TopicTrend

TopicTrend

By: Jovian Lin

Discover Emerging and Novel Research Topics

Introduction

Formulating a research idea is the 1st step for success in academia.

A worthy research idea must be original and innovative.

In order to come up with innovative research ideas, researchers have to read a lot of published articles…

… which is time-consuming.

“Is there any shortcut to success?” “No.”

“There are efficient ways to achieve success”

Search Engines in Digital Libraries:

Search engines support information seeking and retrieval.

Introduction

SearchEngine

“Search Query”

List of titles (of articles)

Search

Results

Search engines support information seeking and retrieval.

However, is this enough for the junior researcher?

Introduction

FYP students 1st year PhD students

• Define a research topic (from zero knowledge)• Help in survey• Identify emerging/new research areas to explore• Determine related topics

How useful is this result to the junior researcher?

Junior researchers want:Understand research topics and trends.Recognize HOT topics.Understand how topics interact and influence research activity.

Problem Definition


Problem Definition

Enter a search query

View results

Select a few articles to read

Extract new terms fromselected article

CurrentInefficient

Method

Search

Results

Information overload !


Problem Definition

Enter a search query

View results

Select a few articles to read

Extract new terms fromselected article

CurrentInefficient

Method


Problem Definition

Enter a search query View results

DesiredEfficient Method

Visualization of the research topics

List of HOT research topics (related to the search query)

Do it quick!TopicTrend

Quick Demo

Recruited 4 participants.

Participants:Tested TopicTrend using queries from their respective domains.Rated TopicTrend’s output (w.r.t. their query). [Quantitative]Filled up a questionnaire. [Qualitative]

Evaluation

• Chemistry / PhD• Engineering (Transportation) / PhD• Comp Science (AI) / PhD• Engineering / FYP

Evaluation

Topic ATopic BTopic CTopic DTopic ETopic FTopic GTopic H

10111111

Topic I 1Topic J 1

Score

9/10

Topic A

Topic B

Topic CTopic D

Topic E

Topic F

Topic G

Topic HTopic I

Topic J

“machine learning”

Evaluation

Average score = 68.125%

Quantitative

Evaluation

Questionaire using Five-Point Likert Scale.

1=Disagree, 5 =Agree.

Some examples:“The system was easy to use.”“The system gave interesting results.”“I was able to get a better understanding of the topics.”“I was able to discover trends.”“I was able to discover relationships between topics.”“I was able to discover potential, novel topics.”

Details in Project Report.

Qualitative

4.75 / 5 4 / 5

4 / 54 / 5

4 / 54 / 5

ConclusionTopicTrend is a visualization tool that helps junior researchers:

Understand research topics and trends.Recognize HOT topics.Understand how topics interact and influence research activity.

However, results were mediocre Due to presence of stop phrases (e.g., “problem set”, “proposed model”, etc)

Solutions and Future Work:TF-IDF weight — don’t have to manually enter stop words.

Statistical measure to evaluate how important a word is.The importance increases to the number of times a word appears in the document...But is offset by the frequency of the word in the corpus.

Latent Dirichlet Allocation (LDA) – view each abstract as a mixture of topics. (David Blei)Online LDA – find topics faster than normal LDA; analyze in a stream.Dynamic Topic Models (DTM) – captures the word evolution of each topic over time.

Search by exemplar (instead of search by keyword)Benefits users who have difficulty expressing their query.

ConclusionTopicTrend is a visualization tool that helps junior researchers:

Understand research topics and trends.Recognize HOT topics.Understand how topics interact and influence research activity.

However, results were mediocre Due to presence of stop phrases (e.g., “problem set”, “proposed model”, etc)

Solutions and Future Work:TF-IDF weight — don’t have to manually enter stop words.

Statistical measure to evaluate how important a word is.The importance increases to the number of times a word appears in the document...But is offset by the frequency of the word in the corpus.

Latent Dirichlet Allocation (LDA) – view each abstract as a mixture of topics. (David Blei)Online LDA – find topics faster than normal LDA; analyze in a stream.Dynamic Topic Models (DTM) – captures the word evolution of each topic over time.

Search by exemplar (instead of search by keyword)Benefits users who have difficulty expressing their query.

Thank You

Backup Slides

OpenNLP — a machine learning based toolkit for theprocessing of natural language text.

Used OpenNLP to retrieve a list of NPs.

Implementation

OpenNLPToolsAn article

1. Sentence Detection2. Tokenization3. Part-of-Speech (POS) Tagging4. Chunking and Retrieving NPs

NP A

NP B

NP C

NP D

NP E

NP F

Sentence Detection

Implementation

Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29. Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group. Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC, was named a director of this British industrial conglomerate. Those contraction-less sentences don't have boundary/odd cases...this one does.

• Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.

• Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group.

• Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC, was named a director of this British industrial conglomerate.

• Those contraction-less sentences don't have boundary/odd cases...this one does.

Tokenization

Implementation



• [Pierre] [Vinken] [,] [61] [years] [old] [,] [will] [join] [the] [board] [as] [a] [nonexecutive] [director] [Nov.] [29] [.]

• [Mr.] [Vinken] [is] [chairman] [of] [Elsevier] [N.V.] [,] [the] [Dutch] [publishing] [group] [.]

Part-of-Speech Tagging

Implementation



• [NNP] [NNP] [,] [CD] [NNS] [JJ] [,] [MD] [VB] [DT] [NN] [IN] [DT] [JJ] [NN] [NNP] [CD] [.]

• [NNP] [NNP] [VBZ] [NN] [IN] [NNP] [NNP] [,] [DT] [JJ] [NN] [NN] [.]

Text Chunking and Extracting NPsText chunking consists of dividing a text in syntactically correlated parts of words.Uses the Tokenization and POS Tagging data.For example:

He reckons the current account deficit will narrow to only # 1.8 billion in September.

Becomes:

[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ] [PP in ] [NP September ] .

Implementation

Text Chunking and Extracting NPsText chunking consists of dividing a text in syntactically correlated parts of words.Uses the Tokenization and POS Tagging data.

Implementation

Note the:• B-Chunk• I-Chunk

OpenNLP — a machine learning based toolkit for theprocessing of natural language text.

Used OpenNLP to retrieve a list of NPs.

Implementation

OpenNLPToolsAn article

1. Sentence Detection2. Tokenization3. Part-of-Speech (POS) Tagging4. Chunking and Retrieving NPs

NP A

NP B

NP C

NP D

NP E

NP F

An algorithm to calculate the score of a NP.

Implementation

NP A

NP B

NP C

NP D

NP E

NP F

# (0 ~ 2 years)

# (2 ~ 4 years)

# (4 yrs & beyond)

10

2

1

Score = 10 + 1

10 + 2 + 1 + 20

= 11

33 = 0.333

# (0 ~ 2 years)

# (2 ~ 4 years)

# (4 yrs & beyond)

1

2

10

Score = 1 + 1

1 + 2 + 10 + 20

= 3

33 = 0.090

An algorithm to calculate the score of a NP.

Implementation

NP A

NP B

NP C

NP D

NP E

NP F

Re-rank the list of NPs base on the score.

Implementation

Re-rankNP B

NP D

NP E

NP C

NP A

NP F

New!NP A

NP B

NP C

NP D

NP E

NP F

Implementation

Calculate the relationship strength between NPs byconsidering the common articles (PIIs) that they have.

The more articles they have in common, the thicker the edge.

The End

Documents

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