{"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/question-answering/papers/103","list_of":"/task/question-answering","task":"Question Answering","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":103,"pages_in_order":109,"rows_per_page":100,"rows":[10201,10300],"of":10817,"counts":{"archive_papers_tagged":10817,"with_a_code_link":4171,"where_syntology_ran_a_sample":1274,"not_listed_spam_title":0,"listed":10817,"listed_where_code_ran":1274,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1073,"every_run_a_failure_of_syntologys_instrument":201,"listed_with_a_run_with_no_instrument_failure":1073,"listed_every_run_a_failure_of_syntologys_instrument":201,"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/question-answering","prev":"/task/question-answering/papers/102","next":"/task/question-answering/papers/104","papers":[{"url":null,"slug":"semeval-2015-task-17-taxonomy-extraction","title":"SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2015-task-2-semantic-textual","title":"SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2015-task-5-qa-tempeval-evaluating","title":"SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shiraz-a-proposed-list-wise-approach-to","title":"Shiraz: A Proposed List Wise Approach to Answer Validation","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"taln-upf-taxonomy-learning-exploiting-crf","title":"TALN-UPF: Taxonomy Learning Exploiting CRF-Based Hypernym Extraction on Encyclopedic Definitions","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tato-leveraging-on-multiple-strategies-for","title":"TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-better-semantic-role-labeling-of","title":"Towards a Better Semantic Role Labeling of Complex Predicates","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twitter-paraphrase-identification-with-simple","title":"Twitter Paraphrase Identification with Simple Overlap Features and SVMs","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vectorslu-a-continuous-word-vector-approach","title":"VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"viske-visual-knowledge-extraction-and","title":"VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"voltron-a-hybrid-system-for-answer-validation","title":"Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"yigou-a-semantic-text-similarity-computing","title":"yiGou: A Semantic Text Similarity Computing System Based on SVM","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/visual-madlibs-fill-in-the-blank-image","slug":"visual-madlibs-fill-in-the-blank-image","title":"Visual Madlibs: Fill in the blank Image Generation and Question Answering","date":"2015-05-31","arxiv_id":"1506.00278","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-sms-based-information-systems","title":"A survey of SMS based Information Systems","date":"2015-05-22","arxiv_id":"1505.06537","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-your-neurons-a-neural-based-approach-to","title":"Ask Your Neurons: A Neural-based Approach to Answering Questions about Images","date":"2015-05-05","arxiv_id":"1505.01121","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":"deep-learning-and-continuous-representations","title":"Deep Learning and Continuous Representations for Natural Language Processing","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":"idest-learning-a-distributed-representation","title":"Idest: Learning a Distributed Representation for Event Patterns","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-knowledge-graphs-for-question","title":"Learning Knowledge Graphs for Question Answering through Conversational Dialog","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistically-motivated-question","title":"Linguistically Motivated Question Classification","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"movie-script-summarization-as-graph-based","title":"Movie Script Summarization as Graph-based Scene Extraction","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":"ontologically-grounded-multi-sense","title":"Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spinning-straw-into-gold-using-free-text-to","title":"Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supersense-tagging-for-danish","title":"Supersense tagging for Danish","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-gathering-in-networks-via-active","title":"Information Gathering in Networks via Active Exploration","date":"2015-04-24","arxiv_id":"1504.06423","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-temporal-expressions-annotated-in","title":"Analysis of Temporal Expressions Annotated in Clinical Notes","date":"2015-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-noun-compound-interpretation-using","title":"Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings","date":"2015-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-relations-in-discourse-the-current","title":"Semantic Relations in Discourse: The Current State of ISO 24617-8","date":"2015-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ratiolog-project-rational-extensions-of","title":"The RatioLog Project: Rational Extensions of Logical Reasoning","date":"2015-03-20","arxiv_id":"1503.06087","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-case-based-reasoning-approach-for-answer","title":"A Case Based Reasoning Approach for Answer Reranking in Question Answering","date":"2015-03-10","arxiv_id":"1503.02917","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-based-deep-matching-of-short-texts","title":"Syntax-based Deep Matching of Short Texts","date":"2015-03-09","arxiv_id":"1503.02427","repositories_listed":0,"syntology":null},{"url":null,"slug":"book-reviews-recognizing-textual-entailment","title":"Book Reviews: Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio Massimo Zanzotto","date":"2015-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-topic-to-question-generation","title":"Towards Topic-to-Question Generation","date":"2015-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hard-to-cheat-a-turing-test-based-on","title":"Hard to Cheat: A Turing Test based on Answering Questions about Images","date":"2015-01-14","arxiv_id":"1501.03302","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-document-co-reference-resolution-using","title":"Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-parallel-news-streams-for","title":"Exploiting Parallel News Streams for Unsupervised Event Extraction","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-lexical-semantic-models-for-non","title":"Higher-order Lexical Semantic Models for Non-factoid Answer Reranking","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":"one-vector-is-not-enough-entity-augmented","title":"One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qanus-an-open-source-question-answering","title":"QANUS: An Open-source Question-Answering Platform","date":"2015-01-01","arxiv_id":"1501.00311","repositories_listed":0,"syntology":null},{"url":null,"slug":"which-step-do-i-take-first-troubleshooting","title":"Which Step Do I Take First? Troubleshooting with Bayesian Models","date":"2015-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ripple-down-rules-for-question-answering","title":"Ripple Down Rules for Question Answering","date":"2014-12-12","arxiv_id":"1412.4160","repositories_listed":0,"syntology":null},{"url":null,"slug":"practice-in-synonym-extraction-at-large-scale","title":"Practice in Synonym Extraction at Large Scale","date":"2014-12-06","arxiv_id":"1412.2197","repositories_listed":0,"syntology":null},{"url":null,"slug":"watsonsim-overview-of-a-question-answering","title":"Watsonsim: Overview of a Question Answering Engine","date":"2014-12-02","arxiv_id":"1412.0879","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-identification-of-the-karta-subject","title":"Accurate Identification of the Karta (Subject) Relation in Bangla","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-purposenet-ontology-an-insight","title":"Creating a PurposeNet Ontology: An insight into the issues encountered during ontology creation","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-trees-for-coreference-resolution","title":"Latent Trees for Coreference Resolution","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lmsim-computing-domain-specific-semantic-word","title":"LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-based-answer-extraction-form","title":"Named Entity Based Answer Extraction form Hindi Text Corpus Using n-grams","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentential-paraphrase-generation-for","title":"Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-systems-and-question-answering","title":"Cognitive Systems and Question Answering","date":"2014-11-18","arxiv_id":"1411.4825","repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-adaptation-of-pos-tagging-for-domain","title":"Rapid Adaptation of POS Tagging for Domain Specific Uses","date":"2014-10-31","arxiv_id":"1411.0007","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-visual-turing-challenge","title":"Towards a Visual Turing Challenge","date":"2014-10-29","arxiv_id":"1410.8027","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-ccg-parsing-with-a-supertag-factored-model","title":"A* CCG Parsing with a Supertag-factored Model","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-the-classification-and","title":"A Framework for the Classification and Annotation of Multiword Expressions in Dialectal Arabic","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/a-multi-world-approach-to-question-answering","slug":"a-multi-world-approach-to-question-answering","title":"A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input","date":"2014-10-01","arxiv_id":"1410.0210","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-for-factoid-question","title":"A Neural Network for Factoid Question Answering over Paragraphs","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-regularized-competition-model-for-question","title":"A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"al-bayan-an-arabic-question-answering-system","title":"Al-Bayan: An Arabic Question Answering System for the Holy Quran","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ctps-contextual-temporal-profiles-for-time","title":"CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-parsing-for-weibo-an-efficient","title":"Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-timegraphs-in-temporal-relation","title":"Exploiting Timegraphs in Temporal Relation Classification","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fear-the-reaper-a-system-for-automatic-multi","title":"Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"invited-talk-2-towards-universal-syntactic","title":"INVITED TALK 2: Towards Universal Syntactic Processing of Natural Language","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"invited-talk-ibm-cognitive-computing-an-nlp","title":"Invited Talk: IBM Cognitive Computing - An NLP Renaissance!","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-relational-embeddings-for-knowledge","title":"Joint Relational Embeddings for Knowledge-based Question Answering","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-and-corpus-driven","title":"Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-compact-lexicons-for-ccg-semantic","title":"Learning Compact Lexicons for CCG Semantic Parsing","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-a-neighbor-adapting-a-japanese","title":"Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-substitution-for-the-medical-domain","title":"Lexical Substitution for the Medical Domain","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"morpho-syntactic-lexical-generalization-for","title":"Morpho-syntactic Lexical Generalization for CCG Semantic Parsing","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-system-for-dialectal","title":"Named Entity Recognition System for Dialectal Arabic","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opinion-mining-with-deep-recurrent-neural","title":"Opinion Mining with Deep Recurrent Neural Networks","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-over-linked-data-using","title":"Question Answering over Linked Data Using First-order Logic","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"taxonomy-construction-using-syntactic","title":"Taxonomy Construction Using Syntactic Contextual Evidence","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-template-mining-for-semantic","title":"Unsupervised Template Mining for Semantic Category Understanding","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"werdy-recognition-and-disambiguation-of-verbs","title":"Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-association-measures-and-their","title":"A Study of Association Measures and their Combination for Arabic MWT Extraction","date":"2014-09-10","arxiv_id":"1409.3005","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-component-combination-in-a-multi","title":"Optimizing Component Combination in a Multi-Indexing Paragraph Retrieval System","date":"2014-08-11","arxiv_id":"1408.2430","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-conversion-aided","title":"A Comparative Study of Conversion Aided Methods for WordNet Sentence Textual Similarity","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-to-features-representation","title":"A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-three-step-transition-based-system-for-non","title":"A Three-Step Transition-Based System for Non-Projective Dependency Parsing","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-ku-using-co-occurrence-modeling-for","title":"AI-KU: Using Co-Occurrence Modeling for Semantic Similarity","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-causality-between-events-and","title":"An Analysis of Causality between Events and its Relation to Temporal Information","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"annotate-and-identify-modalities-speech-acts","title":"Annotate and Identify Modalities, Speech Acts and Finer-Grained Event Types in Chinese Text","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-structure-of-adverbial-derivatives","title":"Argument structure of adverbial derivatives in Russian","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-compound-processing-compound","title":"Automatic Compound Processing: Compound Splitting and Semantic Analysis for Afrikaans and Dutch","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedicalclinical-nlp","title":"Biomedical/Clinical NLP","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-english-vietnamese-named-entity","title":"Building English-Vietnamese Named Entity Corpus with Aligned Bilingual News Articles","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cmu-arc-factored-discriminative-semantic","title":"CMU: Arc-Factored, Discriminative Semantic Dependency Parsing","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-distributional-semantics-models","title":"Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"converting-phrase-structures-to-dependency","title":"Converting Phrase Structures to Dependency Structures in Sanskrit","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-parsing-past-present-and-future","title":"Dependency Parsing: Past, Present, and Future","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dlscu-sentence-similarity-from-word-alignment-1","title":"DLS@CU: Sentence Similarity from Word Alignment","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-with-easy-first-nonprojective","title":"Experiments with Easy-first nonprojective constituent parsing","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-the-human-computational-effort","title":"Exploiting the Human Computational Effort Dedicated to Message Reply Formatting for Training Discursive Email Segmenters","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fine-grained-entity-type","title":"Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-supplementary-travel-guides-from","title":"Generating Supplementary Travel Guides from Social Media","date":"2014-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"ab9145445a801c793abcd594128213b863720bf23253dae8c735ec079e6a32af","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}