Content Words (CWs) are important segments of the text. In text mining, we utilize them for various purposes such as topic identification, document summarization, question answering etc. Usually, the identification of CWs requires various language dependent tools. However, such tools are not available for many languages and developing of them for all languages is costly. On the other hand, because of recent growth of text contents in various languages, language independent text mining carries great potentiality. To mine text automatically, the language tool independent CWs finding is a requirement. In this research, we devise a framework that identifies text segments into CWs in a language independent way. We identify some structural features that relate text segments into CWs. We devise the features over a large text corpus and apply machine learning-based classification that classifies the segments into CWs. The proposed framework only uses large text corpus and some training examples, apart from these, it does not require any language specific tool. We conduct experiments of our framework for three different languages: English, Vietnamese and Indonesian, and found that it works with more than 83% accuracy.

1. LiCord: Language Independent Content Word Finder
Md-Mizanur Rahoman, Tetsuya Nasukawa, Hiroshi Kanayama &
Ryutaro Ichise
April 18, 2016

2. Background
currently 100s of languages are available, only few of them can be
automatically mined because of low or no NLP-resources availability
creating NLP-resources for all languages is not feasible
Content Words finding system for languages can be considered
basic NLP-resource
Rahoman et.al., | LiCord | 2

3. Content Word
definition: Content Words [ref: American Heritage Dictionary]
are nouns, most verbs, adjectives, and adverbs that refer to some
object, action, or characteristic
carry independent meaning
are usually open i.e, new words can be added
example: “NO8DO” is the official motto of Seville.
usage: Content Words can be used
(new) topic identification
document summarizing
question answering etc.
Rahoman et.al., | LiCord | 3

4. Problem & Possible Solution
problem
Content Words finding requires language dependent NLP-resource
language parser
parallel corpora etc.
NLP-resource developing for all language is costly and “not feasible”
possible solution
morphological features of text segment can classify whether a segment
is Content Word
machine learning model can classify text segment into Content Word
big text corpus can generate balanced morphological features for such
text segments
Rahoman et.al., | LiCord | 4

5. System Framework
the model generation has four processes:
NGram Constructor − perform text segmentation
Function Word Decider − devise feature values for the segments
Feature Value Calculator − devise feature values for the segments
Classifier Learner − generate classification model to decide the
segments into Content Words
Rahoman et.al., | LiCord | 5

6. System Framework
the model generation has four processes:
NGram Constructor − perform text segmentation
Function Word Decider − devise feature values for the segments
Feature Value Calculator − devise feature values for the segments
Classifier Learner − generate classification model to decide the
segments into Content Words
Rahoman et.al., | LiCord | 6

7. 1.NGram Constructor
segment text and construct variable token (length) n-grams
calculate n-gram frequencies
Table: Variable length n-grams and their frequencies for an exemplary
corpus T- = “Japan is an Asian country. Japan is a peaceful country”.
n-grams and frequencies over the T-
size 1 n-gram {[Japan−2], [is−2], [an−1], ..., }
(/uni-gram) [country−2], [a−1], ... }
size 2 n-gram {[Japan is−2], [is an−1], ..., }
(/bi-gram) [Asian country−1], ...}
size 3 n-gram {[Japan is an−1], [is an Asian−1], }
(/tri-gram) [an Asian country−1], ... }
Rahoman et.al., | LiCord | 7

8. System Framework
the model generation has four processes:
NGram Constructor − perform text segmentation
Function Word Decider − devise feature values for the segments
Feature Value Calculator − devise feature values for the segments
Classifier Learner − generate classification model to decide the
segments into Content Words
Rahoman et.al., | LiCord | 8

9. 2.Function Word Decider
Function Words
express grammatical relationships with other words
have little lexical meaning or have ambiguous meaning
are frequent n-grams over a text document
example: “the”, “in”, “in spite of” etc.
decide by
pick a threshold number of frequent n-grams
map frequent n-grams with available translation of known Function
Words
use threshold only, if translation service is not available
n-gram # of token frq frq%
the 1 3124631 67.60
in 1 1774988 38.40
... ... ... ...
united states 2 43698 0.94
... ... ... ...
Rahoman et.al., | LiCord | 9

10. System Framework
the model generation has four processes:
NGram Constructor − perform text segmentation
Function Word Decider − devise feature values for the segments
Feature Value Calculator − devise feature values for the segments
Classifier Learner − generate classification model to decide the
segments into Content Words
Rahoman et.al., | LiCord | 10

11. 3.Feature Value Calculator
select fifteen different morphological features of text & calculate
their values for n-grams over a big corpus
where the n-grams appear i.e., begining/mid/end part of the sentences
how frequent the n-grams appear in a corpus
how the n-grams get added with Function Words, punctuation
etc.
Rahoman et.al., | LiCord | 11

12. System Framework
the model generation has four processes:
NGram Constructor − perform text segmentation
Function Word Decider − devise feature values for the segments
Feature Value Calculator − devise feature values for the segments
Classifier Learner − generate classification model to decide the
segments into Content Words
Rahoman et.al., | LiCord | 12

13. 4.Classifier Learner (1/2)
construct frequency-range-wise classification models
Reason
consume a large amount of time, if all n-grams are used as training
example
does not represent entire dataset, if randomly picked
assume same frequency n-grams shares same kind of morphological
features (over the corpus)
Rahoman et.al., | LiCord | 13

14. 4.Classifier Learner (2/2)
construct frequency-range-wise classification models
Method
collect range-based n-grams
X(i,j) = {x | x ∈ N ∧ i ≤ frq(x) ≤ j}
N = all n-grams in corpus, x = n-gram
select threshold number of n-grams as training n-grams for each range
calculate features for each range-wise selected n-grams
learn classification model for each range training n-grams
Rahoman et.al., | LiCord | 14

15. Experiment
check whether LiCord can identify Content Words language
independently
analyzed language − English, Vietnamese, and Indonesian
used training resource − Wikipedia Pages & Wikipedia Titles
+ve: when n-gram (text segment) exists on Wikipedia Title.
E.g., Seville, official motto etc.
-ve: otherwise.
E.g.“NO8DO” is, is the etc.
classification algorithm − Support Vector Machine and C4.5
(tree-based algorithm)
Rahoman et.al., | LiCord | 15

16. Language Independent Content Word Finding (1/2)
testing method − check test n-grams whether they are Content
Words
Table: CW finding accuracy %
Frequency English Indone- Vietnam-
Range sian ese
(1,1) 76.68 90.56 90.30
(2,2) 83.00 93.20 94.15
(3,4) 84.37 94.23 94.76
(5,9) 83.87 95.89 93.97
(10,14) 87.09 96.15 94.95
Average 83.25 93.80 93.54
Rahoman et.al., | LiCord | 16

17. Language Independent Content Word Finding (2/2)
Newly discovered Content Words finding accuracy %
Frequency English Indone- Vietnam-
Range sian ese
(1,1) 27.90 11.34 10.63
(2,2) 45.00 18.54 25.00
(3,4) 52.11 24.45 27.56
(5,9) 50.34 25.56 30.88
(10,14) 61.90 29.89 35.13
Average 47.45 21.95 22.50
finding − checking of a large number of sentences for their specific
morphological features over a big corpus can generate machine
learning model to find Content Words
Rahoman et.al., | LiCord | 17

18. Conclusion
language independent way Content Word finding a requirement in
current days’ text mining
we propose a supervised Machine Learning technique to classify
text segments to Content Words
experiment results show proposed methods can serve as a Content
Word finder
Rahoman et.al., | LiCord | 18

19. Question & Suggestion
Md-Mizanur Rahoman, mizan@nii.ac.jp
Rahoman et.al., | LiCord | 19

20. Experiment 1 (1/2)
purpose − whether LiCord can identify NEs (Named Entities), and
act like sentence parser
identifying NEs − executed for some test sentences, compared with
Wikifier and Spotlight
Table: Comparison for LiCord
with Wikifier
Recall
Wikifier 33.33%
LiCord 90.47%
Table: Comparison for LiCord
with Spotlight
Recall
Spotlight 83.33%
LiCord 91.66%
Rahoman et.al., | LiCord | 20

21. Experiment 1 (2/2)
acting as parser − executed for some test sentences, compared with
Stanford parser for Content Words
Table: Comparison for LiCord with Parser
Language Recall
English 92.30%
finding − checking of a large number of sentences for their specific
morphological features over a big corpus can support word
segmenting
Rahoman et.al., | LiCord | 21