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A customizable keyword extraction package.

Project description

.. role:: math(raw)
:format: html latex

Discovery and Representation of Open Making Related Terms

Bulent Ozel, UZH



The first objective of this module is to provide a customizable and
standardized text preprocessing prior to further analyses where more
advanced machine learning and or statistical techniques can be applied
and compared with each other. In that sense, it provides a pipelined set
of functionalities (i) to be able to inspect, organize, prune and merge
texts around one or very few specific theme(s) or topic(s), (ii) remove
unwanted terms or literals from the texts, (iii) tokenize the texts,
(iv) count the terms in texts, and (v) when desired stem the tokenized

The second objective of this module is to be able compare or score a
foreground corpus or a specific corpus against a background corpus or
reference corpus. Example use cases could be, for instance, exploring
the language of a sub-culture, a community, or a movement looking at to
what extend the specific use of the language of the group differentiates
itself from the common language.

In cases when there are more than a few number of themes or topics, and
where each topic is represented with a large set of documents that
validates the employment of standardized matrix decomposition based
methodologies, then the scoring option of this module can be skipped
entirely. More specifically, in use cases where the objective is being
able to classify and differentiate a number of topics or issues from
each other and where there are sufficient data that fulfills the
underlining assumptions of NMF, LDA or LSI based approaches, then tools
from, for instance, Python’s
`sklearn.decomposition <>`__
package are suggested.

Nevertheless, the outputs of this module, such as its normalized term
frequencies or the specificity scores it associates to them regarding to
a reference background corpus, can be used as input to other matrix
decomposition techniques.


Support for this work is partly covered by the OpenMaker Project:

Collaborators(s): \* Hamza Zeytinoglu



A. Via Python's standard distribution channel PyPI

.. code:: bash

pip install omterms

B. Via from its GitHub source

.. code:: bash

git clone

.. code:: bash

cd omterms

.. code:: bash

pip install .

A quick use

.. code:: python

>>> from omterms.interface import *
>>> extract_terms("Some input X text to process less then 3 seconds.").head()
Configuring the text cleaner ...
A single text is provided.
Extracting the terms ...
Tokenizing the input text ..
Done. Number of terms: 10
Cleaning process: Initial size of tokens = 10
Reduction due to punctuations and stopwords = 3.
Reduction due to all numeral terms = 1
Reduction due to short terms = 1
Reduction due to rare terms = 0
Reduction due to partially numeral terms = 0
Reduction due to terms with not allowed symbols = 0
The total term count reduction during this cleaning process = 5
Percentage = 50%
TF Term wTF
0 1 input 0.2
1 1 text 0.2
2 1 process 0.2
3 1 less 0.2
4 1 seconds 0.2

More on usage

`Please see the
tutorial. <>`__



Roadmap on Keyword and Keyphrase Extraction

The method outlined here aims to set-up a base line for future

- It uses a statistical approach combined with standardized procedures
that are widely applied in standard NLP workflows.
- In this base line, it aims to present a work flow that can be ablied

- different languages
- differrent problem domains
- analysis on a single theme with limited training set

1. Overall work flow

In short, the workflow presented on this notebook is the second stage on
a workflow objective of which is being able to measure relevance of a
given external input to a specific theme, issue or topic. The steps of
the work flow is as follows.

1. Forming a specific corpus where the corpus consists of set of
documents around a topic. The corpus could be

- a set of blog articles around an issue let say green finance
- or a set of Wikipedia articles around the same subject
- or collection of news articles around the green finance
- or collection of tweets around the same issue.

At the moment we have another module that given a set of seed
Wikipedia articles around an issue the crawler scrapes textual data
from articles. For the details of the module please `see the scraper
module. <>`__.
The output of that module is a set of input texts stored in a
collection in JSON format.

2. Given an input set of texts on a theme a concept or a topic identify
set of terms that more likely or less likely can occur within a
discussion on the topic. This module hereby presents one of the
simple methods for this purpose.

3. Given a list of weighted terms which are more likely to occur or
reprsent a theme, concept or topic and input query text measure the
relevance of the input text to the topic/theme/concept. `The notebook
in this
link <>`__
demonstrates one way doing such scoring of a given text against the
curated set of terms of this particular module.

2. Suggested future work

- Comparing and combining this comparison based scoring with matrix
decompostion based topic modelling approaches such as NMF, LDA, LSI.

- Using language specicif term frequency counts of Wikipedia itself for
comparisons. In NLP terminology, the *foreground* corpus around a
topic needs to be compared and contrasted to a *background* corpus.

- Improving the semantic crawler of the previous stage to be able to
increase quality of the specific corpuses

Methodological Improvements

- Instead of tokenizing all terms, examine possibilities of key-phrase
extrcation combining with *tf-idf* and

- experiment with extracting noun phrases and words, for this use
NLTK's regular expression module for POS (part of speeach)
- extract n-grams where n=1,2,3

3. Definitions and assumptions


- At the comparison stage, it is assumed that a document's terms tend
to be relatively frequent within the document as compared to an
external reference corpus. However, it should be noted this
assumption is contested in the field. See the paper by Chuang et el.

- Condidering the fact that the crawler is used to aggregate
semantically related set of documents into a single document, *tf x
idf* is equivalent to *tf*. As can be seen below, we use a normalized
version of *tf*: *ntS / NS*.

- Fewer number of but relatively more relevant training (input corpus)
is prefered in order to reduce term extraction problems due to length
of documents. However, it should be noted that the crawling depth of
an identiefied wiki article from stage 1 of this document can be used
as an additional weight on relevance/reprsesntation of keywords.

- We have limited ourselves to terms instead of n-grams and phrases or
use of POS to be able to develop a base model that can work on
different languages.


Given for instance a set of texts around open source software movement a
term that is identified can be a word such as *openness*, a person such
as *Stallman* a license type such as *GNU*, an acronym for an
organization such as *FSF* the Free Software Foundation, or a technology
such as *Emacs*.

Likelihood ratio

It is a simple measure computed comparing frequency count of a term in a
specific corpus versus its frequency count in the reference reference
corpus. Here assumption is that the reference corpus is a large enough
sample of the language at observing the occurance of a term. Then having
a higher/lower observation frequency of a term in the specific corpus is
a proxy indicator for the term choice while having a debate on the

The likelihood ratio for a term :math:`P_t` is calculated as:

:math:`P_t = log ( (ntS/NS) / (ntR/NR) )`


- *ntS* is the raw frequency count of the term in the entire specific
- *ntR* is the raw frequenccy count of the term in the reference corpus
- *NS* is the total number of terms in the specific corpus
- *NR* is the total number of terms in the reference corpus

It should be noted that frequency counts are calculated after having
applied the same tokenization and post processing such as excluding
stop-words, pancuations, rare terms, etc both on the reference corpus
and the specific corpus.

4. Some thoughts on a conceptual approach at using the extracted keywords or phrases to predict topical relevance of a new text

Using the outcome of this technique to score arbitrary input texts
against a single issue such as financial sustainability or against a set
of issues such as the 10 basic human values requires a set of
normalization of the raw scores and their rescaling/transformation.

The factors that need to be considered are:

- **Differing document lengths:** The likelihood of repetion of a key
phrase increases as the size of the input text gets larger. In more
concrete terms, when a scoring that simply sums up detection of
weighted keyphrases or words within a given input text would be very
sensitive to the document length. For isntance, the an executive
summary of an article would very likely get quite lower score than
the full article on any issue.

*Among other methods, this can simply be resolved by computing per
word scores, where the word set to be conidered is the tokenized and
cleaned set of words that represent the input text.*

- **Topical relevance:** This factor would be important when the
subject matter of the inputs texts vary among each other. In other
words, this factor would matter to a very high significance, let's
say when one wants to compare perceptions of indivuduals on the role
of traditions in the personal lives and when this question is not
asked them in a uniform manner that under the same social, cultural,
environmental and physical conditions.

Let’s assume that issue under investigation is again the perception
and role of traditions in personal lives. It is possible that the
same blogger with a strong opinion on traditions (i) may not touch
the issue while talking on data science, (ii) he would slightly touch
the issue while he talks about her preferences in mobile devices
(iii) He dives into subject using all keywords and phrases when he
talks about impact of traditions on social order. In brief, it is
necessary to offset the variability of the topical relavance of an
input text to the issue under investigation when arbitrary text
samples are used for scoring.

*An offsetting scheme can be devised when opinion or perception of an
actor is to be measured with respect to more than one factor that
define the issue under investigation. For instance, when we want to
measure the position of a political leader on individual liberties vs
social security or when we want to profile discourse of the political
leader as of a ten basic human values we could employ some simple
statistical methods in order to offset the topical relevance of the
discourses or the speeches of the political figure to what we would
like to measure.*

*A simple method could be rescaling the scores on each sub factor
such as the scores of liberty and security that are measured from the
same speech into a range of -1 to 1. This can simply be done by
taking the mean of the two and then deducting the mean from each
score and scaling them into a scala of -1 to 1. This way it may be
possible to use multiple speeches of the same political figure on
different topics to evalaute his or her postion on liberty vs
security matter.*

In statistical terms this problem corresponds to adjusting or
normalizing ratings or scores measured on different scales to a
notionally common scale. Given the fact that in most cases a normal
distribution for underlying factors may not be assumed the
quantile-normalization technique is suggested. The quantile
normalization sorts and ranks the variables with a non-negative
amplitudes. Then these rankings can be scaled to for instance to a 0-1

- **Level of subjectivity**. This is variability in terms of relevant
importance attributed to each issue given a set of issues. For
instance, according to a study (Schwartz 2012), it is seen that
almost all cultures around the world attach a higher importance to
‘universalism’ human value than the ’power’ basic human value. So
when the objective of the scoring is not simply to rank of importance
attached to each of them, then a comparative importance with respect
to overall observations needs to be tackled.

*Observed variances in each query texts can be considered. That is, a
simple statistical methods can be used for instance to be able to
compare two or more query texts with respect to each other. A
suggested method would be (1) estimate coefficient of variation for
each input text using per-word scores (2) the rescale
quantile-normalized scores that is suggested above using the
estimated coefficient of variation in each case.*

*When this rescaling is applied for instance to universalism versus
power or liberty vs security the coeffcient of variation would act as
a polarization measure.*

Scoring and value system profiling

When one attempts to use scores, for instance, around the basic ten
human values and one wants to construct the value system of the person,
then both ranking of the scores as well as the relevant importance of
each score from a number of texts from the same person should be taken
into consideration.

5. State of the art

- Survey Paper: Kazi Saidul Hasan and Vincent Ng, 2014. “Automatic
Keyphrase Extraction: A Survey of the State of the Art” Proceedings
of the 52nd Annual Meeting of the Association for Computational
Linguistics, pages 1262–1273.

- Survey Paper: Sifatullah Siddiqi and Aditi Sharan. Article: Keyword
and Keyphrase Extraction Techniques: A Literature Review.
International Journal of Computer Applications 109(2):18-23, January

- Survey Paper: Z. A. Merrouni, B. Frikh, and B. Ouhbi. Automatic
keyphrase extraction: An overview of the state of the art. In 2016
4th IEEE Colloquium on Information Science and Technology (CiSt),
pages 306–313, Oct 2016

- PageRank - Topical: Zhiyuan Liu, Wenyi Huang, Yabin Zheng and Maosong
Sun, 2010. “Automatic Keyphrase Extraction via Topic Decomposition”.
Proceeding EMNLP '10 Proceedings of the 2010 Conference on Empirical
Methods in Natural Language Processing Pages 366-376

- RAKE (Rapid Automatic Keyword Extraction ): Stuart Rose, Dave Engel,
Nick Cramer, and Wendy Cowley. Automatic keyword extraction from
individual documents. Text Mining, pages 1–20, 2010.

- TextRank - Graph Based : Rada Mihalcea and Paul Tarau. Textrank:
Bringing order into texts. Association for Computational Linguistics,

- STOPWORDS: S. Popova, L. Kovriguina, D. Mouromtsev, and I. Khodyrev.
Stopwords in keyphrase extraction problem. In 14th Conference

- Corpus Similarity - Keyword frequency based: Adam Kilgarriff. Using
word frequency lists to measure corpus homogeneity and similarity
between corpora. In Proceedings of ACLSIGDAT Workshop on very large
corpora, pages 231–245, 1997.

- Recommendation - Keyphrase Based: F. Ferrara, N. Pudota and C. Tasso.
A keyphrase-based paper recommender system. In: Digital Libraries and
Archives. Springer Berlin Heidelberg, 2011. p. 14-25.

- Jason Chuang, Christopher D. Manning, Jeffrey Heer, 2012. "Without
the Clutter of Unimportant Words": Descriptive Keyphrases for Text
Visualization" ACM Trans. on Computer-Human Interaction, 19(3), 1–29.

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