A general purpose semantic parser using FrameNet and WordNet®.

A general purpose semantic parser using FrameNet and WordNet®.

Date: May 2004
Creator: Shi, Lei
Description: Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Contributing Partner: UNT Libraries
Refactoring FrameNet for Efficient Relational Queries

Refactoring FrameNet for Efficient Relational Queries

Date: December 2003
Creator: Ahmad, Zeeshan Asim
Description: The FrameNet database is being used in a variety of NLP research and applications such as word sense disambiguation, machine translation, information extraction and question answering. The database is currently available in XML format. The XML database though a wholesome way of distributing data in its entireness, is not practical for use unless converted to a more application friendly database. In light of this we have successfully converted the XML database to a relational MySQL™ database. This conversion reduced the amount of data storage amount to less than half. Most importantly the new database enables us to perform fast complex querying and facilitates use by applications and research. We show the steps taken to ensure relational integrity of the data during the refactoring process and a simple demo application demonstrating ease of use.
Contributing Partner: UNT Libraries
Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

Date: 2005
Creator: Shi, Lei & Mihalcea, Rada, 1974-
Description: This paper describes the authors' work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing.
Contributing Partner: UNT College of Engineering
Open Text Semantic Parsing Using FrameNet and WordNet

Open Text Semantic Parsing Using FrameNet and WordNet

Date: May 2004
Creator: Shi, Lei & Mihalcea, Rada, 1974-
Description: This article discusses open text semantic parsing using FrameNet and WordNet.
Contributing Partner: UNT College of Engineering
An Algorithm for Open Text Semantic Parsing

An Algorithm for Open Text Semantic Parsing

Date: August 2004
Creator: Shi, Lei & Mihalcea, Rada, 1974-
Description: This paper describes an algorithm for open text shallow semantic parsing.
Contributing Partner: UNT College of Engineering