HPSG-Inspired Joint Neural Constituent and Dependency Parsing in O(n(3)) Time Complexity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10441614" target="_blank" >RIV/00216208:11320/22:10441614 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rQMY.bNWG9" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rQMY.bNWG9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASLP.2021.3138715" target="_blank" >10.1109/TASLP.2021.3138715</a>

Jazyk výsledku

angličtina

Název v původním jazyce

HPSG-Inspired Joint Neural Constituent and Dependency Parsing in O(n(3)) Time Complexity

Popis výsledku v původním jazyce

Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, inspired by Head-driven Phrase Structure Grammar (HPSG). We thus refer to this joint parsing of constituency and dependency as HPSG-like parsing. However, in HPSG-like parsing, decoding this unified grammar has a higher time complexity (O(n(3))) than decoding either form individually (O(n(3))) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in O(n(3)) time complexity. Furthermore, on the basis of this proposed practical HPSG-like parser, we investigated the strengths of HPSG-like parsing and explored the general method of training an HPSG-like parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG-like parsing.

Název v anglickém jazyce

HPSG-Inspired Joint Neural Constituent and Dependency Parsing in O(n(3)) Time Complexity

Popis výsledku anglicky

Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, inspired by Head-driven Phrase Structure Grammar (HPSG). We thus refer to this joint parsing of constituency and dependency as HPSG-like parsing. However, in HPSG-like parsing, decoding this unified grammar has a higher time complexity (O(n(3))) than decoding either form individually (O(n(3))) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in O(n(3)) time complexity. Furthermore, on the basis of this proposed practical HPSG-like parser, we investigated the strengths of HPSG-like parsing and explored the general method of training an HPSG-like parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG-like parsing.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE - ACM Transactions on Audio, Speech, and Language Processing

ISSN

2329-9290

e-ISSN

2329-9304

Svazek periodika

30

Číslo periodika v rámci svazku

28.12.2021

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

355-366

Kód UT WoS článku

000742717400001

EID výsledku v databázi Scopus

2-s2.0-85122302994