Controlled Natural Language Generation from a Multilingual FrameNet-based Gra...Normunds Grūzītis
We present a currently bilingual but potentially multilingual FrameNet-based grammar library implemented in Grammatical Framework. The contribution of this paper is two-fold. First, it offers a methodological approach to automatically generate the grammar based on semantico-syntactic valence patterns extracted from FrameNet- annotated corpora. Second, it provides a proof of concept for two use cases illustrating how the acquired multilingual grammar can be exploited in different CNL applications in the domains of arts and tourism.
Natural Language Processing from Object Automation Object Automation
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Natural Language Processing by Mr.Madan Kartheesan, Technical Lead, Object Automation.
Controlled Natural Language Generation from a Multilingual FrameNet-based Gra...Normunds Grūzītis
We present a currently bilingual but potentially multilingual FrameNet-based grammar library implemented in Grammatical Framework. The contribution of this paper is two-fold. First, it offers a methodological approach to automatically generate the grammar based on semantico-syntactic valence patterns extracted from FrameNet- annotated corpora. Second, it provides a proof of concept for two use cases illustrating how the acquired multilingual grammar can be exploited in different CNL applications in the domains of arts and tourism.
Natural Language Processing from Object Automation Object Automation
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Natural Language Processing by Mr.Madan Kartheesan, Technical Lead, Object Automation.
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
1. Entity Relationship Modeling
Objectives:
• To illustrate how relationships between entities are
defined and refined.
• To know how relationships are incorporated into the
database design process.
• To describe how ERD components affect database
design and implementation.
2. Entity-Relationship (ER) Modeling.
ER Modeling is a top-down approach to database
design.
Entity Relationship (ER) Diagram
A detailed, logical representation of the
entities, associations and data elements for an
organization or business
Notation uses three main constructs
Data entities
Relationships
Attributes
3. Notation
Entity Attribute Relationship
EntityName
EntityName Verb phrase
List of
Attributes
Acceptable
4. Entities
Examples of entities:
Person: LECTURER, STUDENT
Place: CLASS ROOM, WAREHOUSE
Object: MACHINE, PRODUCT, CAR
Event: REGISTRATION, LECTURE
Concept: COURSE, LECTURE
Guidelines for naming and defining entity types:
An entity type name is a singular noun
An entity type should be descriptive and specific
An entity name should be concise
Event entity types should be named for the result of the event, not the
activity or process of the event.
5. Attributes
Example of entity types and associated attributes:
STUDENT: Student_ID, Student_Name, Home_Address,
Phone_Number, B_Date
Guidelines for naming attributes:
An attribute name is a noun.
An attribute name should be unique
To make an attribute name unique and clear, each attribute name
should follow a standard format
Similar attributes of different entity types should use similar but
distinguishing names.
6. Relationship
Associations between instances of one or more entity types that is of interest
Given a name that describes its function.
• relationship name is an active or a passive verb.
Relationship name:
Lecture
Lecturer Subject
An Lecturer gives lecture on one or more Subjects
A Subject can be taught by one or more lecturer.
7. Relationship
The degree of a relationship = the number of entity
sets that participate in the relationship
Mostly binary relationships
Sometimes more
Mapping cardinality of a relationship
1 –1
1 – many
many – 1
Many-many
13. Tables
Student
PK N_ID
F_Name Subject
L_Name
Subject_Dscription
Enrollment Address
Stud_ID FK B_Date Subject_Unit
CK PK Subject_Code
Course_Name FK
CC# FK
Enroll_Date
Lecturer
Lecture
L_Address
PK CC#
L_FirstName
Subject FK
L_LastName
Time
Date L_Email
Lecturer_ID FK PK Lecturer_ID