-scale-multi-document","slug":"multi-news-a-large-scale-multi-document","title":"Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model","date":"2019-06-04","arxiv_id":"1906.01749","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-based-article-identification-in","slug":"clustering-based-article-identification-in","title":"Clustering-Based Article Identification in Historical Newspapers","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/team-peter-brinkmann-at-semeval-2019-task-4","slug":"team-peter-brinkmann-at-semeval-2019-task-4","title":"Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/190601512","slug":"190601512","title":"LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization","date":"2019-05-28","arxiv_id":"1906.01512","repositories_listed":1,"syntology":null},{"url":"/paper/a-cross-domain-transferable-neural-coherence","slug":"a-cross-domain-transferable-neural-coherence","title":"A Cross-Domain Transferable Neural Coherence Model","date":"2019-05-28","arxiv_id":"1905.11912","repositories_listed":1,"syntology":null},{"url":"/paper/190513132","slug":"190513132","title":"Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs","date":"2019-05-24","arxiv_id":"1905.13132","repositories_listed":1,"syntology":null},{"url":"/paper/structured-summarization-of-academic","slug":"structured-summarization-of-academic","title":"Structured Summarization of Academic Publications","date":"2019-05-19","arxiv_id":"1905.07695","repositories_listed":1,"syntology":null},{"url":"/paper/check-it-a-plugin-for-detecting-and-reducing","slug":"check-it-a-plugin-for-detecting-and-reducing","title":"Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web","date":"2019-05-10","arxiv_id":"1905.04260","repositories_listed":1,"syntology":null},{"url":"/paper/bvs-corpus-a-multilingual-parallel-corpus-of","slug":"bvs-corpus-a-multilingual-parallel-corpus-of","title":"BVS Corpus: A Multilingual Parallel Corpus of Biomedical Scientific Texts","date":"2019-05-05","arxiv_id":"1905.01712","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/recommending-research-articles-to-consumers","slug":"recommending-research-articles-to-consumers","title":"Recommending research articles to consumers of online vaccination information","date":"2019-04-26","arxiv_id":"1904.11886","repositories_listed":1,"syntology":null},{"url":"/paper/importance-of-copying-mechanism-for-news","slug":"importance-of-copying-mechanism-for-news","title":"Importance of Copying Mechanism for News Headline Generation","date":"2019-04-25","arxiv_id":"1904.11475","repositories_listed":1,"syntology":null},{"url":"/paper/listening-between-the-lines-learning-personal","slug":"listening-between-the-lines-learning-personal","title":"Listening between the Lines: Learning Personal Attributes from Conversations","date":"2019-04-24","arxiv_id":"1904.10887","repositories_listed":1,"syntology":null},{"url":"/paper/who-blames-whom-in-a-crisis-detecting-blame","slug":"who-blames-whom-in-a-crisis-detecting-blame","title":"Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks","date":"2019-04-24","arxiv_id":"1904.10637","repositories_listed":1,"syntology":null},{"url":"/paper/no-permanent-friends-or-enemies-tracking","slug":"no-permanent-friends-or-enemies-tracking","title":"No Permanent Friends or Enemies: Tracking Relationships between Nations from News","date":"2019-04-18","arxiv_id":"1904.08950","repositories_listed":1,"syntology":null},{"url":"/paper/topic-grouper-an-agglomerative-clustering","slug":"topic-grouper-an-agglomerative-clustering","title":"Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling","date":"2019-04-13","arxiv_id":"1904.06483","repositories_listed":1,"syntology":null},{"url":"/paper/190501962","slug":"190501962","title":"Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector","date":"2019-04-10","arxiv_id":"1905.01962","repositories_listed":1,"syntology":null},{"url":"/paper/eliciting-new-wikipedia-users-interests-via","slug":"eliciting-new-wikipedia-users-interests-via","title":"Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start","date":"2019-04-08","arxiv_id":"1904.03889","repositories_listed":1,"syntology":null},{"url":"/paper/a-literature-study-of-embeddings-on-source","slug":"a-literature-study-of-embeddings-on-source","title":"A Literature Study of Embeddings on Source Code","date":"2019-04-05","arxiv_id":"1904.03061","repositories_listed":1,"syntology":null},{"url":"/paper/probing-biomedical-embeddings-from-language","slug":"probing-biomedical-embeddings-from-language","title":"Probing Biomedical Embeddings from Language Models","date":"2019-04-03","arxiv_id":"1904.02181","repositories_listed":1,"syntology":null},{"url":"/paper/good-news-everyone-context-driven-entity","slug":"good-news-everyone-context-driven-entity","title":"Good News, Everyone! Context driven entity-aware captioning for news images","date":"2019-04-02","arxiv_id":"1904.01475","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/a-graph-structured-dataset-for-wikipedia","slug":"a-graph-structured-dataset-for-wikipedia","title":"A Graph-structured Dataset for Wikipedia Research","date":"2019-03-20","arxiv_id":"1903.08597","repositories_listed":1,"syntology":null},{"url":"/paper/props-probabilistic-personalization-of-black","slug":"props-probabilistic-personalization-of-black","title":"PROPS: Probabilistic personalization of black-box sequence models","date":"2019-03-05","arxiv_id":"1903.02013","repositories_listed":1,"syntology":null},{"url":"/paper/using-natural-language-processing-techniques","slug":"using-natural-language-processing-techniques","title":"Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora","date":"2019-03-01","arxiv_id":"1903.00415","repositories_listed":1,"syntology":null},{"url":"/paper/citation-needed-a-taxonomy-and-algorithmic","slug":"citation-needed-a-taxonomy-and-algorithmic","title":"Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability","date":"2019-02-28","arxiv_id":"1902.11116","repositories_listed":1,"syntology":null},{"url":"/paper/graph-adversarial-training-dynamically","slug":"graph-adversarial-training-dynamically","title":"Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure","date":"2019-02-20","arxiv_id":"1902.08226","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/team-papelo-transformer-networks-at-fever","slug":"team-papelo-transformer-networks-at-fever","title":"Team Papelo: Transformer Networks at FEVER","date":"2019-01-08","arxiv_id":"1901.02534","repositories_listed":1,"syntology":null},{"url":"/paper/statement-networks-a-power-structure","slug":"statement-networks-a-power-structure","title":"Statement networks: a power structure narrative as depicted by newspapers","date":"2018-12-10","arxiv_id":"1812.03632","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/abstractive-summarization-of-reddit-posts","slug":"abstractive-summarization-of-reddit-posts","title":"Abstractive Summarization of Reddit Posts with Multi-level Memory Networks","date":"2018-11-02","arxiv_id":"1811.00783","repositories_listed":1,"syntology":null},{"url":"/paper/the-data-challenge-in-misinformation","slug":"the-data-challenge-in-misinformation","title":"The Data Challenge in Misinformation Detection: Source Reputation vs. Content Veracity","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/suspicious-news-detection-using-micro-blog","slug":"suspicious-news-detection-using-micro-blog","title":"Suspicious News Detection Using Micro Blog Text","date":"2018-10-27","arxiv_id":"1810.11663","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-hierarchical-alignment-for-author","slug":"large-scale-hierarchical-alignment-for-author","title":"Large-scale Hierarchical Alignment for Data-driven Text Rewriting","date":"2018-10-18","arxiv_id":"1810.08237","repositories_listed":1,"syntology":null},{"url":"/paper/indosum-a-new-benchmark-dataset-for","slug":"indosum-a-new-benchmark-dataset-for","title":"IndoSum: A New Benchmark Dataset for Indonesian Text Summarization","date":"2018-10-12","arxiv_id":"1810.05334","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/indosum-a-new-benchmark-dataset-for#ran","syntology_url":"https://syntology.ai/paper/1810.05334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05334"}},"official":{"repos":["kata-ai/indosum"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluation-of-a-sequence-tagging-tool-for","slug":"evaluation-of-a-sequence-tagging-tool-for","title":"Evaluation of a Sequence Tagging Tool for Biomedical Texts","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/legal-judgment-prediction-via-topological","slug":"legal-judgment-prediction-via-topological","title":"Legal Judgment Prediction via Topological Learning","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-a-transparency-driven-environment","slug":"utilizing-a-transparency-driven-environment","title":"Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History","date":"2018-10-01","arxiv_id":"1810.00968","repositories_listed":1,"syntology":null},{"url":"/paper/towards-exploiting-background-knowledge-for","slug":"towards-exploiting-background-knowledge-for","title":"Towards Exploiting Background Knowledge for Building Conversation Systems","date":"2018-09-21","arxiv_id":"1809.08205","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-and-analyzing-semantic-relatedness","slug":"extracting-and-analyzing-semantic-relatedness","title":"Extracting and Analyzing Semantic Relatedness between Cities Using News Articles","date":"2018-09-08","arxiv_id":"1809.02823","repositories_listed":1,"syntology":null},{"url":"/paper/crowdsourcing-storylines-harnessing-the-crowd","slug":"crowdsourcing-storylines-harnessing-the-crowd","title":"Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gold-corpus-for-telegraphic-summarization","slug":"gold-corpus-for-telegraphic-summarization","title":"Gold Corpus for Telegraphic Summarization","date":"2018-08-01","arxiv_id":null,"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/neural-article-pair-modeling-for-wikipedia","slug":"neural-article-pair-modeling-for-wikipedia","title":"Neural Article Pair Modeling for Wikipedia Sub-article Matching","date":"2018-07-31","arxiv_id":"1807.11689","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/identifying-key-sentences-for-precision","slug":"identifying-key-sentences-for-precision","title":"Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning","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/extracting-parallel-sentences-with","slug":"extracting-parallel-sentences-with","title":"Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation","date":"2018-06-13","arxiv_id":"1806.05559","repositories_listed":1,"syntology":null},{"url":"/paper/scidtb-discourse-dependency-treebank-for","slug":"scidtb-discourse-dependency-treebank-for","title":"SciDTB: Discourse Dependency TreeBank for Scientific Abstracts","date":"2018-06-10","arxiv_id":"1806.03653","repositories_listed":1,"syntology":null},{"url":"/paper/drcd-a-chinese-machine-reading-comprehension","slug":"drcd-a-chinese-machine-reading-comprehension","title":"DRCD: a Chinese Machine Reading Comprehension Dataset","date":"2018-06-04","arxiv_id":"1806.00920","repositories_listed":1,"syntology":null},{"url":"/paper/deep-dirichlet-multinomial-regression","slug":"deep-dirichlet-multinomial-regression","title":"Deep Dirichlet Multinomial Regression","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inducing-temporal-relations-from-time-anchor","slug":"inducing-temporal-relations-from-time-anchor","title":"Inducing Temporal Relations from Time Anchor Annotation","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/prediction-for-the-newsroom-which-articles","slug":"prediction-for-the-newsroom-which-articles","title":"Prediction for the Newsroom: Which Articles Will Get the Most Comments?","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/harvesting-paragraph-level-question-answer","slug":"harvesting-paragraph-level-question-answer","title":"Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia","date":"2018-05-15","arxiv_id":"1805.05942","repositories_listed":1,"syntology":null},{"url":"/paper/profiling-medical-journal-articles-using-a","slug":"profiling-medical-journal-articles-using-a","title":"Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/quootstrap-scalable-unsupervised-extraction","slug":"quootstrap-scalable-unsupervised-extraction","title":"Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping","date":"2018-04-07","arxiv_id":"1804.02525","repositories_listed":1,"syntology":null},{"url":"/paper/authorship-verification-in-the-absence-of","slug":"authorship-verification-in-the-absence-of","title":"Authorship verification in the absence of explicit features and thresholds","date":"2018-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/content-based-citation-recommendation","slug":"content-based-citation-recommendation","title":"Content-Based Citation Recommendation","date":"2018-02-22","arxiv_id":"1802.08301","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/content-based-citation-recommendation#ran","syntology_url":"https://syntology.ai/paper/1802.08301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08301"}},"official":{"repos":["allenai/citeomatic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-rumor-news-using-agreement","slug":"investigating-rumor-news-using-agreement","title":"Investigating Rumor News Using Agreement-Aware Search","date":"2018-02-21","arxiv_id":"1802.07398","repositories_listed":1,"syntology":null},{"url":"/paper/matching-long-text-documents-via-graph","slug":"matching-long-text-documents-via-graph","title":"Matching Article Pairs with Graphical Decomposition and Convolutions","date":"2018-02-21","arxiv_id":"1802.07459","repositories_listed":1,"syntology":null},{"url":"/paper/dvqa-understanding-data-visualizations-via","slug":"dvqa-understanding-data-visualizations-via","title":"DVQA: Understanding Data Visualizations via Question Answering","date":"2018-01-24","arxiv_id":"1801.08163","repositories_listed":1,"syntology":null},{"url":"/paper/matching-with-text-data-an-experimental","slug":"matching-with-text-data-an-experimental","title":"Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality","date":"2018-01-02","arxiv_id":"1801.00644","repositories_listed":1,"syntology":null},{"url":"/paper/relation-extraction-a-survey","slug":"relation-extraction-a-survey","title":"Relation Extraction : A Survey","date":"2017-12-14","arxiv_id":"1712.05191","repositories_listed":1,"syntology":null},{"url":"/paper/171104305","slug":"171104305","title":"Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey","date":"2017-11-12","arxiv_id":"1711.04305","repositories_listed":1,"syntology":null},{"url":"/paper/text-annotation-graphs-annotating-complex","slug":"text-annotation-graphs-annotating-complex","title":"Text Annotation Graphs: Annotating Complex Natural Language Phenomena","date":"2017-11-01","arxiv_id":"1711.00529","repositories_listed":1,"syntology":null},{"url":"/paper/wikipedia-graph-mining-dynamic-structure-of","slug":"wikipedia-graph-mining-dynamic-structure-of","title":"Wikipedia graph mining: dynamic structure of collective memory","date":"2017-10-01","arxiv_id":"1710.00398","repositories_listed":1,"syntology":null},{"url":"/paper/towards-building-a-knowledge-base-of-monetary","slug":"towards-building-a-knowledge-base-of-monetary","title":"Towards Building a Knowledge Base of Monetary Transactions from a News Collection","date":"2017-09-18","arxiv_id":"1709.05743","repositories_listed":1,"syntology":null},{"url":"/paper/aggregating-and-predicting-sequence-labels","slug":"aggregating-and-predicting-sequence-labels","title":"Aggregating and Predicting Sequence Labels from Crowd Annotations","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/temporal-word-analogies-identifying-lexical","slug":"temporal-word-analogies-identifying-lexical","title":"Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automatic-synonym-discovery-with-knowledge","slug":"automatic-synonym-discovery-with-knowledge","title":"Automatic Synonym Discovery with Knowledge Bases","date":"2017-06-25","arxiv_id":"1706.08186","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-exploration-and-discovery-of","slug":"interactive-exploration-and-discovery-of","title":"Interactive Exploration and Discovery of Scientific Publications with PubVis","date":"2017-06-25","arxiv_id":"1706.08094","repositories_listed":1,"syntology":null},{"url":"/paper/neural-domain-adaptation-for-biomedical","slug":"neural-domain-adaptation-for-biomedical","title":"Neural Domain Adaptation for Biomedical Question Answering","date":"2017-06-12","arxiv_id":"1706.03610","repositories_listed":1,"syntology":null},{"url":"/paper/wikipedia-vandal-early-detection-from-user","slug":"wikipedia-vandal-early-detection-from-user","title":"Wikipedia Vandal Early Detection: from User Behavior to User Embedding","date":"2017-06-03","arxiv_id":"1706.00887","repositories_listed":1,"syntology":null},{"url":"/paper/using-titles-vs-full-text-as-source-for","slug":"using-titles-vs-full-text-as-source-for","title":"Using Titles vs. Full-text as Source for Automated Semantic Document Annotation","date":"2017-05-15","arxiv_id":"1705.05311","repositories_listed":1,"syntology":null},{"url":"/paper/friendships-rivalries-and-trysts","slug":"friendships-rivalries-and-trysts","title":"Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts","date":"2017-04-25","arxiv_id":"1704.07828","repositories_listed":1,"syntology":null}],"record_sha256":"4b63a1f221a9ae2fa06eec409ba1cfbaa21ba8738dde30ce885d887400919277","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}