Search Results for author: Nick Howell

Found 7 papers, 2 papers with code

A survey of part-of-speech tagging approaches applied to K’iche’

no code implementations NAACL (AmericasNLP) 2021 Francis Tyers, Nick Howell

We study the performance of several popular neural part-of-speech taggers from the Universal Dependencies ecosystem on Mayan languages using a small corpus of 1435 annotated K’iche’ sentences consisting of approximately 10, 000 tokens, with encouraging results: F_1 scores 93%+ on lemmatisation, part-of-speech and morphological feature assignment.

Diversity Part-Of-Speech Tagging +1

The Knesset Corpus: An Annotated Corpus of Hebrew Parliamentary Proceedings

no code implementations28 May 2024 Gili Goldin, Nick Howell, Noam Ordan, Ella Rabinovich, Shuly Wintner

We present the Knesset Corpus, a corpus of Hebrew parliamentary proceedings containing over 30 million sentences (over 384 million tokens) from all the (plenary and committee) protocols held in the Israeli parliament between 1998 and 2022.

A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing

2 code implementations14 Oct 2022 Amir Zeldes, Nick Howell, Noam Ordan, Yifat Ben Moshe

Foundational Hebrew NLP tasks such as segmentation, tagging and parsing, have relied to date on various versions of the Hebrew Treebank (HTB, Sima'an et al. 2001).

Language Modelling

An Unsupervised Method for Weighting Finite-state Morphological Analyzers

2 code implementations LREC 2020 Amr Keleg, Francis Tyers, Nick Howell, Tommi Pirinen

In this paper, we have developed a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results.

Morphological Analysis

Language Models for Cloze Task Answer Generation in Russian

no code implementations LREC 2020 Anastasia Nikiforova, Sergey Pletenev, Daria Sinitsyna, Semen Sorokin, Anastasia Lopukhina, Nick Howell

Currently, to get a measure of the language unit predictability, a neurolinguistic experiment known as a cloze task has to be conducted on a large number of participants.

Answer Generation Language Modelling +1

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