{"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/14","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":14,"pages_in_order":41,"rows_per_page":100,"rows":[1301,1400],"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/13","next":"/task/word-embeddings/papers/15","papers":[{"url":null,"slug":"gwpt-a-green-word-embedding-based-pos-tagger","title":"GWPT: A Green Word-Embedding-based POS Tagger","date":"2024-01-15","arxiv_id":"2401.07475","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-to-promote-translational","title":"Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research","date":"2024-01-10","arxiv_id":"2401.05145","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-text-similarity-based-on-semantic","title":"Estimating Text Similarity based on Semantic Concept Embeddings","date":"2024-01-09","arxiv_id":"2401.04422","repositories_listed":0,"syntology":null},{"url":null,"slug":"mosecrot-model-stitching-with-static-word","title":"MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer","date":"2024-01-09","arxiv_id":"2401.04821","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-vision-language-models-on-solid","title":"Building Vision-Language Models on Solid Foundations with Masked Distillation","date":"2024-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-embedding-layers-and","title":"An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks","date":"2023-12-31","arxiv_id":"2401.00582","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-of-dimensionality-change-on-the-bias","title":"Effect of dimensionality change on the bias of word embeddings","date":"2023-12-28","arxiv_id":"2312.17292","repositories_listed":0,"syntology":null},{"url":null,"slug":"zur-darstellung-eines-mehrstufigen","title":"Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch","date":"2023-12-26","arxiv_id":"2312.16311","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-exr-controllable-review-generation","title":"Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models","date":"2023-12-24","arxiv_id":"2312.15490","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-biomedical-ner-through-multi","title":"Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling","date":"2023-12-24","arxiv_id":"2312.15550","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-cognitive-maps-based-on-neural","title":"Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations","date":"2023-12-22","arxiv_id":"2401.01364","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-continuous-and-discrete","title":"Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings","date":"2023-12-18","arxiv_id":"2312.11272","repositories_listed":0,"syntology":null},{"url":null,"slug":"well-calibrated-confidence-measures-for-multi","title":"Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels","date":"2023-12-14","arxiv_id":"2312.09304","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for-diagnosis-and","title":"Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease","date":"2023-12-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"where-exactly-does-contextualization-in-a-plm","title":"Where exactly does contextualization in a PLM happen?","date":"2023-12-11","arxiv_id":"2312.06514","repositories_listed":0,"syntology":null},{"url":null,"slug":"constructing-vec-tionaries-to-extract-latent","title":"Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals","date":"2023-12-10","arxiv_id":"2312.05990","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-approach-to-evaluate-sentence","title":"Unsupervised Approach to Evaluate Sentence-Level Fluency: Do We Really Need Reference?","date":"2023-12-03","arxiv_id":"2312.01500","repositories_listed":0,"syntology":null},{"url":null,"slug":"lego-learning-to-disentangle-and-invert","title":"Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models","date":"2023-11-23","arxiv_id":"2311.13833","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-word-embeddings-for-low-resource","title":"Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages","date":"2023-11-21","arxiv_id":"2311.12489","repositories_listed":0,"syntology":null},{"url":null,"slug":"bit-cipher-a-simple-yet-powerful-word","title":"Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models","date":"2023-11-18","arxiv_id":"2311.11012","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-fusion-of-signals-in-data","title":"Compositional Fusion of Signals in Data Embedding","date":"2023-11-18","arxiv_id":"2311.11085","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-definitions-from-large-language-models","title":"Word Definitions from Large Language Models","date":"2023-11-10","arxiv_id":"2311.06362","repositories_listed":0,"syntology":null},{"url":null,"slug":"matnexus-a-comprehensive-text-mining-and","title":"MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover","date":"2023-11-07","arxiv_id":"2311.06303","repositories_listed":0,"syntology":null},{"url":"/paper/explainable-identification-of-hate-speech","slug":"explainable-identification-of-hate-speech","title":"Explainable Identification of Hate Speech towards Islam using Graph Neural Networks","date":"2023-11-02","arxiv_id":"2311.04916","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-framework-for-understanding","title":"Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms","date":"2023-10-30","arxiv_id":"2310.20061","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-not-harm-protected-groups-in-debiasing","title":"Do Not Harm Protected Groups in Debiasing Language Representation Models","date":"2023-10-27","arxiv_id":"2310.18458","repositories_listed":0,"syntology":null},{"url":null,"slug":"analogical-proportions-and-creativity-a","title":"Analogical Proportions and Creativity: A Preliminary Study","date":"2023-10-20","arxiv_id":"2310.13500","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-deep-learning-framework-for","title":"An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records","date":"2023-10-16","arxiv_id":"2310.10187","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-interpretability-using-human","title":"Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings","date":"2023-10-16","arxiv_id":"2310.10262","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-down-word-semantics-from-pre-trained","title":"Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection","date":"2023-10-08","arxiv_id":"2310.05115","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-process-for-topic-modelling-via-word","title":"A Process for Topic Modelling Via Word Embeddings","date":"2023-10-06","arxiv_id":"2312.03705","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-unseen-multiword-expressions-in","title":"Detecting Unseen Multiword Expressions in American Sign Language","date":"2023-09-30","arxiv_id":"2310.00207","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-embeddings-for-measuring-text","title":"Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments","date":"2023-09-15","arxiv_id":"2310.05964","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pretrained-image-text-models-for","title":"Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning","date":"2023-09-08","arxiv_id":"2309.04628","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-approaches-to-spoken-content-embedding","title":"Neural approaches to spoken content embedding","date":"2023-08-28","arxiv_id":"2308.14905","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnt-contrastive-concept-embeddings-for","title":"Learnt Contrastive Concept Embeddings for Sign Recognition","date":"2023-08-18","arxiv_id":"2308.09515","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-preliminary-study-on-a-conceptual-game","title":"A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System","date":"2023-08-16","arxiv_id":"2308.13538","repositories_listed":0,"syntology":null},{"url":null,"slug":"gloss-alignment-using-word-embeddings","title":"Gloss Alignment Using Word Embeddings","date":"2023-08-08","arxiv_id":"2308.04248","repositories_listed":0,"syntology":null},{"url":null,"slug":"vocab-expander-a-system-for-creating-domain","title":"Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings","date":"2023-08-07","arxiv_id":"2308.03519","repositories_listed":0,"syntology":null},{"url":null,"slug":"lessons-in-reproducibility-insights-from-nlp","title":"Lessons in Reproducibility: Insights from NLP Studies in Materials Science","date":"2023-07-28","arxiv_id":"2307.15759","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-flow-of-ideas-in-word-embeddings","title":"The flow of ideas in word embeddings","date":"2023-07-26","arxiv_id":"2307.16819","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-resolving-word-ambiguity-with-word","title":"Towards Resolving Word Ambiguity with Word Embeddings","date":"2023-07-25","arxiv_id":"2307.13417","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-acoustic-word-embedding","title":"Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder","date":"2023-07-19","arxiv_id":"2307.09871","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-topical-approach-to-capturing-customer","title":"A Topical Approach to Capturing Customer Insight In Social Media","date":"2023-07-14","arxiv_id":"2307.11775","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-sentiment-2","title":"Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach","date":"2023-07-13","arxiv_id":"2307.06540","repositories_listed":0,"syntology":null},{"url":null,"slug":"undecimated-wavelet-transform-for-word","title":"Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages","date":"2023-07-06","arxiv_id":"2307.03679","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multilingual-transfer-for","title":"Leveraging multilingual transfer for unsupervised semantic acoustic word embeddings","date":"2023-07-05","arxiv_id":"2307.02083","repositories_listed":0,"syntology":null},{"url":null,"slug":"conceptual-cognitive-maps-formation-with","title":"Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings","date":"2023-07-04","arxiv_id":"2307.01577","repositories_listed":0,"syntology":null},{"url":null,"slug":"racial-bias-trends-in-the-text-of-us-legal","title":"Racial Bias Trends in the Text of US Legal Opinions","date":"2023-07-04","arxiv_id":"2307.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-world-knowledge-modeling-and","title":"Social World Knowledge: Modeling and Applications","date":"2023-06-28","arxiv_id":"2306.16299","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-neural-embeddings-with-sparse","title":"Interpretable Neural Embeddings with Sparse Self-Representation","date":"2023-06-25","arxiv_id":"2306.14135","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-automated-extraction-of-research-topics","title":"Semi-automated extraction of research topics and trends from NCI funding in radiological sciences from 2000-2020","date":"2023-06-22","arxiv_id":"2306.13075","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-uncertainty-in-word","title":"A Bayesian approach to uncertainty in word embedding bias estimation","date":"2023-06-15","arxiv_id":"2306.09066","repositories_listed":0,"syntology":null},{"url":null,"slug":"definition-modeling-to-model-definitions","title":"\"Definition Modeling: To model definitions.\" Generating Definitions With Little to No Semantics","date":"2023-06-14","arxiv_id":"2306.08433","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-mbert-understand-romansh-evaluating-word","title":"Does mBERT understand Romansh? Evaluating word embeddings using word alignment","date":"2023-06-14","arxiv_id":"2306.08702","repositories_listed":0,"syntology":null},{"url":null,"slug":"curatr-a-platform-for-semantic-analysis-and","title":"Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts","date":"2023-06-13","arxiv_id":"2306.08020","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-topic-extraction-in-recommender","title":"Enhancing Topic Extraction in Recommender Systems with Entropy Regularization","date":"2023-06-12","arxiv_id":"2306.07403","repositories_listed":0,"syntology":null},{"url":null,"slug":"alzheimer-disease-classification-through-asr","title":"Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses","date":"2023-06-06","arxiv_id":"2306.03443","repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustic-word-embeddings-for-untranscribed","title":"Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling","date":"2023-06-03","arxiv_id":"2306.02153","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-for-banking-industry","title":"Word Embeddings for Banking Industry","date":"2023-06-02","arxiv_id":"2306.01807","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-hate-speech-detection-in-low-resource","title":"Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili","date":"2023-06-01","arxiv_id":"2306.00410","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-augmented-language-models-for","title":"Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation","date":"2023-05-30","arxiv_id":"2305.18846","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-on-multilingual-news-clustering","title":"Research on Multilingual News Clustering Based on Cross-Language Word Embeddings","date":"2023-05-30","arxiv_id":"2305.18880","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-wacky-vs-definitely-wacky-a-study-of","title":"Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models","date":"2023-05-25","arxiv_id":"2305.16426","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-using-aligned-word","title":"Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages","date":"2023-05-24","arxiv_id":"2305.15380","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatgpt-edss-empathetic-dialogue-speech","title":"ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings","date":"2023-05-23","arxiv_id":"2305.13724","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-and-mitigating-indirect-stereotypes","title":"Detecting and Mitigating Indirect Stereotypes in Word Embeddings","date":"2023-05-23","arxiv_id":"2305.14574","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-brain-context-sensitivity-with-masked","title":"Probing Brain Context-Sensitivity with Masked-Attention Generation","date":"2023-05-23","arxiv_id":"2305.13863","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-a-large-language-model-to-control","title":"Controllable Speaking Styles Using a Large Language Model","date":"2023-05-17","arxiv_id":"2305.10321","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-aware-dimension-selection-for","title":"Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance","date":"2023-05-13","arxiv_id":"2305.07826","repositories_listed":0,"syntology":null},{"url":null,"slug":"lacos-bloom-low-rank-adaptation-with","title":"LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM","date":"2023-05-10","arxiv_id":"2305.06404","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-european-press-coverage-of-the","title":"Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework","date":"2023-04-29","arxiv_id":"2305.00182","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-frame-induction-with-deep-metric","title":"Semantic Frame Induction with Deep Metric Learning","date":"2023-04-27","arxiv_id":"2304.14286","repositories_listed":0,"syntology":null},{"url":null,"slug":"transcending-the-male-code-implicit-masculine","title":"Transcending the \"Male Code\": Implicit Masculine Biases in NLP Contexts","date":"2023-04-22","arxiv_id":"2304.12810","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-sense-induction-with-knowledge-1","title":"Word Sense Induction with Knowledge Distillation from BERT","date":"2023-04-20","arxiv_id":"2304.10642","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-query-by-example-keyword","title":"Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping","date":"2023-04-19","arxiv_id":"2304.09585","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-product-development-npd-through-social","title":"New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings","date":"2023-04-17","arxiv_id":"2304.08369","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-ctc-alignment-based-non-autoregressive","title":"A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition","date":"2023-04-15","arxiv_id":"2304.07611","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-graph-structure-information-for","title":"Investigating Graph Structure Information for Entity Alignment with Dangling Cases","date":"2023-04-10","arxiv_id":"2304.04718","repositories_listed":0,"syntology":null},{"url":"/paper/on-evaluation-of-bangla-word-analogies","slug":"on-evaluation-of-bangla-word-analogies","title":"On Evaluation of Bangla Word Analogies","date":"2023-04-10","arxiv_id":"2304.04613","repositories_listed":0,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-evaluation-of-bangla-word-analogies#ran","syntology_url":"https://syntology.ai/paper/2304.04613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04613"}},"official":null}},{"url":null,"slug":"affect-as-a-proxy-for-literary-mood","title":"Affect as a proxy for literary mood","date":"2023-04-06","arxiv_id":"2304.02894","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-opinion-mining-and-topic","title":"Deep Learning for Opinion Mining and Topic Classification of Course Reviews","date":"2023-04-06","arxiv_id":"2304.03394","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-name-classes-for-vision-and","title":"Learning to Name Classes for Vision and Language Models","date":"2023-04-04","arxiv_id":"2304.01830","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-generation-of-multiple-choice","title":"Automatic Generation of Multiple-Choice Questions","date":"2023-03-25","arxiv_id":"2303.14576","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-biases-in-the-texts-using-an-end","title":"Addressing Biases in the Texts using an End-to-End Pipeline Approach","date":"2023-03-13","arxiv_id":"2303.07024","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-text-based-conspiracy-tweets","title":"Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings","date":"2023-03-07","arxiv_id":"2303.03706","repositories_listed":0,"syntology":null},{"url":null,"slug":"changes-in-commuter-behavior-from-covid-19","title":"Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area","date":"2023-02-27","arxiv_id":"2302.13512","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-model-for-mongolian-citizens","title":"Deep learning model for Mongolian Citizens Feedback Analysis using Word Vector Embeddings","date":"2023-02-23","arxiv_id":"2302.12069","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-category-structure-with-contextual","title":"Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks","date":"2023-02-14","arxiv_id":"2302.06942","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-word-embeddings-for-the-social-1","title":"Evaluation of Word Embeddings for the Social Sciences","date":"2023-02-13","arxiv_id":"2302.06174","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialectograms-machine-learning-differences","title":"Dialectograms: Machine Learning Differences between Discursive Communities","date":"2023-02-11","arxiv_id":"2302.05657","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-language-models-performing-zero-shot","title":"Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities","date":"2023-01-26","arxiv_id":"2301.11100","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-for-accessible-multi","title":"Machine Translation for Accessible Multi-Language Text Analysis","date":"2023-01-20","arxiv_id":"2301.08416","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-exploration-of-natural","title":"News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand","date":"2023-01-18","arxiv_id":"2301.07535","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-fake-review-detection-using-supervised","title":"Online Fake Review Detection Using Supervised Machine Learning And BERT Model","date":"2023-01-09","arxiv_id":"2301.03225","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-representational-geometry-of","title":"Analyzing the Representational Geometry of Acoustic Word Embeddings","date":"2023-01-08","arxiv_id":"2301.03012","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-meaning-instead-of-words-to-track","title":"Using meaning instead of words to track topics","date":"2023-01-02","arxiv_id":"2301.00565","repositories_listed":0,"syntology":null},{"url":null,"slug":"tegformer-topic-to-essay-generation-with-good","title":"TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence","date":"2022-12-27","arxiv_id":"2212.13456","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-classification-in-shipping-industry","title":"Text classification in shipping industry using unsupervised models and Transformer based supervised models","date":"2022-12-21","arxiv_id":"2212.12407","repositories_listed":0,"syntology":null},{"url":null,"slug":"independent-components-of-word-embeddings","title":"Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test","date":"2022-12-19","arxiv_id":"2212.09580","repositories_listed":0,"syntology":null}],"record_sha256":"81ecff0cb7ef42f19bc43b927344d157f4d488b1b5aa5695d35490816f31591a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}