1. SURVEY OF NATURAL
LANGUAGE PROCESSING
MD. TARIQUL ISLAM
ID: 15-98808-3
MSCS

2. ABSTRACTION
Document classification is a part of Natural language
processing. We have different methodology and technique for
processing the document classification. The purpose of this
article is to survey some papers related to document
classification. Those survey will help the researcher to
understand which will be the best approach to use for natural
language processing

3. PARAGRAPH TOPIC CLASSIFICATION
• In that article authors try to provide idea about combining
multiple natural language methodology and technique to
improve topic classification or categorization.
• Authors are using the different topic modeling with machine
learning technique
• They using 4,00000 Wikipedia articles for train dataset and
using for classified the paragraph.

4. BENEFITS OF COMBINATION OF DIFFERENT
(NLP) ALGORITHM
Naive Bayes [tf-
idf]
Common baseline model for text
classification
OvR [GloVe]
One-vs-Rest
supports multi label learning; richer feature
(GloVe)
LDA + OvR [tf] To capture latent topics more effectively

5. RESULT OF COMBINE MULTIPLE NLP
ALGORITHM

6. TEXT SEGMENTATION WITH TOPIC MODELS
This article provide idea about how to use Latent Dirichlet
Allocation (LDA) topic modeling for text segmentation algorithm,
• Improve the algorithm named TextTiling and C99.
• Authors also proposed their own methodology named
TopicTiling
• TopicTiling is simplified version of TextTiling.
• Cost effective algorithm for NLP and document
classification.

7. DATASET USAGES AND TRAINING SET
Using two popular dataset
• “Cho dataset” (Choi, F. Y. Y. (2000). Advances in domain independent linear
text segmentation. In Proceedings of the 1st North American chapter of the
Association for Computational Linguistics conference, pages 26–33, Seattle, WA,
USA)
• “Galley Dataset”(Galley, M., McKeown, K., Fosler-Lussier, E., and Jing, H.
(2003). Discourse segmentation of multi-party conversation. In Proceedings of the
41st Annual Meeting on Association for Computational Linguistics, volume 1, pages
562–569, Sapporo, Japan.)

8. METHODOLOGY AND TECHNIQUE

9. RESULT OF TEXT SEGMENTATION WITH TOPIC
MODELS

10. IDENTIFICATION OF RELATED INFORMATION OF
INTEREST ACROSS FREE TEXT DOCUMENTS
• At this article author using an approach which will present
information of interest in free text document
• and then identifying and presenting the related information of
interest of other large set of free text document.
• The goal is to find specific related items of interest within
documents whether the documents are of the same category or
not.
• The information of interest authors identified is information
related to a person, location, something at a location,
organization or group, vehicle, event, phone number, email
address, URL, social security number and domain-specific
information such as suspect, victim, license plate and driver's

11. EXAMPLE OF INTEREST OF DOCUMENTS

12. A MACHINE LEARNING APPROACH TO
IDENTIFYING SECTIONS IN LEGAL BRIEFS
• Authors was using the binary classification and segmentation
technique to classified the and identify the Legal Briefs of
different case.
• They just use the two step for classify the document those are
• Classify the header of the sections
• Predicate the text of header and body
• It is an cross-validation experiment and it shows their
approach has over 90% accuracy on both tasks.
• is significantly more accurate than baseline methods.

13. PROCESS OF SEPARATING SECTION

14. REGULAR EXPRESSION OF BASELINE
APPROACH
concatenation of the following list of parts
1. The beginning of the string
2. An optional asterisk
3. An optional Roman Numeral or Natural Number followed
by an optional period and space
4. A list of zero or more all capitalized words
5. The end of the string blocks that contain a match

15. DATA MINING: DOCUMENT CLASSIFICATION
USING NAIVE BAYES CLASSIFIER
• This article provides the Information about effectiveness of
Hierarchical Classification technique about Naïve Bayes.
• Why It is efficient then Flat Classification.
• Proposed the methodology and architecture about using Naïve
Bayes
• and how to performs better for multi label documentation
classification.
• Discuss about document classification standard.

16. STANDARD DOCUMENT CLASSIFICATION
SETUP

17. RESULT OF PROPOSED
METHODOLOGY(HIERARCHICAL CLASSIFICATION)