Threshold Setting and Performance Monitoring for Novel Text Mining
Wenyin Tang and Flora S. Tsai
School of Electrical and Electronic Engineering
Nanyang Technological University
E-mail: wenyintang@ntu.edu.sg
, fst1@columbia.edu
May 2, 2009
1

Outline
• Introduction
– Novel Text Mining (NTM) System
– Performance Evaluation of NTM
• Adaptive Threshold Setting for NTM
– Motivations
– Our Method: Gaussian-based Adaptive
Threshold Setting (GATS)
– Experimental Result
• Conclusion
2

Overview of Novel Text Mining System
Prepare a clean data matrix which can be easily processed by a computer.
Interact with users: input documents, output novel info, preference setting and feedback.
Categorise each incoming document or sentence into its relevant topic bin.
Detect novel yet relevant documents or sentences in each
3 topic.

Novel Text Mining Algorithm
Given a set of relevant documents in a specific topic, e.g. “football games”, NTM retrieves the novel documents by:
– Step 1: rank documents in the topic “football games” in a chronological order.
D1, D2, D3, D4 …
– Step 2: assign a novelty score for each document by comparing the document with its history documents.
– Step 3: predict the document as
“novel” if its novelty score is
D1 greater than the predefined novelty threshold.
D4
Unfortunately, I am
“non-novel” because I am very similar to my nearest neighbor D3
D3
I am “novel” because I am dissimilar with my nearest neighbor D2
D2
I am “novel” because I am the first document
I am “novel” because I am dissimilar to D1
Vector space
4

NTM Performance Evaluation
• Given a set of documents D1, D2, to D10, relevant to some topic, for example,
D1, D2, D3, D4, D5, D6, D7, D8, D9, D10
System (S):
Assessor (A): novel non-novel
# Novel:
8
5
Matched (M):
4
•
Precision (P) reflects how likely the system retrieved docs are truly novel. P=M/S=4/8=0.5, i.e. 50% system retrieved docs are truly novel.
• Recall (R) reflects how likely the truly novel docs can be retrieved
• by the system. R=M/A=4/5=0.8, i.e. 80% truly novel docs can be retrieved by the system.
F
β score : the function of P and R:
F



P

1
1

R

5

Threshold Setting vs. Users’ Requirements
I want to read the most novel information in a short time 1 .
I do not want to miss any novel information 2 .
I am not sure until I can see the documents
The NTM system should define the novelty threshold based on the users’ requirements adaptively.
Different users may have different performance requirements.
1. High-precision NTM systems are desired;
2. High-recall NTM systems are desired.
6

Why Adaptive Threshold Setting
Motivations :
1. As NTM system is a real-time system, there is little or no training information in the initial stages of NTM.
Therefore, the threshold cannot be predefined with confidence.
2. As NTM system is an accumulating system, more training information will be available for threshold setting, based on user’s feedback given over time.
3. Different users may have different definitions of
“novelty”:
– One user: a document with 50% novel info
– Another user: a document with 90% novel info
7

Gaussian-based Adaptive Threshold
Setting (GATS)
Basic idea:
• GATS is a score distribution-based threshold setting method. It models the score distributions of both novel and non-novel documents (based on the user feedback);
• This parametric model provides the global information of data, from which we can construct an optimization criterion of desired performance to search the best threshold.
8

Novelty Score Distributions
Novel Gaussian probability distribution approximation
Non-novel
Empirical probability distribution and its Gaussian probability distribution approximation for TREC 2004
Novelty Track data topic N54
9

Optimization Criterion
Satisfy 2 conditions:
1. Criterion is a function of Threshold:
J=f (
θ
)
2. Criterion is directly related to system performance:
J=F
β
(
θ
)

Optimization Criterion
Novel
Non-novel
S
1 S
0
P (

)

S
1
(

S
1
)
(


)
S
0
(

)
R (

)

S
1
(

) n
1
θ
S
1
(

)
 n
1
Pr( x
 
 n
1

0
 
| c
1
) p ( x | c
1
) dx

*  arg
 max F

(

)
 arg
 max
θ
S
0
(

)
 n
0
Pr( x
 
| c
0
)

P (

)
1

1

R (


)
11

Flow Chart of NTM with GATS

Experimental Data
Sentence-level data: TREC 2004 Novelty Track data
The news providers of the document set are Xinghua English (XIE) , New
York Times (NYT), and Associated Press Worldstream (APW). The NIST assessors created 50 topics for this data. Each topic consists of around 25 documents. These documents were ordered chronologically and then segmented into sentences. Each sentence was given an identifier and concatenated together to form the target sentence set. In this data, the overall percentage of novel sentences is around 41.4%. The statistics of data is summarized in Table 1.
Table 1 Statistics of TREC 2004 Novelty Track data
#Novel
Relevant 3454
#Non-novel
4889
Sum
8343
(41.4%) (58.6%)
13

Experimental Data
Document-level data: APWSJ
APWSJ consists of news articles from Associate Press (AP) and Wall Street
Journal (WSJ), which cover the same period from 1988 to 1990 [Zhang et al.,
2002]. There are 50 TREC topics from Q101 to Q150 in this data and 5 topics (Q131, Q142, Q145, Q147, Q150) that lack non-novel documents are excluded from the experiments. The statistics of this data are summarized in
Table 2.
Table 2 Statistics of APWSJ data
Relevant
#Novel
10,839
(91.1%)
#Non-novel Sum
1057 11,896
(8.9%)
14

Methods & Parameters
• Baseline:
– Fixed threshold setting θ from 0.05~0.95 with an equal step 0.05.
• Our method, GATS:
– Complete feedback : with β from 0.1~0.9 with an equal step 0.1.
– Partial feedback : with β from 0.1~0.9 with an equal step 0.1, percentages of feedback:
10%, 20%, 50% and 80%.

Experimental Result
Sentence-Level NTM on TREC 2004 Data
Recall
16

Experimental Result
Document-Level NTM on APWSJ Data
Redundancy-Recall
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Comparison: GATS vs. Fixed Threshold
• For precision-recall tradeoff
– Fixed threshold θ cannot reflect the tradeoff of the precision and recall directly.
– GATS parameter β reflects the weights of precision and recall directly.
• Under various performance requirements, GATS is able to approximate the best fixed threshold.
Table 3 Comparison of F
β on TREC 2004 Novelty Track data

Experimental Result
Sentence-Level NTM on TREC 2004 Data
Recall
PR curves of GATS (tuned for F
β the user’s feedback.
) with different percentages of
19

Experimental Result
Document-Level NTM on APWSJ Data
Redundancy-Recall
R-PR curves of GATS with different percentages of the user’s feedback.
20

Conclusion
• A Gaussian-based Adaptive Threshold Setting (GATS) algorithm was proposed for NTM system.
• GATS is a generic method, which can be tuned according to different performance requirements varying from high-precision to high-recall.
• By testing the proposed method on both document and sentence-level datasets, we found the experimental results showed the promising performance of GATS for a real-time NTM system.
21

Q & A
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