Amir More

3 paper cards in Mova's library · 2017–2019

Amir More wrote 3 of the papers Mova reads, in 2017–2019, most of them on Dependency parsing, Treebanks; often with Reut Tsarfaty, Amit Seker, Victoria Basmova.

The profile is the library's match of the name; two people with one name are not always told apart.

Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

measured, no gain — MDFirst as a re-ranker: keep the top-B segmentations from the Hebrew segmenter and let the parser rescore each one with its tree score, then take the best joint result. The first target is the segmenter in tools/hbo. (Joint Transition-Based Models for Morpho-Syntactic Parsing… · top-k segmentations rescored by gparse's tree score (hbo lattice/choose): no gain beyond noise on Chronicles (unpointed 79.73 -> 79.58..79.99, pointed 85.24…)

Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew (2019)
In Hebrew, morphological analysis and disambiguation (MA&D) normally comes before parsing, yet the syntax would help choose the morphology, and the reverse.
Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task (2018)
The ONLP Lab submission used UDPipe for segmentation and morphological disambiguation, then yap's arc-eager dependency parser (a Zhang–Nivre variant with morphological-signature features).
Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task (2017)
The OpenU-NLP-Lab system for the CoNLL 2017 UD shared task is a transition-based, non-neural joint morphological disambiguator and arc-standard dependency parser (built in yap).