{"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/word-embeddings/papers/27","list_of":"/task/word-embeddings","task":"Word Embeddings","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":27,"pages_in_order":41,"rows_per_page":100,"rows":[2601,2700],"of":4002,"counts":{"archive_papers_tagged":4002,"with_a_code_link":1177,"where_syntology_ran_a_sample":155,"not_listed_spam_title":0,"listed":4002,"listed_where_code_ran":155,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":122,"every_run_a_failure_of_syntologys_instrument":33,"listed_with_a_run_with_no_instrument_failure":122,"listed_every_run_a_failure_of_syntologys_instrument":33,"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/word-embeddings","prev":"/task/word-embeddings/papers/26","next":"/task/word-embeddings/papers/28","papers":[{"url":null,"slug":"word-embedding-based-automatic-mt-evaluation","title":"Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"190600112","title":"Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words","date":"2019-05-31","arxiv_id":"1906.00112","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600114","title":"Examining Structure of Word Embeddings with PCA","date":"2019-05-31","arxiv_id":"1906.00114","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pretrained-word-embeddings-for","title":"Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data","date":"2019-05-31","arxiv_id":"1905.13359","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-adversarial-training-for-text","title":"Interpretable Adversarial Training for Text","date":"2019-05-30","arxiv_id":"1905.12864","repositories_listed":0,"syntology":null},{"url":null,"slug":"threshold-based-retrieval-and-textual","title":"Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions","date":"2019-05-30","arxiv_id":"1905.13350","repositories_listed":0,"syntology":null},{"url":null,"slug":"190601496","title":"Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains","date":"2019-05-29","arxiv_id":"1906.01496","repositories_listed":0,"syntology":null},{"url":null,"slug":"attack2vec-leveraging-temporal-word","title":"ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks","date":"2019-05-29","arxiv_id":"1905.12590","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multilingual-word-embeddings-using","title":"Learning Multilingual Word Embeddings Using Image-Text Data","date":"2019-05-29","arxiv_id":"1905.12260","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-post-processing-methods","title":"An Empirical Study on Post-processing Methods for Word Embeddings","date":"2019-05-27","arxiv_id":"1905.10971","repositories_listed":0,"syntology":null},{"url":null,"slug":"190511796","title":"Self-supervised audio representation learning for mobile devices","date":"2019-05-24","arxiv_id":"1905.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiasingword-embeddings-improves-multimodal","title":"Debiasing Word Embeddings Improves Multimodal Machine Translation","date":"2019-05-24","arxiv_id":"1905.10464","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-assembly-sparse-imitation-learning-for","title":"Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces","date":"2019-05-23","arxiv_id":"1905.09700","repositories_listed":0,"syntology":null},{"url":null,"slug":"gwu-nlp-lab-at-semeval-2019-task-3-emocontext","title":"GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus","date":"2019-05-23","arxiv_id":"1905.09439","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieving-multi-entity-associations-an","title":"Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings","date":"2019-05-22","arxiv_id":"1905.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-part-of-speech-tagging","title":"Domain adaptation for part-of-speech tagging of noisy user-generated text","date":"2019-05-21","arxiv_id":"1905.08920","repositories_listed":0,"syntology":null},{"url":null,"slug":"supertml-domain-transfer-from-computer-vision","title":"SuperTML: Domain Transfer from Computer Vision to Structured Tabular Data through Two-Dimensional Word Embedding","date":"2019-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/syntax-enhanced-neural-machine-translation","slug":"syntax-enhanced-neural-machine-translation","title":"Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations","date":"2019-05-08","arxiv_id":"1905.02878","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-typedriven-vector-semantics-for-ellipsis","title":"A Typedriven Vector Semantics for Ellipsis with Anaphora using Lambek Calculus with Limited Contraction","date":"2019-05-05","arxiv_id":"1905.01647","repositories_listed":0,"syntology":null},{"url":null,"slug":"models-in-the-wild-on-corruption-robustness","title":"Models in the Wild: On Corruption Robustness of NLP Systems","date":"2019-05-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-transformers-for-simple-question-1","title":"Pretrained Transformers for Simple Question Answering","date":"2019-05-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-context-sensitive-models-for","title":"A Comparison of Context-sensitive Models for Lexical Substitution","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-open-ie-relations-and-kb-relations","title":"Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-paraphrases-of-standard-clause","title":"Detecting Paraphrases of Standard Clause Titles in Insurance Contracts","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-is-not-enough-going-firther","title":"Distribution is not enough: going Firther","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-stability-of-concrete-nouns","title":"Investigating the Stability of Concrete Nouns in Word Embeddings","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-agnostic-model-for-aspect-based","title":"Language-Agnostic Model for Aspect-Based Sentiment Analysis","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-mixed-curvature-representations-in","title":"Learning Mixed-Curvature Representations in Product Spaces","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-learning-word-embeddings-from","title":"On Learning Word Embeddings From Linguistically Augmented Text Corpora","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-word-concreteness-and-imagery","title":"Predicting Word Concreteness and Imagery","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relwalk-a-latent-variable-model-approach-to","title":"RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-frame-embeddings-for-detecting","title":"Semantic Frame Embeddings for Detecting Relations between Software Requirements","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effectiveness-of-pre-trained-code","title":"The Effectiveness of Pre-Trained Code Embeddings","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-hyper-alignment-for-multilingual","title":"Unsupervised Hyper-alignment for Multilingual Word Embeddings","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-barycenter-model-ensembling","title":"Wasserstein Barycenter Model Ensembling","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"words-are-vectors-dependencies-are-matrices","title":"Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-open-world-specification-mining-via","title":"Enabling Open-World Specification Mining via Unsupervised Learning","date":"2019-04-27","arxiv_id":"1904.12098","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-word-embeddings-enhanced-event","title":"Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding","date":"2019-04-26","arxiv_id":"1904.11942","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bag-of-concepts-model-improves-relation","title":"A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data","date":"2019-04-24","arxiv_id":"1904.10743","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-automatic-evaluation-of-open-domain","title":"Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings","date":"2019-04-24","arxiv_id":"1904.10635","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-social-media-into-a-pan-european","title":"Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach","date":"2019-04-24","arxiv_id":"1904.10876","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-stability-of-medical-concept","title":"Understanding the Stability of Medical Concept Embeddings","date":"2019-04-21","arxiv_id":"1904.09552","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-concept-based-adversarial","title":"Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings","date":"2019-04-20","arxiv_id":"1904.09446","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-cross-lingual-opinion-target","title":"Zero-Shot Cross-Lingual Opinion Target Extraction","date":"2019-04-19","arxiv_id":"1904.09122","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-underlying-gender-bias-in","title":"Evaluating the Underlying Gender Bias in Contextualized Word Embeddings","date":"2019-04-18","arxiv_id":"1904.08783","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-aware-joint-probability-model","title":"Contextual Aware Joint Probability Model Towards Question Answering System","date":"2019-04-17","arxiv_id":"1904.08109","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-expansion-for-cross-language-question","title":"Query Expansion for Cross-Language Question Re-Ranking","date":"2019-04-16","arxiv_id":"1904.07982","repositories_listed":0,"syntology":null},{"url":null,"slug":"190501958","title":"Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy","date":"2019-04-11","arxiv_id":"1905.01958","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-cybersecurity-events-from-noisy","title":"Detecting Cybersecurity Events from Noisy Short Text","date":"2019-04-10","arxiv_id":"1904.05054","repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-in-a-name-reducing-bias-in-bios-without","title":"What's in a Name? Reducing Bias in Bios without Access to Protected Attributes","date":"2019-04-10","arxiv_id":"1904.05233","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixing-syntagmatic-and-paradigmatic","title":"Mixing syntagmatic and paradigmatic information for concept detection","date":"2019-04-09","arxiv_id":"1904.04461","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-greek-word-embeddings","title":"Evaluation of Greek Word Embeddings","date":"2019-04-08","arxiv_id":"1904.04032","repositories_listed":0,"syntology":null},{"url":null,"slug":"thisiscompetition-at-semeval-2019-task-9-bert","title":"ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples","date":"2019-04-06","arxiv_id":"1904.03339","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-context-and-fragment-feature-usage","title":"Effective Context and Fragment Feature Usage for Named Entity Recognition","date":"2019-04-05","arxiv_id":"1904.03305","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fine-tuned-embeddings-that-model","title":"Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis","date":"2019-04-05","arxiv_id":"1904.03164","repositories_listed":0,"syntology":null},{"url":null,"slug":"190412587","title":"Text Classification Components for Detecting Descriptions and Names of CAD models","date":"2019-04-04","arxiv_id":"1904.12587","repositories_listed":0,"syntology":null},{"url":null,"slug":"rewe-regressing-word-embeddings-for","title":"ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems","date":"2019-04-04","arxiv_id":"1904.02461","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-kgr10-polish-word-embeddings-in","title":"Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF","date":"2019-04-03","arxiv_id":"1904.04055","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptation-of-hierarchical-structured-models","title":"Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation","date":"2019-04-01","arxiv_id":"1904.04021","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-abbreviation-disambiguation","title":"Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings","date":"2019-04-01","arxiv_id":"1904.00929","repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustically-grounded-word-embeddings-for","title":"Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition","date":"2019-03-29","arxiv_id":"1903.12306","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-word-embeddings-for-tweet","title":"Deep Learning and Word Embeddings for Tweet Classification for Crisis Response","date":"2019-03-26","arxiv_id":"1903.11024","repositories_listed":0,"syntology":null},{"url":null,"slug":"expanding-the-text-classification-toolbox","title":"Expanding the Text Classification Toolbox with Cross-Lingual Embeddings","date":"2019-03-23","arxiv_id":"1903.09878","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-entity-representations-for-few-shot","title":"Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories","date":"2019-03-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-neural-embeddings-for","title":"Personalized Neural Embeddings for Collaborative Filtering with Text","date":"2019-03-19","arxiv_id":"1903.07860","repositories_listed":0,"syntology":null},{"url":null,"slug":"creation-and-evaluation-of-datasets-for","title":"Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain","date":"2019-03-07","arxiv_id":"1903.02671","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-extraction-datasets-in-the-digital","title":"Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings","date":"2019-03-04","arxiv_id":"1903.01284","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-embeddings-for-visual-data","title":"Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata","date":"2019-03-04","arxiv_id":"1903.01275","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-contextual-representation-learning","title":"Efficient Contextual Representation Learning Without Softmax Layer","date":"2019-02-28","arxiv_id":"1902.11269","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-decoding-event-related","title":"A Framework for Decoding Event-Related Potentials from Text","date":"2019-02-27","arxiv_id":"1902.10296","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-vectors-are-reflections-of-word","title":"Context Vectors are Reflections of Word Vectors in Half the Dimensions","date":"2019-02-26","arxiv_id":"1902.09859","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-tree-lstm-structure-aware","title":"Interpretable Structure-aware Document Encoders with Hierarchical Attention","date":"2019-02-26","arxiv_id":"1902.09713","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sentiment-analysis-using-a-graph-based","title":"Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications","date":"2019-02-23","arxiv_id":"1902.10247","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-clinical-concept-extraction-with","slug":"enhancing-clinical-concept-extraction-with","title":"Enhancing Clinical Concept Extraction with Contextual Embeddings","date":"2019-02-22","arxiv_id":"1902.08691","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-in-speech-recognition-contextual","title":"Learned In Speech Recognition: Contextual Acoustic Word Embeddings","date":"2019-02-18","arxiv_id":"1902.06833","repositories_listed":0,"syntology":null},{"url":null,"slug":"combination-of-domain-knowledge-and-deep","title":"Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media","date":"2019-02-16","arxiv_id":"1902.06050","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-for-idiolect-identification","title":"Word embeddings for idiolect identification","date":"2019-02-10","arxiv_id":"1902.03658","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-resolution-word-embedding-for","title":"A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases","date":"2019-02-02","arxiv_id":"1902.00663","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-for-sentiment-analysis-a","title":"Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey","date":"2019-02-02","arxiv_id":"1902.00753","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-regularization-based-algorithm-for","title":"A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings","date":"2019-02-01","arxiv_id":"1902.00184","repositories_listed":0,"syntology":null},{"url":null,"slug":"decomposing-generalization-models-of-generic","title":"Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements","date":"2019-01-31","arxiv_id":"1901.11429","repositories_listed":0,"syntology":null},{"url":null,"slug":"analogies-explained-towards-understanding","title":"Analogies Explained: Towards Understanding Word Embeddings","date":"2019-01-28","arxiv_id":"1901.09813","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-word-embedding-models-methods-and","title":"Evaluating Word Embedding Models: Methods and Experimental Results","date":"2019-01-28","arxiv_id":"1901.09785","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-a-survey","title":"Word Embeddings: A Survey","date":"2019-01-25","arxiv_id":"1901.09069","repositories_listed":0,"syntology":null},{"url":null,"slug":"morty-embedding-improved-embeddings-without","title":"MORTY Embedding: Improved Embeddings without Supervision","date":"2019-01-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-sensitive-malicious-spelling-error","title":"Context-Sensitive Malicious Spelling Error Correction","date":"2019-01-23","arxiv_id":"1901.07688","repositories_listed":0,"syntology":null},{"url":null,"slug":"deconstructing-word-embeddings","title":"Deconstructing Word Embeddings","date":"2019-01-08","arxiv_id":"1902.00551","repositories_listed":0,"syntology":null},{"url":null,"slug":"team-ep-at-tac-2018-automating-data","title":"Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents","date":"2019-01-07","arxiv_id":"1901.02081","repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-representations-of-text-data-in-deep","title":"Vector representations of text data in deep learning","date":"2019-01-07","arxiv_id":"1901.01695","repositories_listed":0,"syntology":null},{"url":null,"slug":"jabberwocky-parsing-dependency-parsing-with","title":"Jabberwocky Parsing: Dependency Parsing with Lexical Noise","date":"2019-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multilingual-topics-through-aspect","title":"Learning multilingual topics through aspect extraction from monolingual texts","date":"2019-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-neural-machine-translation","title":"Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input","date":"2018-12-23","arxiv_id":"1812.09664","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-topic-modeling-for-short-texts-with","title":"Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings","date":"2018-12-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-does-tokenization-affect-neural","title":"How Much Does Tokenization Affect Neural Machine Translation?","date":"2018-12-20","arxiv_id":"1812.08621","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-are-the-biases-in-my-word-embedding","title":"What are the biases in my word embedding?","date":"2018-12-20","arxiv_id":"1812.08769","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unbiased-approach-to-quantification-of","title":"Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence","date":"2018-12-13","arxiv_id":"1812.10424","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-weak-and-strong-islamophobic-hate","title":"Detecting weak and strong Islamophobic hate speech on social media","date":"2018-12-12","arxiv_id":"1812.10400","repositories_listed":0,"syntology":null},{"url":null,"slug":"delta-embedding-learning","title":"Delta Embedding Learning","date":"2018-12-11","arxiv_id":"1812.04160","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-agnostic-identification","title":"Unsupervised domain-agnostic identification of product names in social media posts","date":"2018-12-11","arxiv_id":"1812.04662","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-classification-of-speech-overlaps","title":"Automatic classification of speech overlaps: Feature representation and algorithms","date":"2018-12-02","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"e23a7a3592923550ea83059eecfb95752b903eef4816151fe07feaf32e45079b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}