Cross Framework Meaning Representation Parsing

Dan Kondratyuk; Casey Kennington; Rohit Gangurde; Nikita Kramar

Cross Framework Meaning Representation Parsing

Dan Kondratyuk, Casey Kennington, Rohit Gangurde, Nikita Kramar

Computer Science Department

Research output: Contribution to conference › Presentation

Abstract

Many natural language processing tasks can benefit from syntactic and semantic information. While there are several choices to representing the syntax and semantics of a sentence using a graph structure, the recent 2019 CoNLL Shared Task brings together five approaches into a single representation. Our approach to the CoNLL shared task is to model the words in the sentences as nodes and use a neural network with an attention layer to determine the graph structure. Our preliminary results have shown promise in this task.

Original language	American English
State	Published - 12 Jul 2019

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title = "Cross Framework Meaning Representation Parsing",

abstract = " Many natural language processing tasks can benefit from syntactic and semantic information. While there are several choices to representing the syntax and semantics of a sentence using a graph structure, the recent 2019 CoNLL Shared Task brings together five approaches into a single representation. Our approach to the CoNLL shared task is to model the words in the sentences as nodes and use a neural network with an attention layer to determine the graph structure. Our preliminary results have shown promise in this task.",

author = "Dan Kondratyuk and Casey Kennington and Rohit Gangurde and Nikita Kramar",

year = "2019",

month = jul,

day = "12",

language = "American English",

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TY - CONF

T1 - Cross Framework Meaning Representation Parsing

AU - Kondratyuk, Dan

AU - Kennington, Casey

AU - Gangurde, Rohit

AU - Kramar, Nikita

PY - 2019/7/12

Y1 - 2019/7/12

N2 - Many natural language processing tasks can benefit from syntactic and semantic information. While there are several choices to representing the syntax and semantics of a sentence using a graph structure, the recent 2019 CoNLL Shared Task brings together five approaches into a single representation. Our approach to the CoNLL shared task is to model the words in the sentences as nodes and use a neural network with an attention layer to determine the graph structure. Our preliminary results have shown promise in this task.

AB - Many natural language processing tasks can benefit from syntactic and semantic information. While there are several choices to representing the syntax and semantics of a sentence using a graph structure, the recent 2019 CoNLL Shared Task brings together five approaches into a single representation. Our approach to the CoNLL shared task is to model the words in the sentences as nodes and use a neural network with an attention layer to determine the graph structure. Our preliminary results have shown promise in this task.

M3 - Presentation

ER -

Cross Framework Meaning Representation Parsing

Abstract

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