Meaning representation in natural language processing
In this report, the semantic information in parse selection is analyzed. A Python software model was used to carry out feature engineering on semantic parsing results by parsers. The data used was from the SemCor corpus and WeScience corpus. Different types of semantic features were generated usin...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2014
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/59189 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-59189 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-591892023-03-03T20:49:17Z Meaning representation in natural language processing Tripathi Surabhita School of Computer Engineering Kim Jung-Jae Francis Bond DRNTU::Engineering::Computer science and engineering In this report, the semantic information in parse selection is analyzed. A Python software model was used to carry out feature engineering on semantic parsing results by parsers. The data used was from the SemCor corpus and WeScience corpus. Different types of semantic features were generated using the model and training and testing was conducted using a maximum entropy model TADM. Error analysis was performed on the entire SemCor and WeScience corpus by reproducing the old results. Generalized features provide better parse selection accuracy than more specific features. Further Machine learning was performed using ELM, Extreme Machine Learning technique, to compare the parse ranking results with TADM. The key fundamental task is to understand the meaning of a word in a sentence and semantic relations between words, resolving ambiguities by considering context. Bachelor of Engineering (Computer Engineering) 2014-04-25T02:54:29Z 2014-04-25T02:54:29Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/59189 en Nanyang Technological University 81 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Computer science and engineering |
spellingShingle |
DRNTU::Engineering::Computer science and engineering Tripathi Surabhita Meaning representation in natural language processing |
description |
In this report, the semantic information in parse selection is analyzed. A Python software model was used to carry out feature engineering on semantic parsing results by parsers. The data used was from the SemCor corpus and WeScience corpus. Different types of semantic features were generated using the model and training and testing was conducted using a maximum entropy model TADM. Error analysis was performed on the entire SemCor and WeScience corpus by reproducing the old results. Generalized features provide better parse selection accuracy than more specific features. Further Machine learning was performed using ELM, Extreme Machine Learning technique, to compare the parse ranking results with TADM. The key fundamental task is to understand the meaning of a word in a sentence and semantic relations between words, resolving ambiguities by considering context. |
author2 |
School of Computer Engineering |
author_facet |
School of Computer Engineering Tripathi Surabhita |
format |
Final Year Project |
author |
Tripathi Surabhita |
author_sort |
Tripathi Surabhita |
title |
Meaning representation in natural language processing |
title_short |
Meaning representation in natural language processing |
title_full |
Meaning representation in natural language processing |
title_fullStr |
Meaning representation in natural language processing |
title_full_unstemmed |
Meaning representation in natural language processing |
title_sort |
meaning representation in natural language processing |
publishDate |
2014 |
url |
http://hdl.handle.net/10356/59189 |
_version_ |
1759857351408484352 |