[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/all/page/10/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [all](https://md.mova.elamur.ai/free/en/docs/papers/all/) / page 10

# papers/all/

1731 pages

[1](https://md.mova.elamur.ai/free/en/docs/papers/all/)[2](https://md.mova.elamur.ai/free/en/docs/papers/all/page/2/)[3](https://md.mova.elamur.ai/free/en/docs/papers/all/page/3/)[4](https://md.mova.elamur.ai/free/en/docs/papers/all/page/4/)[5](https://md.mova.elamur.ai/free/en/docs/papers/all/page/5/)[6](https://md.mova.elamur.ai/free/en/docs/papers/all/page/6/)[7](https://md.mova.elamur.ai/free/en/docs/papers/all/page/7/)[8](https://md.mova.elamur.ai/free/en/docs/papers/all/page/8/)[9](https://md.mova.elamur.ai/free/en/docs/papers/all/page/9/)**10**[11](https://md.mova.elamur.ai/free/en/docs/papers/all/page/11/)[12](https://md.mova.elamur.ai/free/en/docs/papers/all/page/12/)[13](https://md.mova.elamur.ai/free/en/docs/papers/all/page/13/)[14](https://md.mova.elamur.ai/free/en/docs/papers/all/page/14/)[15](https://md.mova.elamur.ai/free/en/docs/papers/all/page/15/)[16](https://md.mova.elamur.ai/free/en/docs/papers/all/page/16/)[17](https://md.mova.elamur.ai/free/en/docs/papers/all/page/17/)[18](https://md.mova.elamur.ai/free/en/docs/papers/all/page/18/)

- [mtrans/gekhman-2020-kobe-knowledge-based-machine-translation](https://md.mova.elamur.ai/free/en/docs/papers/gekhman-2020-kobe-knowledge-based-machine-translation/) — KoBE: Knowledge-Based Machine Translation Evaluation
- [hbo/hbo-2014-roorda-laf-fabric-data-analysis-tool](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2014-roorda-laf-fabric-data-analysis-tool/) — LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible
- [he/lingbulk-2012-zhicharevich-language-classification-segmentation-noisy-documents](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2012-zhicharevich-language-classification-segmentation-noisy-documents/) — Language Classification and Segmentation of Noisy Documents in Hebrew Scripts
- [hbo/hbo-2017-cook-language-contact-genesis-mishnaic-hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2017-cook-language-contact-genesis-mishnaic-hebrew/) — Language contact and the genesis of Mishnaic Hebrew
- [hbo/hbo-2024-staps-large-language-models-biblical-hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2024-staps-large-language-models-biblical-hebrew/) — Large Language Models and Biblical Hebrew: Limitations, pitfalls, opportunities
- [hbo/hbo-2022-gueta-large-pre-trained-models-extra](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-gueta-large-pre-trained-models-extra/) — Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All
- [en/lex-2022-eyal-substitution-wsi](https://md.mova.elamur.ai/free/en/docs/papers/lex-2022-eyal-substitution-wsi/) — Large Scale Substitution-based Word Sense Induction
- [he/langstream-2016-belinkov-large-scale-machine-translation-between](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2016-belinkov-large-scale-machine-translation-between/) — Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results
- [en/fitzgerald-2018-large-scale-qa-srl-parsing](https://md.mova.elamur.ai/free/en/docs/papers/fitzgerald-2018-large-scale-qa-srl-parsing/) — Large-Scale QA-SRL Parsing
- [en/noble-2021-large-scale-text-pre-training](https://md.mova.elamur.ai/free/en/docs/papers/noble-2021-large-scale-text-pre-training/) — Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning
- [hbo/hbo-2008-young-late-biblical-hebrew-qumran-pesher](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2008-young-late-biblical-hebrew-qumran-pesher/) — Late Biblical Hebrew and the Qumran Pesher Habakkuk
- [he/findings-2025-malul-lchaim-investigating-long-context-reasoning](https://md.mova.elamur.ai/free/en/docs/papers/findings-2025-malul-lchaim-investigating-long-context-reasoning/) — LCHAIM - Investigating Long Context Reasoning in Hebrew
- [en/jorgensen-2016-learning-pos-tagger-aave-like](https://md.mova.elamur.ai/free/en/docs/papers/jorgensen-2016-learning-pos-tagger-aave-like/) — Learning a POS tagger for AAVE-like language
- [en/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree/) — Learning Accurate, Compact, and Interpretable Tree Annotation
- [en/aclbulk-2017-bollmann-learning-attention-historical-text-normalization](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-bollmann-learning-attention-historical-text-normalization/) — Learning attention for historical text normalization by learning to pronounce
- [mtrans/lavoie-2002-learning-domain-specific-transfer-rules](https://md.mova.elamur.ai/free/en/docs/papers/lavoie-2002-learning-domain-specific-transfer-rules/) — Learning Domain-Specific Transfer Rules: An Experiment with Korean to English Translation
- [hbo/aclbulk-2004-daya-learning-hebrew-roots-machine-learning](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2004-daya-learning-hebrew-roots-machine-learning/) — Learning Hebrew Roots: Machine Learning with Linguistic Constraints
- [mtrans/manchego-2017-learning-how-simplify-explicit-labeling](https://md.mova.elamur.ai/free/en/docs/papers/manchego-2017-learning-how-simplify-explicit-labeling/) — Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
- [hbo/cl-1995-levinger-learning-morpho-lexical-probabilities-untagged](https://md.mova.elamur.ai/free/en/docs/papers/cl-1995-levinger-learning-morpho-lexical-probabilities-untagged/) — Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew
- [en/lingbulk-2008-chrupala-learning-morphology-morfette](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2008-chrupala-learning-morphology-morfette/) — Learning Morphology with Morfette
- [mtrans/eisner-2003-learning-non-isomorphic-tree-mappings](https://md.mova.elamur.ai/free/en/docs/papers/eisner-2003-learning-non-isomorphic-tree-mappings/) — Learning Non-Isomorphic Tree Mappings for Machine Translation
- [en/cocos-2018-learning-scalar-adjective-intensity-paraphrases](https://md.mova.elamur.ai/free/en/docs/papers/cocos-2018-learning-scalar-adjective-intensity-paraphrases/) — Learning Scalar Adjective Intensity from Paraphrases
- [mtrans/sanchez-2020-martinez-learning-synchronous-context-free-grammars](https://md.mova.elamur.ai/free/en/docs/papers/sanchez-2020-martinez-learning-synchronous-context-free-grammars/) — Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation
- [mtrans/barzilay-2003-learning-paraphrase-unsupervised-approach-multiple](https://md.mova.elamur.ai/free/en/docs/papers/barzilay-2003-learning-paraphrase-unsupervised-approach-multiple/) — Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment
- [jie-2022-learning-reason-deductively-math-word](https://md.mova.elamur.ai/free/en/docs/papers/jie-2022-learning-reason-deductively-math-word/) — Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction
- [mtrans/woodsend-2011-learning-simplify-sentences-quasi-synchronous](https://md.mova.elamur.ai/free/en/docs/papers/woodsend-2011-learning-simplify-sentences-quasi-synchronous/) — Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming
- [hosseini-2014-learning-solve-arithmetic-word-problems](https://md.mova.elamur.ai/free/en/docs/papers/hosseini-2014-learning-solve-arithmetic-word-problems/) — Learning to Solve Arithmetic Word Problems with Verb Categorization
- [mtrans/chiang-2010-learning-translate-source-target-syntax](https://md.mova.elamur.ai/free/en/docs/papers/chiang-2010-learning-translate-source-target-syntax/) — Learning to Translate with Source and Target Syntax
- [mitra-2016-learning-use-formulas-solve-simple](https://md.mova.elamur.ai/free/en/docs/papers/mitra-2016-learning-use-formulas-solve-simple/) — Learning To Use Formulas To Solve Simple Arithmetic Problems
- [hbo/hbo-2014-westbury-left-dislocation-biblical-hebrew-cognitive](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2014-westbury-left-dislocation-biblical-hebrew-cognitive/) — Left Dislocation in Biblical Hebrew: A Cognitive Linguistic Account
- [en/aclbulk-2012-gesmundo-lemmatisation-as-tagging-task](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-gesmundo-lemmatisation-as-tagging-task/) — Lemmatisation as a Tagging Task
- [en/verdizade-2021-wiktionary-en-sv](https://md.mova.elamur.ai/free/en/docs/papers/verdizade-2021-wiktionary-en-sv/) — Lemmatisation, etymology and information overload on English and Swedish editions of Wiktionary
- [en/gram-2020-trips-lemmatising-verbs-middle-english-corpora](https://md.mova.elamur.ai/free/en/docs/papers/gram-2020-trips-lemmatising-verbs-middle-english-corpora/) — Lemmatising Verbs in Middle English Corpora: The Benefit of Enriching the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME)
- [arc/aclbulk-2020-gorene-lemmatization-pos-tagging-process-joint](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-gorene-lemmatization-pos-tagging-process-joint/) — Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac
- [sem/ctx-2025-saeed-lemmatization-as-classification-task-results](https://md.mova.elamur.ai/free/en/docs/papers/ctx-2025-saeed-lemmatization-as-classification-task-results/) — Lemmatization as a Classification Task: Results from Arabic across Multiple Genres
- [en/cianflone-2018-let-s-do-it-again](https://md.mova.elamur.ai/free/en/docs/papers/cianflone-2018-let-s-do-it-again/) — Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
- [mtrans/jerome-1893-letter-57-pammachius-best](https://md.mova.elamur.ai/free/en/docs/papers/jerome-1893-letter-57-pammachius-best/) — Letter 57, to Pammachius, On the Best Method of Translating
- [mtrans/benson-1739-letters-concerning-poetical-translations](https://md.mova.elamur.ai/free/en/docs/papers/benson-1739-letters-concerning-poetical-translations/) — Letters Concerning Poetical Translations, and Virgil's and Milton's Arts of Verse
- [he/aclbulk-2026-dahan-leveraging-digitized-newspapers-collect-summarization](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-dahan-leveraging-digitized-newspapers-collect-summarization/) — Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages
- [mtrans/torregrosa-2019-leveraging-rule-based-machine-translation](https://md.mova.elamur.ai/free/en/docs/papers/torregrosa-2019-leveraging-rule-based-machine-translation/) — Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models
- [en/moosavi-2017-lexical-features-coreference-resolution-be](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2017-lexical-features-coreference-resolution-be/) — Lexical Features in Coreference Resolution: To be Used With Caution
- [en/beale-1988-lexicon-grammar-probabilistic-tagging-written](https://md.mova.elamur.ai/free/en/docs/papers/beale-1988-lexicon-grammar-probabilistic-tagging-written/) — Lexicon and grammar in probabilistic tagging of written English
- [en/bank-2007-ahrenberg-lines-english-swedish-parallel-treebank](https://md.mova.elamur.ai/free/en/docs/papers/bank-2007-ahrenberg-lines-english-swedish-parallel-treebank/) — LinES: An English-Swedish Parallel Treebank
- [en/otmazgin-2023-lingmess-linguistically-informed-multi-expert](https://md.mova.elamur.ai/free/en/docs/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/) — LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
- [en/enprag-2004-oepen-lingo-redwoods-rich-dynamic-treebank](https://md.mova.elamur.ai/free/en/docs/papers/enprag-2004-oepen-lingo-redwoods-rich-dynamic-treebank/) — LinGO Redwoods: A Rich and Dynamic Treebank for HPSG
- [en/ducret-2020-linguistic-features-automatic-sarcasm-detection](https://md.mova.elamur.ai/free/en/docs/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/) — Linguistic Features in Automatic Sarcasm Detection
- [mtrans/ferreira-2017-linguistic-realisation-as-machine-translation](https://md.mova.elamur.ai/free/en/docs/papers/ferreira-2017-linguistic-realisation-as-machine-translation/) — Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation
- [hbo/hbo-2019-stornaiuolo-linguistic-functional-analysis-biblical-hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2019-stornaiuolo-linguistic-functional-analysis-biblical-hebrew/) — Linguistic-functional Analysis of the Biblical Hebrew Lexemes ʿaṭarâ, keter and nezer
- [mtrans/rajkumar-2011-linguistically-motivated-complementizer-choice-surface](https://md.mova.elamur.ai/free/en/docs/papers/rajkumar-2011-linguistically-motivated-complementizer-choice-surface/) — Linguistically Motivated Complementizer Choice in Surface Realization
- [mtrans/macketanz-2022-linguistically-motivated-evaluation-2022-state](https://md.mova.elamur.ai/free/en/docs/papers/macketanz-2022-linguistically-motivated-evaluation-2022-state/) — Linguistically Motivated Evaluation of the 2022 State-of-the-art Machine Translation Systems for Three Language Directions
- [hbo/hbo-2022-staps-lipne-face-locative-preposition-threatening](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-staps-lipne-face-locative-preposition-threatening/) — Lip̄nē 'in the face of': A Locative Preposition with a Threatening Connotation
- [ginn-2025-llm-dependency-parsing-context-rules](https://md.mova.elamur.ai/free/en/docs/papers/ginn-2025-llm-dependency-parsing-context-rules/) — LLM Dependency Parsing with In-Context Rules
- [mtrans/coleman-2024-llm-assisted-rule-based-machine](https://md.mova.elamur.ai/free/en/docs/papers/coleman-2024-llm-assisted-rule-based-machine/) — LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages
- [hbo/hbo-2025-notarius-loanwords-language-archaic-biblical-hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2025-notarius-loanwords-language-archaic-biblical-hebrew/) — Loanwords in the Language of Archaic Biblical Hebrew Poetry: A Case for Akkadianisms
- [en/khashabi-2018-looking-beyond-surface-challenge-set](https://md.mova.elamur.ai/free/en/docs/papers/khashabi-2018-looking-beyond-surface-challenge-set/) — Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
- [en/baechler-2018-loss-preservation-case-germanic-non](https://md.mova.elamur.ai/free/en/docs/papers/baechler-2018-loss-preservation-case-germanic-non/) — Loss and preservation of case in Germanic non-standard varieties
- [mtrans/wein-2024-lost-translationese-reducing-translation-effect](https://md.mova.elamur.ai/free/en/docs/papers/wein-2024-lost-translationese-reducing-translation-effect/) — Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation
- [mtrans/patel-2022-low-resource-authorship-style-transfer](https://md.mova.elamur.ai/free/en/docs/papers/patel-2022-low-resource-authorship-style-transfer/) — Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?
- [en/aclbulk-2016-lewis-lstm-ccg-parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-lewis-lstm-ccg-parsing/) — LSTM CCG Parsing
- [en/grote-1995-ma-r-king-concessions-english](https://md.mova.elamur.ai/free/en/docs/papers/grote-1995-ma-r-king-concessions-english/) — Ma(r)king concessions in English and German
- [en/arevalo-2021-macberth-development-evaluation-historically-pre](https://md.mova.elamur.ai/free/en/docs/papers/arevalo-2021-macberth-development-evaluation-historically-pre/) — MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)
- [arc/langstream-2026-sliwa-machine-translation-between-english-syriac](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2026-sliwa-machine-translation-between-english-syriac/) — Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning
- [he/lingbulk-2010-shilon-machine-translation-between-hebrew-arabic](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2010-shilon-machine-translation-between-hebrew-arabic/) — Machine Translation between Hebrew and Arabic: Needs, Challenges and Preliminary Solutions
- [mtrans/dorr-1994-machine-translation-divergences-formal-description](https://md.mova.elamur.ai/free/en/docs/papers/dorr-1994-machine-translation-divergences-formal-description/) — Machine Translation Divergences: A Formal Description and Proposed Solution
- [mtrans/thunes-1994-machine-translation-strategies-comparison-f](https://md.mova.elamur.ai/free/en/docs/papers/thunes-1994-machine-translation-strategies-comparison-f/) — Machine Translation Strategies: A Comparison of F-Structure Transfer and Semantically Based Interlingua
- [mtrans/beaven-1988-machine-translation-isomorphic-ucgs](https://md.mova.elamur.ai/free/en/docs/papers/beaven-1988-machine-translation-isomorphic-ucgs/) — Machine Translation Using Isomorphic UCGs
- [mtrans/ding-2005-machine-translation-probabilistic-synchronous-dependency](https://md.mova.elamur.ai/free/en/docs/papers/ding-2005-machine-translation-probabilistic-synchronous-dependency/) — Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars
- [mtrans/somers-1990-machine-translation-without-source-text](https://md.mova.elamur.ai/free/en/docs/papers/somers-1990-machine-translation-without-source-text/) — Machine Translation without a source text
- [mtrans/sgall-1987-machine-translation-linguistics-interlingua](https://md.mova.elamur.ai/free/en/docs/papers/sgall-1987-machine-translation-linguistics-interlingua/) — Machine Translation, Linguistics, and Interlingua
- [mtrans/gamon-2002-machine-learned-contexts-linguistic-operations](https://md.mova.elamur.ai/free/en/docs/papers/gamon-2002-machine-learned-contexts-linguistic-operations/) — Machine-learned contexts for linguistic operations in German sentence realization
- [sem/lingbulk-2014-pasha-madamira-fast-comprehensive-tool-morphological](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2014-pasha-madamira-fast-comprehensive-tool-morphological/) — MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
- [sem/aclbulk-2006-habash-magead-morphological-analyzer-generator-arabic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-habash-magead-morphological-analyzer-generator-arabic/) — MAGEAD: A Morphological Analyzer and Generator for the Arabic Dialects
- [hbo/hbo-2012-andrason-making-sound-performative-qatal-explanation](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2012-andrason-making-sound-performative-qatal-explanation/) — Making It Sound: The Performative Qatal and Its Explanation
- [en/meyer-2020-many-systems-one-strategy-acquiring](https://md.mova.elamur.ai/free/en/docs/papers/meyer-2020-many-systems-one-strategy-acquiring/) — Many systems, one strategy: Acquiring ordinals in Dutch and English
- [mtrans/bonn-2023-mapping-amr-umr-resources-adapting](https://md.mova.elamur.ai/free/en/docs/papers/bonn-2023-mapping-amr-umr-resources-adapting/) — Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility
- [en/costa-2023-mapping-explicit-implicit-discourse-relations](https://md.mova.elamur.ai/free/en/docs/papers/costa-2023-mapping-explicit-implicit-discourse-relations/) — Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0
- [roy-2018-mapping-declarative-knowledge-word-problem](https://md.mova.elamur.ai/free/en/docs/papers/roy-2018-mapping-declarative-knowledge-word-problem/) — Mapping to Declarative Knowledge for Word Problem Solving
- [sem/aclbulk-2006-zitouni-maximum-entropy-based-restoration-arabic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-zitouni-maximum-entropy-based-restoration-arabic/) — Maximum Entropy Based Restoration of Arabic Diacritics
- [en/missing-2013-richardson-mctest-challenge-dataset-open-domain](https://md.mova.elamur.ai/free/en/docs/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/) — MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
- [en/scivetti-2023-meaning-representation-english-prepositional-phrase](https://md.mova.elamur.ai/free/en/docs/papers/scivetti-2023-meaning-representation-english-prepositional-phrase/) — Meaning Representation of English Prepositional Phrase Roles: SNACS Supersenses vs. Tectogrammatical Functors
- [mtrans/lo-2017-meant-2-0-accurate-semantic](https://md.mova.elamur.ai/free/en/docs/papers/lo-2017-meant-2-0-accurate-semantic/) — MEANT 2.0: Accurate semantic MT evaluation for any output language
- [mtrans/lo-2011-meant-inexpensive-high-accuracy-semi](https://md.mova.elamur.ai/free/en/docs/papers/lo-2011-meant-inexpensive-high-accuracy-semi/) — MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles
- [mtrans/wein-2023-measuring-fine-grained-semantic-equivalence](https://md.mova.elamur.ai/free/en/docs/papers/wein-2023-measuring-fine-grained-semantic-equivalence/) — Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation
- [sem/aclbulk-2005-marsi-memory-based-morphological-analysis-generation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-marsi-memory-based-morphological-analysis-generation/) — Memory-Based Morphological Analysis Generation and Part-of-Speech Tagging of Arabic
- [hbo/hbo-2024-cohen-menakbert-hebrew-diacriticizer](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2024-cohen-menakbert-hebrew-diacriticizer/) — MenakBERT — Hebrew Diacriticizer
- [mtrans/marecek-2016-merged-bilingual-trees-based-universal](https://md.mova.elamur.ai/free/en/docs/papers/marecek-2016-merged-bilingual-trees-based-universal/) — Merged bilingual trees based on Universal Dependencies in Machine Translation
- [en/pustejovsky-2005-merging-propbank-nombank-timebank-penn](https://md.mova.elamur.ai/free/en/docs/papers/pustejovsky-2005-merging-propbank-nombank-timebank-penn/) — Merging PropBank, NomBank, TimeBank, Penn Discourse Treebank and Coreference
- [en/woods-2021-metacommunicative-i-why-i-fragments](https://md.mova.elamur.ai/free/en/docs/papers/woods-2021-metacommunicative-i-why-i-fragments/) — Metacommunicative-why fragments as probes into the grammar of the speech act layer
- [mtrans/banerjee-2005-meteor-automatic-metric-mt-evaluation](https://md.mova.elamur.ai/free/en/docs/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/) — METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments
- [mtrans/bennett-1988-methodological-considerations-metal-project](https://md.mova.elamur.ai/free/en/docs/papers/bennett-1988-methodological-considerations-metal-project/) — Methodological considerations in the METAL project
- [en/goering-2025-metre-clitics-old-english-old](https://md.mova.elamur.ai/free/en/docs/papers/goering-2025-metre-clitics-old-english-old/) — Metre and clitics in Old English and Old Saxon
- [he/langstream-2024-shalumov-mevaker-conclusion-extraction-allocation-resources](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2024-shalumov-mevaker-conclusion-extraction-allocation-resources/) — Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language
- [en/yu-2021-midas-dialog-act-annotation-scheme](https://md.mova.elamur.ai/free/en/docs/papers/yu-2021-midas-dialog-act-annotation-scheme/) — MIDAS: A Dialog Act Annotation Scheme for Open Domain Human-Machine Spoken Conversations
- [en/webster-2018-mind-gap-balanced-corpus-gendered](https://md.mova.elamur.ai/free/en/docs/papers/webster-2018-mind-gap-balanced-corpus-gendered/) — Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
- [en/ud-2018-droganova-mind-gap-data-enrichment-dependency](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-droganova-mind-gap-data-enrichment-dependency/) — Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions
- [kalpakchi-2021-minor-changes-make-difference-case](https://md.mova.elamur.ai/free/en/docs/papers/kalpakchi-2021-minor-changes-make-difference-case/) — Minor changes make a difference: a case study on the consistency of UD-based dependency parsers
- [hbo/hbo-2026-smiley-miqrabert-regression-based-sentence-bert](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2026-smiley-miqrabert-regression-based-sentence-bert/) — MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection
- [schneider-2021-mischievous-nominal-constructions-universal-dependencies](https://md.mova.elamur.ai/free/en/docs/papers/schneider-2021-mischievous-nominal-constructions-universal-dependencies/) — Mischievous nominal constructions in Universal Dependencies
- [he/lingbulk-2018-landau-missing-objects-hebrew-argument-ellipsis](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2018-landau-missing-objects-hebrew-argument-ellipsis/) — Missing objects in Hebrew: Argument ellipsis, not VP ellipsis
- [en/albright-2002-modeling-english-past-tense-intuitions](https://md.mova.elamur.ai/free/en/docs/papers/albright-2002-modeling-english-past-tense-intuitions/) — Modeling English Past Tense Intuitions with Minimal Generalization

[1](https://md.mova.elamur.ai/free/en/docs/papers/all/)[2](https://md.mova.elamur.ai/free/en/docs/papers/all/page/2/)[3](https://md.mova.elamur.ai/free/en/docs/papers/all/page/3/)[4](https://md.mova.elamur.ai/free/en/docs/papers/all/page/4/)[5](https://md.mova.elamur.ai/free/en/docs/papers/all/page/5/)[6](https://md.mova.elamur.ai/free/en/docs/papers/all/page/6/)[7](https://md.mova.elamur.ai/free/en/docs/papers/all/page/7/)[8](https://md.mova.elamur.ai/free/en/docs/papers/all/page/8/)[9](https://md.mova.elamur.ai/free/en/docs/papers/all/page/9/)**10**[11](https://md.mova.elamur.ai/free/en/docs/papers/all/page/11/)[12](https://md.mova.elamur.ai/free/en/docs/papers/all/page/12/)[13](https://md.mova.elamur.ai/free/en/docs/papers/all/page/13/)[14](https://md.mova.elamur.ai/free/en/docs/papers/all/page/14/)[15](https://md.mova.elamur.ai/free/en/docs/papers/all/page/15/)[16](https://md.mova.elamur.ai/free/en/docs/papers/all/page/16/)[17](https://md.mova.elamur.ai/free/en/docs/papers/all/page/17/)[18](https://md.mova.elamur.ai/free/en/docs/papers/all/page/18/)