{"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/sentence/papers/104","list_of":"/task/sentence","task":"Sentence","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":104,"pages_in_order":108,"rows_per_page":100,"rows":[10301,10400],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":3811,"where_syntology_ran_a_sample":657,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":657,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":544,"every_run_a_failure_of_syntologys_instrument":113,"listed_with_a_run_with_no_instrument_failure":544,"listed_every_run_a_failure_of_syntologys_instrument":113,"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/sentence","prev":"/task/sentence/papers/103","next":"/task/sentence/papers/105","papers":[{"url":null,"slug":"robo-an-edit-distance-for-sentence-comparison","title":"ROBO, an edit distance for sentence comparison Application to automatic summarization","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-fine-tuning-for-word-embedding","title":"Supervised Fine Tuning for Word Embedding with Integrated Knowledge","date":"2015-05-29","arxiv_id":"1505.07931","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-category-theory-of-communication-theory","title":"A Category Theory of Communication Theory","date":"2015-05-28","arxiv_id":"1505.07712","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-frobenius-model-of-information-structure-in-1","title":"A Frobenius Model of Information Structure in Categorical Compositional Distributional Semantics","date":"2015-05-23","arxiv_id":"1505.06294","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-alignment-of-video-with","title":"Weakly-Supervised Alignment of Video With Text","date":"2015-05-22","arxiv_id":"1505.06027","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scale-multiple-instance-video","title":"A Multi-scale Multiple Instance Video Description Network","date":"2015-05-21","arxiv_id":"1505.05914","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-for-modern-standard-arabic","title":"Sentiment Analysis For Modern Standard Arabic And Colloquial","date":"2015-05-12","arxiv_id":"1505.03105","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-modeling-embedding-and-translation-to","title":"Jointly Modeling Embedding and Translation to Bridge Video and Language","date":"2015-05-07","arxiv_id":"1505.01861","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-for-image-captioning-the","title":"Language Models for Image Captioning: The Quirks and What Works","date":"2015-05-07","arxiv_id":"1505.01809","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-feature-based-classification-technique-for","title":"A Feature-based Classification Technique for Answering Multi-choice World History Questions","date":"2015-05-05","arxiv_id":"1505.00863","repositories_listed":0,"syntology":null},{"url":null,"slug":"interleaved-textimage-deep-mining-on-a-large","title":"Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation","date":"2015-05-04","arxiv_id":"1505.00670","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-update-strategies-for-large","title":"A Comparison of Update Strategies for Large-Scale Maximum Expected BLEU Training","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamic-programming-algorithm-for-tree","title":"A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linear-time-transition-system-for-crossing","title":"A Linear-Time Transition System for Crossing Interval Trees","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-sentences-from-standard-wikipedia-to","title":"Aligning Sentences from Standard Wikipedia to Simple Wikipedia","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"apro-all-pairs-ranking-optimization-for-mt","title":"APRO: All-Pairs Ranking Optimization for MT Tuning","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chain-based-rnn-for-relation-classification","title":"Chain Based RNN for Relation Classification","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-adaptation-to-user-feedback-for","title":"Continuous Adaptation to User Feedback for Statistical Machine Translation","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-optimization-in-crowdsourcing","title":"Cost Optimization in Crowdsourcing Translation: Low cost translations made even cheaper","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-sentence-generation-with-non","title":"Data-driven sentence generation with non-isomorphic trees","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogue-focus-tracking-for-zero-pronoun","title":"Dialogue focus tracking for zero pronoun resolution","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-unsupervised-alignment-of","title":"Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extractive-summarisation-based-on-keyword","title":"Extractive Summarisation Based on Keyword Profile and Language Model","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-preordering-for-smt-using","title":"Fast and Accurate Preordering for SMT using Neural Networks","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"good-news-or-bad-news-using-affect-control","title":"Good News or Bad News: Using Affect Control Theory to Analyze Readers' Reaction Towards News Articles","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grounded-semantic-parsing-for-complex","title":"Grounded Semantic Parsing for Complex Knowledge Extraction","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"i-can-has-cheezburger-a-nonparanormal","title":"I Can Has Cheezburger? A Nonparanormal Approach to Combining Textual and Visual Information for Predicting and Generating Popular Meme Descriptions","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-and-characterization-of","title":"Identification and Characterization of Newsworthy Verbs in World News","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-inference-of-implicit-discourse","title":"Improving the Inference of Implicit Discourse Relations via Classifying Explicit Discourse Connectives","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-update-summarization-via-supervised","title":"Improving Update Summarization via Supervised ILP and Sentence Reranking","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-temporally-anchored-spatial","title":"Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inflection-generation-as-discriminative","title":"Inflection Generation as Discriminative String Transduction","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-domain-word-alignment-for","title":"Latent Domain Word Alignment for Heterogeneous Corpora","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-interpret-and-describe-abstract","title":"Learning to Interpret and Describe Abstract Scenes","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-event-ordering-with-an-edge-factored","title":"Lexical Event Ordering with an Edge-Factored Model","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"model-invertibility-regularization-sequence","title":"Model Invertibility Regularization: Sequence Alignment With or Without Parallel Data","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mpqa-30-an-entityevent-level-sentiment-corpus","title":"MPQA 3.0: An Entity/Event-Level Sentiment Corpus","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-target-machine-translation-with-multi","title":"Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prosodic-boundary-information-helps","title":"Prosodic boundary information helps unsupervised word segmentation","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"removing-the-training-wheels-a-coreference","title":"Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"russian-chinese-sentence-level-aligned-news","title":"Russian-Chinese Sentence-level Aligned News Corpus","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-compression-for-automatic-subtitling","title":"Sentence Compression For Automatic Subtitling","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-segmentation-of-aphasic-speech","title":"Sentence segmentation of aphasic speech","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shared-common-ground-influences-information","title":"Shared common ground influences information density in microblog texts","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"subsentential-sentiment-on-a-shoestring-a","title":"Subsentential Sentiment on a Shoestring: A Crosslingual Analysis of Compositional Classification","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-geometry-of-statistical-machine","title":"The Geometry of Statistical Machine Translation","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-logic-of-amr-practical-unified-graph","title":"The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transition-based-syntactic-linearization","title":"Transition-Based Syntactic Linearization","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-code-switching-for-multilingual","title":"Unsupervised Code-Switching for Multilingual Historical Document Transcription","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-external-resources-and-joint-learning","title":"Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"why-read-if-you-can-scan-trigger-scoping","title":"Why Read if You Can Scan? Trigger Scoping Strategy for Biographical Fact Extraction","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-translation-model-using-a-deep-neural","title":"Lexical Translation Model Using a Deep Neural Network Architecture","date":"2015-04-28","arxiv_id":"1504.07395","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-mining-for-feature-based-opinion","title":"Review Mining for Feature Based Opinion Summarization and Visualization","date":"2015-04-13","arxiv_id":"1504.03068","repositories_listed":0,"syntology":null},{"url":"/paper/discriminative-neural-sentence-modeling-by","slug":"discriminative-neural-sentence-modeling-by","title":"Discriminative Neural Sentence Modeling by Tree-Based Convolution","date":"2015-04-05","arxiv_id":"1504.01106","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-using-machine-translation-techniques","title":"Towards Using Machine Translation Techniques to Induce Multilingual Lexica of Discourse Markers","date":"2015-03-31","arxiv_id":"1503.09144","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-open-relation-extraction-using","title":"Multilingual Open Relation Extraction Using Cross-lingual Projection","date":"2015-03-22","arxiv_id":"1503.06450","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-dependent-translation-selection-using","title":"Context-Dependent Translation Selection Using Convolutional Neural Network","date":"2015-03-09","arxiv_id":"1503.02357","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoding-source-language-with-convolutional","title":"Encoding Source Language with Convolutional Neural Network for Machine Translation","date":"2015-03-06","arxiv_id":"1503.01838","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-multi-sentence-lingual","title":"Generating Multi-Sentence Lingual Descriptions of Indoor Scenes","date":"2015-02-28","arxiv_id":"1503.00064","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-are-tree-structures-necessary-for-deep","title":"When Are Tree Structures Necessary for Deep Learning of Representations?","date":"2015-02-28","arxiv_id":"1503.00185","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-clustering-using-k-means-and-k","title":"Document Clustering using K-Means and K-Medoids","date":"2015-02-27","arxiv_id":"1502.07938","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-translation-prediction-with-global","title":"Local Translation Prediction with Global Sentence Representation","date":"2015-02-27","arxiv_id":"1502.07920","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sentence-embedding-using-long-short-term","title":"Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval","date":"2015-02-24","arxiv_id":"1502.06922","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-hybrid-metric-for-verifying-parallel","title":"A new hybrid metric for verifying parallel corpora of Arabic-English","date":"2015-02-12","arxiv_id":"1502.03752","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-aspect-category","title":"Representation Learning for Aspect Category Detection in Online Reviews","date":"2015-02-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inriasac-simple-hypernym-extraction-methods","title":"INRIASAC: Simple Hypernym Extraction Methods","date":"2015-02-04","arxiv_id":"1502.01271","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-system-categorical-quantum-semantics-in","title":"Open System Categorical Quantum Semantics in Natural Language Processing","date":"2015-02-03","arxiv_id":"1502.00831","repositories_listed":0,"syntology":null},{"url":null,"slug":"representing-objects-relations-and-sequences","title":"Representing Objects, Relations, and Sequences","date":"2015-01-29","arxiv_id":"1501.07627","repositories_listed":0,"syntology":null},{"url":null,"slug":"surveynatural-language-parsing-for-indian","title":"Survey:Natural Language Parsing For Indian Languages","date":"2015-01-28","arxiv_id":"1501.07005","repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-based-language-model-for-statistical","title":"Phrase Based Language Model for Statistical Machine Translation: Empirical Study","date":"2015-01-21","arxiv_id":"1501.05203","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-based-lattice-dependency-parser-for","title":"A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-compositional-semantics-for","title":"Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parsing-algebraic-word-problems-into","title":"Parsing Algebraic Word Problems into Equations","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-parsing-of-ambiguous-input-through","title":"Semantic Parsing of Ambiguous Input through Paraphrasing and Verification","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-origin-and-character-of-long","title":"Quantifying origin and character of long-range correlations in narrative texts","date":"2014-12-29","arxiv_id":"1412.8319","repositories_listed":0,"syntology":null},{"url":null,"slug":"altecondb-a-large-vocabulary-arabic-online","title":"AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database","date":"2014-12-24","arxiv_id":"1412.7626","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-augmented-distributional-semantics-for","title":"Entity-Augmented Distributional Semantics for Discourse Relations","date":"2014-12-17","arxiv_id":"1412.5673","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetric-weighted-first-order-model-counting","title":"Symmetric Weighted First-Order Model Counting","date":"2014-12-03","arxiv_id":"1412.1505","repositories_listed":0,"syntology":null},{"url":null,"slug":"extraction-of-pharmacokinetic-evidence-of","title":"Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature","date":"2014-12-02","arxiv_id":"1412.0744","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-keyword-based-monolingual-sentence-aligner","title":"A Keyword-based Monolingual Sentence Aligner in Text Simplification","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-probabilistic-framework-for-multimodal","title":"A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"global-belief-recursive-neural-networks","title":"Global Belief Recursive Neural Networks","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-know-the-best-machine-translation","title":"How to Know the Best Machine Translation System in Advance before Translating a Sentence?","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-karaka-relations-in-an","title":"Identification of Karaka relations in an English sentence","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-net-based-method-for-determining","title":"Word net based Method for Determining Semantic Sentence Similarity through various Word Senses","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-sentence-plausibility-to-learn-the","title":"Using Sentence Plausibility to Learn the Semantics of Transitive Verbs","date":"2014-11-28","arxiv_id":"1411.7942","repositories_listed":0,"syntology":null},{"url":null,"slug":"fisher-vectors-derived-from-hybrid-gaussian","title":"Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation","date":"2014-11-26","arxiv_id":"1411.7399","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-joint-probabilistic-classification-model-of","title":"A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems","date":"2014-11-21","arxiv_id":"1411.5732","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-recurrent-visual-representation","title":"Learning a Recurrent Visual Representation for Image Caption Generation","date":"2014-11-20","arxiv_id":"1411.5654","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-faster-method-for-tracking-and-scoring","title":"A Faster Method for Tracking and Scoring Videos Corresponding to Sentences","date":"2014-11-14","arxiv_id":"1411.4064","repositories_listed":0,"syntology":null},{"url":null,"slug":"collecting-image-description-datasets-using","title":"Collecting Image Description Datasets using Crowdsourcing","date":"2014-11-12","arxiv_id":"1411.3041","repositories_listed":0,"syntology":null},{"url":null,"slug":"submodular-meets-structured-finding-diverse","title":"Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets","date":"2014-11-06","arxiv_id":"1411.1752","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-learning-model-for-parsing-arabic","title":"Supervised learning model for parsing Arabic language","date":"2014-10-31","arxiv_id":"1410.8783","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stronger-null-hypothesis-for-crossing","title":"A stronger null hypothesis for crossing dependencies","date":"2014-10-20","arxiv_id":"1410.5485","repositories_listed":0,"syntology":null},{"url":null,"slug":"explain-images-with-multimodal-recurrent","title":"Explain Images with Multimodal Recurrent Neural Networks","date":"2014-10-04","arxiv_id":"1410.1090","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-model-of-coherence-based-on-distributed","title":"A Model of Coherence Based on Distributed Sentence Representation","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-sentence-alignment-of-a-parallel","title":"Bilingual Sentence Alignment of a Parallel Corpus by Using English as a Pivot Language","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-chinese-sentence-polarity","title":"Improving Chinese Sentence Polarity Classification via Opinion Paraphrasing","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multi-documents-summarization-by","title":"Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-novel-sentence-modeling","title":"探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"358527c063fdfba0433507a024f9c29a63083fd5e9e38c7bf96441b939a26276","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}