--- title: "Martha Palmer — papers, page 2 — Elamur Research" description: "Martha Palmer: 38 papers on Elamur Research (1994–2024), h-index 57 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/martha-palmer/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/martha-palmer/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Martha Palmer](https://md.research.elamur.ai/free/en/authors/martha-palmer/) / page 2 # Martha Palmer · page 2 38 papers in our library · 1994–2024 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/martha-palmer/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/martha-palmer/) [1](https://md.research.elamur.ai/free/en/authors/martha-palmer/) **2** 26. **[Towards a Domain Independent Semantics: Enhancing Semantic Representation with Construction Grammar](https://md.research.elamur.ai/free/en/papers/hwang-2010-domain-independent-semantics-enhancing-semantic/)** *(2010 · 17 citations)* The authors test whether ideas from Construction Grammar (CxG) can help improve the understanding of unfamiliar predicates. 27. **[SemLink 2.0: Chasing Lexical Resources](https://md.research.elamur.ai/free/en/papers/stowe-2021-semlink-2-0-chasing-lexical/)** *(2021 · 16 citations)* SemLink maps between PropBank rolesets and arguments, VerbNet classes and thematic roles, FrameNet frames and frame elements, and OntoNotes sense groups, and holds a corpus of verb instances tagged in all of them. 28. **[Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility](https://md.research.elamur.ai/free/en/papers/bonn-2023-mapping-amr-umr-resources-adapting/)** *(2023 · 15 citations)* A reference paper that lists how AMR structures map onto UMR, the typologically motivated successor to AMR. 29. **[Cross-document coreference: An approach to capturing coreference without context](https://md.research.elamur.ai/free/en/papers/bettner-2019-cross-document-coreference-approach-capturing/)** *(2019 · 15 citations)* The paper addresses a basic difficulty in building patient timelines from clinical text. Deciding whether two mentions corefer usually relies on a shared discourse context, but linking mentions across separate notes means reasoning over narratives that do not form one coherent text. The authors extended the THYME… 30. **[Enriching lexical transfer with cross-linguistic semantic features or how to do interlingua without interlingua](https://md.research.elamur.ai/free/en/papers/nasr-1997-enriching-lexical-transfer-cross-linguistic/)** *(1997 · 12 citations)* The paper proposes lexico-structural transfer as an alternative to an interlingua. The source and target are dependency trees in Meaning-Text Theory's deep syntactic level, and transfer rules relate lexemes together with their syntax. The test case is motion verbs: English "swim across the lake" becomes French… 31. **[Comprehensive and Consistent PropBank Light Verb Annotation](https://md.research.elamur.ai/free/en/papers/bonial-2016-comprehensive-consistent-propbank-light-verb/)** *(2016 · 10 citations)* The paper describes how PropBank's guidelines for light verb constructions (make an offer, take a bath, have a nap, give a kiss) were revised. 32. **[Linear Cross-document Event Coreference Resolution with X-AMR](https://md.research.elamur.ai/free/en/papers/ahmed-2024-linear-cross-document-event-coreference/)** *(2024 · 9 citations)* Resolving event coreference by comparing every pair of mentions scales quadratically. This becomes very costly with large corpora and especially with LLM prompting. The authors replace pairwise scoring with a compact symbolic representation called X-AMR, a cross-document extension of Abstract Meaning Representation.… 33. **[Semantic Tagging for the Penn Treebank](https://md.research.elamur.ai/free/en/papers/palmer-2000-semantic-tagging-penn-treebank/)** *(2000 · 8 citations)* The paper describes a pilot in which running Penn Treebank text is tagged with WordNet senses, following the method of the SENSEVAL exercise. 34. **[UMR Annotation of Multiword Expressions](https://md.research.elamur.ai/free/en/papers/bonn-2023-umr-annotation-multiword-expressions/)** *(2023 · 7 citations)* This is a guideline paper. It runs no experiment and reports no counts. UMR, which is based on AMR, maps words to concepts. The authors define a multiword expression (MWE) operationally: several words that together form one concept. They sort MWEs into three groups. Fixed MWEs are unmodifiable word sequences, such as… 35. **[Dependency Dialogue Acts — Annotation Scheme and Case Study](https://md.research.elamur.ai/free/en/papers/cai-2023-dependency-dialogue-acts-annotation-scheme/)** *(2023 · 6 citations)* Dependency Dialogue Acts (DDA) is an annotation scheme for multi-party, multi-threaded talk (classroom group work, chat). 36. **[The DARPA Wikidata Overlay: Wikidata as an ontology for natural language processing](https://md.research.elamur.ai/free/en/papers/spaulding-2023-darpa-wikidata-overlay-wikidata-as/)** *(2023 · 6 citations)* Wikidata is the largest crowdsourced knowledge base about the world (over 102 million items). However, it is entity-centric. Events there are mostly named by deverbal nouns, and their typical participants (who acts, toward whom the action is directed) are not linked to these events. This is insufficient for… 37. **[Annotation Difficulties in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kalouli-2021-annotation-difficulties-natural-language-inference/)** *(2021 · 5 citations)* Modern models score very well on popular NLI benchmarks, yet they generalize poorly and ignore the fact that people often disagree about inference labels. 38. **[A corpus of preposition supersenses in English web reviews](https://md.research.elamur.ai/free/en/papers/schneider-2016-corpus-preposition-supersenses-english-web/)** *(2016 · 3 citations)* English prepositions are extremely frequent and highly polysemous, yet earlier annotated resources for their meaning cover only a few dozen common types, sample isolated tokens, or lack inter-annotator agreement figures. [← prev](https://md.research.elamur.ai/free/en/authors/martha-palmer/) [1](https://md.research.elamur.ai/free/en/authors/martha-palmer/) **2**