{"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/12","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":12,"pages_in_order":48,"rows_per_page":100,"rows":[1101,1200],"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/11","next":"/task/information-retrieval/papers/13","papers":[{"url":"/paper/unsupervised-cross-lingual-information","slug":"unsupervised-cross-lingual-information","title":"Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only","date":"2018-05-02","arxiv_id":"1805.00879","repositories_listed":1,"syntology":null},{"url":"/paper/bioread-a-new-dataset-for-biomedical-reading","slug":"bioread-a-new-dataset-for-biomedical-reading","title":"BioRead: A New Dataset for Biomedical Reading Comprehension","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/c-hts-a-concept-based-hierarchical-text","slug":"c-hts-a-concept-based-hierarchical-text","title":"C-HTS: A Concept-based Hierarchical Text Segmentation approach","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/krauts-a-german-temporally-annotated-news","slug":"krauts-a-german-temporally-annotated-news","title":"KRAUTS: A German Temporally Annotated News Corpus","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/parallel-corpora-for-the-biomedical-domain","slug":"parallel-corpora-for-the-biomedical-domain","title":"Parallel Corpora for the Biomedical Domain","date":"2018-05-01","arxiv_id":null,"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/two-multilingual-corpora-extracted-from-the","slug":"two-multilingual-corpora-extracted-from-the","title":"Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-embedding-approach-for-synonym","slug":"word-embedding-approach-for-synonym","title":"Word Embedding Approach for Synonym Extraction of Multi-Word Terms","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-deep-listwise-context-model-for","slug":"learning-a-deep-listwise-context-model-for","title":"Learning a Deep Listwise Context Model for Ranking Refinement","date":"2018-04-16","arxiv_id":"1804.05936","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/learning-a-deep-listwise-context-model-for#ran","syntology_url":"https://syntology.ai/paper/1804.05936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05936"}},"official":{"repos":["QingyaoAi/Deep-Listwise-Context-Model-for-Ranking-Refinement"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/improving-the-representation-and-conversion","slug":"improving-the-representation-and-conversion","title":"Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context","date":"2018-04-13","arxiv_id":"1804.04956","repositories_listed":1,"syntology":null},{"url":"/paper/web2text-deep-structured-boilerplate-removal","slug":"web2text-deep-structured-boilerplate-removal","title":"Web2Text: Deep Structured Boilerplate Removal","date":"2018-03-27","arxiv_id":"1801.02607","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/one-deep-music-representation-to-rule-them","slug":"one-deep-music-representation-to-rule-them","title":"One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies","date":"2018-02-12","arxiv_id":"1802.04051","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-vector-space-model-sparse","slug":"revisiting-the-vector-space-model-sparse","title":"Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification","date":"2018-02-12","arxiv_id":"1802.03938","repositories_listed":1,"syntology":null},{"url":"/paper/sentipers-a-sentiment-analysis-corpus-for","slug":"sentipers-a-sentiment-analysis-corpus-for","title":"SentiPers: A Sentiment Analysis Corpus for Persian","date":"2018-01-23","arxiv_id":"1801.07737","repositories_listed":1,"syntology":null},{"url":"/paper/a-resource-light-method-for-cross-lingual","slug":"a-resource-light-method-for-cross-lingual","title":"A Resource-Light Method for Cross-Lingual Semantic Textual Similarity","date":"2018-01-19","arxiv_id":"1801.06436","repositories_listed":1,"syntology":null},{"url":"/paper/use-of-deep-learning-in-modern-recommendation","slug":"use-of-deep-learning-in-modern-recommendation","title":"Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works","date":"2017-12-20","arxiv_id":"1712.07525","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-memory-networks-efficient-machine","slug":"attentive-memory-networks-efficient-machine","title":"Attentive Memory Networks: Efficient Machine Reading for Conversational Search","date":"2017-12-19","arxiv_id":"1712.07229","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/balancing-speed-and-quality-in-online","slug":"balancing-speed-and-quality-in-online","title":"Balancing Speed and Quality in Online Learning to Rank for Information Retrieval","date":"2017-11-26","arxiv_id":"1711.09446","repositories_listed":1,"syntology":null},{"url":"/paper/rdf2vec-rdf-graph-embeddings-and-their","slug":"rdf2vec-rdf-graph-embeddings-and-their","title":"RDF2Vec: RDF Graph Embeddings and Their Applications","date":"2017-11-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-generative-framework-for-paraphrase","slug":"a-deep-generative-framework-for-paraphrase","title":"A Deep Generative Framework for Paraphrase Generation","date":"2017-09-15","arxiv_id":"1709.05074","repositories_listed":1,"syntology":null},{"url":"/paper/combining-graph-degeneracy-and-submodularity","slug":"combining-graph-degeneracy-and-submodularity","title":"Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/do-not-trust-the-trolls-predicting","slug":"do-not-trust-the-trolls-predicting","title":"Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transition-based-disfluency-detection-using","slug":"transition-based-disfluency-detection-using","title":"Transition-Based Disfluency Detection using LSTMs","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/r3-reinforced-reader-ranker-for-open-domain","slug":"r3-reinforced-reader-ranker-for-open-domain","title":"R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering","date":"2017-08-31","arxiv_id":"1709.00023","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-measures-for-relevance-and","slug":"evaluation-measures-for-relevance-and","title":"Evaluation Measures for Relevance and Credibility in Ranked Lists","date":"2017-08-23","arxiv_id":"1708.07157","repositories_listed":1,"syntology":null},{"url":"/paper/simple-and-effective-dimensionality-reduction","slug":"simple-and-effective-dimensionality-reduction","title":"Simple and Effective Dimensionality Reduction for Word Embeddings","date":"2017-08-11","arxiv_id":"1708.03629","repositories_listed":1,"syntology":null},{"url":"/paper/learning-what-is-essential-in-questions","slug":"learning-what-is-essential-in-questions","title":"Learning What is Essential in Questions","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/matchzoo-a-toolkit-for-deep-text-matching","slug":"matchzoo-a-toolkit-for-deep-text-matching","title":"MatchZoo: A Toolkit for Deep Text Matching","date":"2017-07-23","arxiv_id":"1707.07270","repositories_listed":1,"syntology":null},{"url":"/paper/lyrics-based-music-genre-classification-using","slug":"lyrics-based-music-genre-classification-using","title":"Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network","date":"2017-07-15","arxiv_id":"1707.04678","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-submodular-rank-aggregation-on","slug":"unsupervised-submodular-rank-aggregation-on","title":"Unsupervised Submodular Rank Aggregation on Score-based Permutations","date":"2017-07-04","arxiv_id":"1707.01166","repositories_listed":1,"syntology":null},{"url":"/paper/an-approach-for-weakly-supervised-deep","slug":"an-approach-for-weakly-supervised-deep","title":"Content-Based Weak Supervision for Ad-Hoc Re-Ranking","date":"2017-07-01","arxiv_id":"1707.00189","repositories_listed":1,"syntology":null},{"url":"/paper/building-a-non-trivial-paraphrase-corpus","slug":"building-a-non-trivial-paraphrase-corpus","title":"Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation Systems","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exploring-diachronic-lexical-semantics-with","slug":"exploring-diachronic-lexical-semantics-with","title":"Exploring Diachronic Lexical Semantics with JeSemE","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/time-expression-analysis-and-recognition","slug":"time-expression-analysis-and-recognition","title":"Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/topical-coherence-in-lda-based-models-through","slug":"topical-coherence-in-lda-based-models-through","title":"Topical Coherence in LDA-based Models through Induced Segmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/benchmark-for-complex-answer-retrieval-1","slug":"benchmark-for-complex-answer-retrieval-1","title":"Benchmark for Complex Answer Retrieval","date":"2017-05-13","arxiv_id":"1705.04803","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-active-search-using-linear-similarity","slug":"scaling-active-search-using-linear-similarity","title":"Scaling Active Search using Linear Similarity Functions","date":"2017-04-30","arxiv_id":"1705.00334","repositories_listed":1,"syntology":null},{"url":"/paper/neural-ranking-models-with-weak-supervision","slug":"neural-ranking-models-with-weak-supervision","title":"Neural Ranking Models with Weak Supervision","date":"2017-04-28","arxiv_id":"1704.08803","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-search-for-structured-data-via","slug":"probabilistic-search-for-structured-data-via","title":"Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes","date":"2017-04-04","arxiv_id":"1704.01087","repositories_listed":1,"syntology":null},{"url":"/paper/automated-wordnet-construction-using-word","slug":"automated-wordnet-construction-using-word","title":"Automated WordNet Construction Using Word Embeddings","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-information-retrieval-for","slug":"discriminative-information-retrieval-for","title":"Discriminative Information Retrieval for Question Answering Sentence Selection","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/integrating-semantic-knowledge-into-lexical","slug":"integrating-semantic-knowledge-into-lexical","title":"Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-demand-injection-of-lexical-knowledge-for","slug":"on-demand-injection-of-lexical-knowledge-for","title":"On-demand Injection of Lexical Knowledge for Recognising Textual Entailment","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/which-is-the-effective-way-for-gaokao","slug":"which-is-the-effective-way-for-gaokao","title":"Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/luandri-a-clean-lua-interface-to-the-indri","slug":"luandri-a-clean-lua-interface-to-the-indri","title":"Luandri: a Clean Lua Interface to the Indri Search Engine","date":"2017-02-16","arxiv_id":"1702.05042","repositories_listed":1,"syntology":null},{"url":"/paper/combination-of-convolutional-and-recurrent","slug":"combination-of-convolutional-and-recurrent","title":"Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-graph-degeneracy-based-approach-to-keyword","slug":"a-graph-degeneracy-based-approach-to-keyword","title":"A Graph Degeneracy-based Approach to Keyword Extraction","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-network-language-models","slug":"convolutional-neural-network-language-models","title":"Convolutional Neural Network Language Models","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/real-time-news-story-detection-and-tracking","slug":"real-time-news-story-detection-and-tracking","title":"Real-time News Story Detection and Tracking with Hashtags","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tweetime-a-minimally-supervised-method-for-1","slug":"tweetime-a-minimally-supervised-method-for-1","title":"TweeTime : A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-match-using-local-and-distributed-1","slug":"learning-to-match-using-local-and-distributed-1","title":"Learning to Match Using Local and Distributed Representations of Text for Web Search","date":"2016-10-26","arxiv_id":"1610.08136","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-word-representations-via","slug":"cross-lingual-word-representations-via","title":"Cross-Lingual Word Representations via Spectral Graph Embeddings","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/meta-a-unified-toolkit-for-text-retrieval-and","slug":"meta-a-unified-toolkit-for-text-retrieval-and","title":"MeTA: A Unified Toolkit for Text Retrieval and Analysis","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-text-segmentation-using-semantic","slug":"unsupervised-text-segmentation-using-semantic","title":"Unsupervised Text Segmentation Using Semantic Relatedness Graphs","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explaining-deep-convolutional-neural-networks","slug":"explaining-deep-convolutional-neural-networks","title":"Explaining Deep Convolutional Neural Networks on Music Classification","date":"2016-07-08","arxiv_id":"1607.02444","repositories_listed":1,"syntology":null},{"url":"/paper/bangla-parts-of-speech-tagging-using-bangla","slug":"bangla-parts-of-speech-tagging-using-bangla","title":"Bangla Parts-of-Speech Tagging using Bangla Stemmer and Rule based Analyzer","date":"2016-06-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-convolutional-neural-networks-for-6","slug":"deep-convolutional-neural-networks-for-6","title":"Deep convolutional neural networks for predominant instrument recognition in polyphonic music","date":"2016-05-31","arxiv_id":"1605.09507","repositories_listed":1,"syntology":null},{"url":"/paper/computing-web-scale-topic-models-using-an","slug":"computing-web-scale-topic-models-using-an","title":"Computing Web-scale Topic Models using an Asynchronous Parameter Server","date":"2016-05-24","arxiv_id":"1605.07422","repositories_listed":1,"syntology":null},{"url":"/paper/joint-learning-of-sentence-embeddings-for","slug":"joint-learning-of-sentence-embeddings-for","title":"Joint Learning of Sentence Embeddings for Relevance and Entailment","date":"2016-05-16","arxiv_id":"1605.04655","repositories_listed":1,"syntology":null},{"url":"/paper/gate-time-extraction-of-temporal-expressions","slug":"gate-time-extraction-of-temporal-expressions","title":"GATE-Time: Extraction of Temporal Expressions and Events","date":"2016-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/linear-algebraic-structure-of-word-senses","slug":"linear-algebraic-structure-of-word-senses","title":"Linear Algebraic Structure of Word Senses, with Applications to Polysemy","date":"2016-01-14","arxiv_id":"1601.03764","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-state-variational-autoencoders-for","slug":"discrete-state-variational-autoencoders-for","title":"Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations","date":"2016-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-architecture-for-semantic-matching","slug":"a-deep-architecture-for-semantic-matching","title":"A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations","date":"2015-11-26","arxiv_id":"1511.08277","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-instrument-recognition-in","slug":"automatic-instrument-recognition-in","title":"Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks","date":"2015-11-17","arxiv_id":"1511.05520","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-urdu-news-using-headlines","slug":"clustering-urdu-news-using-headlines","title":"Clustering Urdu News Using Headlines","date":"2015-09-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-baseline-temporal-tagger-for-all-languages","slug":"a-baseline-temporal-tagger-for-all-languages","title":"A Baseline Temporal Tagger for all Languages","date":"2015-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-bag-of-features-model-for-music-auto","slug":"a-deep-bag-of-features-model-for-music-auto","title":"A Deep Bag-of-Features Model for Music Auto-Tagging","date":"2015-08-20","arxiv_id":"1508.04999","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-dependency-parsing-based-on","slug":"cross-lingual-dependency-parsing-based-on","title":"Cross-lingual Dependency Parsing Based on Distributed Representations","date":"2015-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-relation-extraction-framework-for","slug":"towards-a-relation-extraction-framework-for","title":"Towards a relation extraction framework for cyber-security concepts","date":"2015-04-16","arxiv_id":"1504.04317","repositories_listed":1,"syntology":null},{"url":"/paper/oadaboost-an-adaboost-variant-for-ordinal","slug":"oadaboost-an-adaboost-variant-for-ordinal","title":"oAdaBoost: An AdaBoost Variant for Ordinal Classification","date":"2015-01-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/recognition-of-sarcasms-in-tweets-based-on","slug":"recognition-of-sarcasms-in-tweets-based-on","title":"Recognition of Sarcasms in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches","date":"2014-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-information-retrieval-approach-to-short","slug":"an-information-retrieval-approach-to-short","title":"An Information Retrieval Approach to Short Text Conversation","date":"2014-08-29","arxiv_id":"1408.6988","repositories_listed":1,"syntology":null},{"url":"/paper/uow-nlp-techniques-developed-at-the","slug":"uow-nlp-techniques-developed-at-the","title":"UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment","date":"2014-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-sentiment-specific-word-embedding","slug":"learning-sentiment-specific-word-embedding","title":"Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification","date":"2014-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-toolkit-for-efficient-learning-of-lexical","slug":"a-toolkit-for-efficient-learning-of-lexical","title":"A Toolkit for Efficient Learning of Lexical Units for Speech Recognition","date":"2014-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-comparison-of-centrality-measures-for-graph","slug":"a-comparison-of-centrality-measures-for-graph","title":"A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction","date":"2013-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/context-independent-term-mapper-for-european","slug":"context-independent-term-mapper-for-european","title":"Context Independent Term Mapper for European Languages","date":"2013-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/chinese-parsing-exploiting-characters","slug":"chinese-parsing-exploiting-characters","title":"Chinese Parsing Exploiting Characters","date":"2013-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detecting-multiword-phrases-in-mathematical","slug":"detecting-multiword-phrases-in-mathematical","title":"Detecting multiword phrases in mathematical text corpora","date":"2012-10-02","arxiv_id":"1210.0852","repositories_listed":1,"syntology":null},{"url":"/paper/finding-structure-in-text-genome-and-other","slug":"finding-structure-in-text-genome-and-other","title":"Finding Structure in Text, Genome and Other Symbolic Sequences","date":"2012-07-08","arxiv_id":"1207.1847","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-topic-coherence-over-many-models","slug":"exploring-topic-coherence-over-many-models","title":"Exploring Topic Coherence over Many Models and Many Topics","date":"2012-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/timen-an-open-temporal-expression","slug":"timen-an-open-temporal-expression","title":"TIMEN: An Open Temporal Expression Normalisation Resource","date":"2012-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-bagging-svm-to-learn-from-positive-and","slug":"a-bagging-svm-to-learn-from-positive-and","title":"A bagging SVM to learn from positive and unlabeled examples","date":"2010-10-05","arxiv_id":"1010.0772","repositories_listed":1,"syntology":null},{"url":"/paper/the-anatomy-of-mitos-web-search-engine","slug":"the-anatomy-of-mitos-web-search-engine","title":"The Anatomy of Mitos Web Search Engine","date":"2008-03-14","arxiv_id":"0803.2220","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-evaluation-of-four-tensor","slug":"empirical-evaluation-of-four-tensor","title":"Empirical Evaluation of Four Tensor Decomposition Algorithms","date":"2007-11-13","arxiv_id":"0711.2023","repositories_listed":1,"syntology":null},{"url":"/paper/a-sequential-algorithm-for-training-text","slug":"a-sequential-algorithm-for-training-text","title":"A Sequential Algorithm for Training Text Classifiers","date":"1994-07-24","arxiv_id":"cmp-lg/9407020","repositories_listed":1,"syntology":null},{"url":null,"slug":"overview-of-the-talentclef-2025-skill-and-job","title":"Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management","date":"2025-07-17","arxiv_id":"2507.13275","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-chaos-to-automation-enabling-the-use-of","title":"From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation","date":"2025-07-15","arxiv_id":"2507.11364","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-information-retrieval-via-time","title":"Temporal Information Retrieval via Time-Specifier Model Merging","date":"2025-07-09","arxiv_id":"2507.06782","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-vision-language-models-for","title":"An analysis of vision-language models for fabric retrieval","date":"2025-07-07","arxiv_id":"2507.04735","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-automatic-term-extraction-with","title":"Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval","date":"2025-06-26","arxiv_id":"2506.21222","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-information-retrieval-for-open","title":"Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision","date":"2025-06-25","arxiv_id":"2506.20070","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-scaled-graphrag-improving-multi-hop","title":"Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs","date":"2025-06-24","arxiv_id":"2506.19967","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-estimation-for-domain","title":"Semantic similarity estimation for domain specific data using BERT and other techniques","date":"2025-06-23","arxiv_id":"2506.18602","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-graph-based-approximate-nearest","title":"Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities","date":"2025-06-19","arxiv_id":"2506.15986","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-manipulation-attacks-web-agents-are","title":"Context manipulation attacks : Web agents are susceptible to corrupted memory","date":"2025-06-18","arxiv_id":"2506.17318","repositories_listed":0,"syntology":null},{"url":null,"slug":"formgym-doing-paperwork-with-agents","title":"FormGym: Doing Paperwork with Agents","date":"2025-06-17","arxiv_id":"2506.14079","repositories_listed":0,"syntology":null},{"url":null,"slug":"fretting-transformer-encoder-decoder-model","title":"Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription","date":"2025-06-17","arxiv_id":"2506.14223","repositories_listed":0,"syntology":null}],"record_sha256":"4576a8755953c41c002aa68581a956007957703462013e15550540b062d5d5a4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}