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I have large text documents (150K lines per document; 160 documents). Then I read them in as a large VCorpus and convert them to a dataframe it runs quite quickly. Now, I want to separate each sentence in rows and remove those that do not contain a certain keyword. Then I run the code R crashes. If I try it with one document the code runs for approximately 10 minutes.
The data (text) looks like this (just with much more text):
| Company1 | Company1 hat engaged in child safety in South Africa. The Community rewards this with great recognition. Charity is a big part of the companies culture.
| Company2 | Company2 opened up several factories in the that do not pay the minimum wage. Affordable Housing is not one of thair priorities.
There is also a small example structure below.
library(readr)
library(qdap)
library(tm)
corp <- VCorpus(DirSource("test"))
text <- as.data.frame(corp)
names(text)[1] <- 'doc_id'
names(text)[2] <- 'text'
text <- dput(text)
text <- structure(list(doc_id = c("Company1.txt", "Company2.txt", "Company3.txt"
), text = c("Acucap's market cap on listing in 2002 was a meagre R372m, which would make it one of the smallest stocks in the real estate sector in today's terms. This may be a reflection of how the real estate sector of the JSE has evolved over the past 10 years. But, it is also amazing that Acucap - at a current market cap of more than R7bn - has managed to retain much of the same level of entrepreneurial spirit as at the time of its listing. Still at the helm is sector veteran Paul Theodosiou. The team has grown since listing. The core operational supporting team is Jonathan Rens, Gavin Jones and Craig Kotze, and the finance team is led by founding chief financial officer Baden Marlow. Acucap's physical portfolio is concentrated in retail, which comprises 74%, offices make up 24% and industrial takes the remaining 2%. Some of the larger retail properties include Festival Mall (the largest single asset in the portfolio) in Kempton Park, Bayside Mall in Tableview and Keywest shopping centre in Krugersdorp. The office portfolio includes assets like Mowbray Golf Park in Pinelands in the Cape, the Microsoft offices in Bryanston and 82 Grayston Drive in the Sandton CBD. Though small in Acucap's portfolio, the industrial portfolio is of a high quality and offers good scope for growth as expansion continues at the N1 Business Park in Midrand and the Montague Business Park in Cape Town. Acucap's other investments include a 17,2% shareholding in Sycom Property Fund (a fellow listed real estate property company) as well as a 100% shareholding in Sycom's management company. Sycom's other significant shareholder is Hyprop Investments. The latter's 33,9% stake in Sycom has been the subject of much speculation over the past few years as the ownership structure has created somewhat of an impasse. Acucap has, however, stated its strategic intent to retain Sycom as a separate fund. Company1 hat engaged in child safety in South Africa. The Community rewards this with great recognition. Charity is a big part of the companies culture.",
"Australian copper producer Aditya Birla Minerals Ltd. said June 16 that there is a risk that the overall reserves at its Nifty copper mine in Western Australia may be adversely affected due to the effects of an earlier ground collapse at the mine. The company also said it will be unable to provide its annual reserve update until all investigative activities are finalized. Aditya Birla is expecting site costs for the June quarter to be in the range of A$17 million to A$19 million, higher than the earlier estimate of A$12 million to A$15 million due to higher-than-expected mine activity, unplanned maintenance on critical infrastructure and additional employee costs. The company was forced to halt operations at the Nifty mine in March following suspected underground subsidence, which led to the standing down of 350 mine employees until at least July 15. Phase two of probe drilling, which is being undertaken as part of efforts to assess mine safety, is progressing ahead of schedule and is now expected to be completed by the end of June instead of mid-July. Aditya Birla said preliminary observations from this second phase are similar to those from phase one drilling, in that there is less water being intersected between levels 16 and 20 than previously expected. Areas with potential to self-propagate into new sinkholes are being reviewed and, as Aditya Birla expected, the rock mass strength has deteriorated on top of mined-out areas. Management has begun an initial review of the results to identify new gaps and/or the need for additional confirmatory drilling. The first phase of the seismic system installation has been commissioned and is fully functional, albeit limited in coverage to upper parts of the mine. The pit has been dewatered with residual mud left at the pit sump. Aditya Birla added that while surface cracking has continued to develop in the area affected by the sinkhole, this was expected and does not present a hazard. However, washouts will occur after heavy rainfall, leading to widening and deepening of the cracks.",
"In preparation for the potential completion of the merger with AGL Resources Inc., the board of directors of Nicor Inc. announced a special pro rata dividend. The dividend is contingent upon the merger being completed prior to Nicor's next scheduled dividend record date, Dec. 31, to ensure that \"shareholders continue to receive a dividend at the current rate until the closing of the merger,\" the company said. In a Nov. 1 news release, Nicor said its board of directors declared a pro rata dividend of 0.5 cent per share per day from Oct. 1 until and including the day before the merger effective date. The dividend is the daily equivalent of the current quarterly dividend rate of 46.5 cents per share. It will be paid to Nicor shareholders of record at the close of business on the day immediately before the effective merger date, which is expected in the fourth quarter. \"The dividend will be paid as soon as practical following the completion of the merger,\" Nicor said. \"This pro rata dividend is in addition to the previously announced Nov. 1 dividend that was paid to shareholders of record Sept. 30. Following the merger closing, AGL Resources is expected to pay a dividend at the rate of $0.004945055 per share, per day for the remainder of the current quarterly dividend period. These dividend payment scenarios, together with a similar plan announced today by AGL Resources, will synchronize the companies' dividends as of the merger effective date in accordance with the merger agreement.\" If the merger is not completed by Dec. 31, Nicor shareholders of record Dec. 31 will receive the regular quarterly dividend of 46.5 cents per share, payable Feb. 1, 2012, and a new pro rata dividend will be announced to ensure that shareholders receive a dividend at the current rate until the merger is completed. Company2 opened up several factories in the that do not pay the minimum wage. Affordable Housing is not one of thair priorities."
)), class = "data.frame", row.names = c(NA, 3L))
options(stringsAsFactors = FALSE)
Sys.setlocale('LC_ALL','C')
library(dplyr)
library(tidyr)
sckeywords <- c("Affordable Housing", "Benefit The Masses",
"Charitability", "Charitable", "Charitably", " Charities ", " Charity ")
pat <- paste0(sckeywords, collapse = '|')
text2 <- (text) %>%
separate_rows(text, sep = '\\.\\s*') %>%
slice({
tmp <- grep(pat, text, ignore.case = TRUE)
sort(unique(c(tmp-1, tmp, tmp + 1)))
})
Can I make it run faster somehow without needing more hardware capacity?
I have 16 GB of RAM and a 4 core CPU (i5-10210U).
There are several ways to increase the speed:
I would use data.table instead of dplyr for this amount of data
text <- structure(list(doc_id = c("Company1.txt", "Company2.txt", "Company3.txt"
), text = c("Acucap's market cap on listing in 2002 was a meagre R372m, which would make it one of the smallest stocks in the real estate sector in today's terms. This may be a reflection of how the real estate sector of the JSE has evolved over the past 10 years. But, it is also amazing that Acucap - at a current market cap of more than R7bn - has managed to retain much of the same level of entrepreneurial spirit as at the time of its listing. Still at the helm is sector veteran Paul Theodosiou. The team has grown since listing. The core operational supporting team is Jonathan Rens, Gavin Jones and Craig Kotze, and the finance team is led by founding chief financial officer Baden Marlow. Acucap's physical portfolio is concentrated in retail, which comprises 74%, offices make up 24% and industrial takes the remaining 2%. Some of the larger retail properties include Festival Mall (the largest single asset in the portfolio) in Kempton Park, Bayside Mall in Tableview and Keywest shopping centre in Krugersdorp. The office portfolio includes assets like Mowbray Golf Park in Pinelands in the Cape, the Microsoft offices in Bryanston and 82 Grayston Drive in the Sandton CBD. Though small in Acucap's portfolio, the industrial portfolio is of a high quality and offers good scope for growth as expansion continues at the N1 Business Park in Midrand and the Montague Business Park in Cape Town. Acucap's other investments include a 17,2% shareholding in Sycom Property Fund (a fellow listed real estate property company) as well as a 100% shareholding in Sycom's management company. Sycom's other significant shareholder is Hyprop Investments. The latter's 33,9% stake in Sycom has been the subject of much speculation over the past few years as the ownership structure has created somewhat of an impasse. Acucap has, however, stated its strategic intent to retain Sycom as a separate fund. Company1 hat engaged in child safety in South Africa. The Community rewards this with great recognition. Charity is a big part of the companies culture.",
"Australian copper producer Aditya Birla Minerals Ltd. said June 16 that there is a risk that the overall reserves at its Nifty copper mine in Western Australia may be adversely affected due to the effects of an earlier ground collapse at the mine. The company also said it will be unable to provide its annual reserve update until all investigative activities are finalized. Aditya Birla is expecting site costs for the June quarter to be in the range of A$17 million to A$19 million, higher than the earlier estimate of A$12 million to A$15 million due to higher-than-expected mine activity, unplanned maintenance on critical infrastructure and additional employee costs. The company was forced to halt operations at the Nifty mine in March following suspected underground subsidence, which led to the standing down of 350 mine employees until at least July 15. Phase two of probe drilling, which is being undertaken as part of efforts to assess mine safety, is progressing ahead of schedule and is now expected to be completed by the end of June instead of mid-July. Aditya Birla said preliminary observations from this second phase are similar to those from phase one drilling, in that there is less water being intersected between levels 16 and 20 than previously expected. Areas with potential to self-propagate into new sinkholes are being reviewed and, as Aditya Birla expected, the rock mass strength has deteriorated on top of mined-out areas. Management has begun an initial review of the results to identify new gaps and/or the need for additional confirmatory drilling. The first phase of the seismic system installation has been commissioned and is fully functional, albeit limited in coverage to upper parts of the mine. The pit has been dewatered with residual mud left at the pit sump. Aditya Birla added that while surface cracking has continued to develop in the area affected by the sinkhole, this was expected and does not present a hazard. However, washouts will occur after heavy rainfall, leading to widening and deepening of the cracks.",
"In preparation for the potential completion of the merger with AGL Resources Inc., the board of directors of Nicor Inc. announced a special pro rata dividend. The dividend is contingent upon the merger being completed prior to Nicor's next scheduled dividend record date, Dec. 31, to ensure that \"shareholders continue to receive a dividend at the current rate until the closing of the merger,\" the company said. In a Nov. 1 news release, Nicor said its board of directors declared a pro rata dividend of 0.5 cent per share per day from Oct. 1 until and including the day before the merger effective date. The dividend is the daily equivalent of the current quarterly dividend rate of 46.5 cents per share. It will be paid to Nicor shareholders of record at the close of business on the day immediately before the effective merger date, which is expected in the fourth quarter. \"The dividend will be paid as soon as practical following the completion of the merger,\" Nicor said. \"This pro rata dividend is in addition to the previously announced Nov. 1 dividend that was paid to shareholders of record Sept. 30. Following the merger closing, AGL Resources is expected to pay a dividend at the rate of $0.004945055 per share, per day for the remainder of the current quarterly dividend period. These dividend payment scenarios, together with a similar plan announced today by AGL Resources, will synchronize the companies' dividends as of the merger effective date in accordance with the merger agreement.\" If the merger is not completed by Dec. 31, Nicor shareholders of record Dec. 31 will receive the regular quarterly dividend of 46.5 cents per share, payable Feb. 1, 2012, and a new pro rata dividend will be announced to ensure that shareholders receive a dividend at the current rate until the merger is completed. Company2 opened up several factories in the that do not pay the minimum wage. Affordable Housing is not one of thair priorities."
)), class = "data.frame", row.names = c(NA, 3L))
library(data.table)
sckeywords <- c("Affordable Housing", "Benefit The Masses",
"Charitability", "Charitable", "Charitably", " Charities ", " Charity ")
pat <- paste0(sckeywords, collapse = '|')
setDT(text)
text <- text[, .(text = unlist(tstrsplit(text, "\\.\\s*", type.convert = TRUE))),
by = "doc_id"]
text <- text[grepl(pat, text)]
You can try to speed up the regex. One possibility is to create a trie, e.g. with trieregex. In your case, it would be:
trie_1 <- "(?:Charitab(?:ility|l[ey])|\\ Charit(?:ies\\ |y\\ )|Benefit\\ The\\ Masses|Affordable\\ Housing)"
text <- text[grepl(trie_1, text)]
# or without the white space around the last two words
trie_2 <- "(?:Charit(?:ab(?:ility|l[ey])|ies|y)|Benefit\\ The\\ Masses|Affordable\\ Housing)"
text <- text[grepl(trie_2, text)]
However, in the short example, using fixed patterns was actually the fastest (checked with microbenchmark), so you could try this:
text <- text[grepl(sckeywords[1], text, fixed = TRUE) | grepl(sckeywords[2], text, fixed = TRUE) |
grepl(sckeywords[3], text, fixed = TRUE) | grepl(sckeywords[4], text, fixed = TRUE) |
grepl(sckeywords[5], text, fixed = TRUE) | grepl(sckeywords[6], text, fixed = TRUE) |
grepl(sckeywords[7], text, fixed = TRUE)]
If this is still too slow, try to use other tools like awk or sed.
Use parallel processing. You could try to parallelise everything:
make a list of the files to read in and send parts of this file list to the different cores/workers. They then read in the files so that you don't need to send big files to different cores (you can also first try to skip this and start with your original code if the file is not too big)
make a clean data.table and filter it
return the data and join everything
When you run into memory issues, you could also try to save the cleaned data.tables separately in the workers instead of returning them.
For a parallel framework, I'd recommend foreach or the futureverse
Edit
Here is a regex that ignores the case of the first character. However, I'm not an expert on regexes, so maybe there are better ways to optimise the regex!
trie_2 <- "(?:[Cc]harit(?:ab(?:ility|l[ey])|ies|y)|[Bb]enefit\\ [Tt]he\\ [Mm]asses|[Aa]ffordable\\ [Hh]ousing)"
participated in the review for their constructive contributions, and to the international organizations. He urged them to continue to show solidarity with C™te dÕIvoire. In closing, he said that, more than ever, C™te dÕIvoire needed their support in order to attain its objectives ahead of 2020.
*II. Conclusions and/or recommendations*
127. The recommendations listed below enjoy the support of C™te dÕIvoire:
127.1 Consider the accession to core human rights instruments (Lesotho); and to other main international human rights treaties that it is not yet a party to (Philippines);
127.2 Make efforts towards the ratification of the OP-CAT (Chile);
127.3 Ratify the OP-CAT (Ghana, Tunisia), as recommended previously in 2009 (Czech Republic) and take policy measures to prevent torture and ill-treatment (Estonia);
This is my text, and I want to extract everything after, II. Conclusions and/or recommendations
I have tried the following;
x <- substring(text, first = "II. Conclusions and/or recommendations\n")
But Im getting the NAs introduced by coercion. what do I do to make it better?
text <- "participated in the review for their constructive contributions, and to the international organizations. He urged them to continue to show solidarity with C™te dÕIvoire. In closing, he said that, more than ever, C™te dÕIvoire needed their support in order to attain its objectives ahead of 2020.
*II. Conclusions and/or recommendations*
127. The recommendations listed below enjoy the support of C™te dÕIvoire:
127.1 Consider the accession to core human rights instruments (Lesotho); and to other main international human rights treaties that it is not yet a party to (Philippines);
127.2 Make efforts towards the ratification of the OP-CAT (Chile);
127.3 Ratify the OP-CAT (Ghana, Tunisia), as recommended previously in 2009 (Czech Republic) and take policy measures to prevent torture and ill-treatment (Estonia);"
Use this:
substring(text, regexpr("II.", text))
Or by giving start position as an integer:
substring(text, 300)
I have a quanteda corpus of hundreds of documents. How do I remove specific sections - like the abstract and footnotes etc. Otherwise, I am faced with doing it manually. Thanks
As requested, here is a text example. It is from a regular journal article. It shows the Meta data, then the abstract, then keywords, then introduction, then author contact details, then body of article, then Note, then Disclosure statement, then Notes on contributors, then references. I would like to remove everything apart from the introduction and body of the article. I would also like to remove the author name and Journal title - which are repeated throughout
" Behavioral Sciences of Terrorism and Political Aggression
ISSN: 1943-4472 (Print) 1943-4480 (Online) Journal homepage: http://www.tandfonline.com/loi/rirt20
Sometimes they come back: responding to
American foreign fighter returnees and other
Elusive threats
Christopher J. Wright
To cite this article: Christopher J. Wright (2018): Sometimes they come back: responding to
American foreign fighter returnees and other Elusive threats, Behavioral Sciences of Terrorism and
Political Aggression, DOI: 10.1080/19434472.2018.1464493
To link to this article: https://doi.org/10.1080/19434472.2018.1464493
Published online: 23 Apr 2018.
Submit your article to this journal
Article views: 57
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=rirt20
"
"BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION, 2018
https://doi.org/10.1080/19434472.2018.1464493
Sometimes they come back: responding to American foreign
fighter returnees and other Elusive threats
Christopher J. Wright
Department of Criminal Justice, Austin Peay State University, Clarksville, TN, USA
ABSTRACT ARTICLE HISTORY
Much has been made of the threat of battle hardened jihadis from Received 8 January 2018
Islamist insurgencies, especially Syria. But do Americans who Accepted 10 April 2018
return home after gaining experience fighting abroad pose a
KEYWORDS
greater risk than homegrown jihadi militants with no such Terrorism; foreign fighters;
experience? Using updated data covering 1990–2017, this study domestic terrorism;
shows that the presence of a returnee decreases the likelihood homegrown terrorism;
that an executed plot will cause mass casualties. Plots carried out lone-wolf; homeland security
Introduction: being afraid. Being a little afraid
How great of a threat do would-be jihadis pose to their home country? And do those who
return home after gaining experience fighting abroad in Islamist insurgencies or attending
terror training camps pose a greater risk than other jihadi militants? The fear, as first outlined
by Hegghammer (2013), is two-fold. First, individuals that have gone abroad to fight might
CONTACT Christopher J. Wright wrightc#apsu.edu Department of Criminal Justice, Austin Peay State University,
Clarksville, TN 37043, USA
© 2018 Society for Terrorism Research
"
"2 C. J. WRIGHT
Many of the earliest studies on Western foreign fighters suggested that those who
returned were in fact more deadly than those with no experience fighting in Islamist insur-
gencies. Hegghammer’s (2013) analysis suggested that these foreign fighter returnees
were a greater danger than when they left. Likewise, Byman (2015), Nilson (2015),
Kenney (2015), and Vidno (2011) came to similar conclusions while offering key insights
into the various mechanisms linking foreign fighting with successful plot execution and
greater mass casualties.
Other studies came to either mixed conclusions or directly contradicted the earlier find-
ings. Adding several years of data to Hegghammer’s (2013) earlier study, Hegghammer
"
" BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION 3
for them to form the types of large, local networks that would be necessary to carry out a
large-scale attack without attracting the attention of security services’ (p. 92).
"
Note
1. Charges were brought against Noor Zahi Salman, the widow of the Omar Mateen who carried
out the June, 2016 attack against the Pulse Nightclub in Orlando, Florida (US Department of
Justice., 2017a, January 17). However, in March of 2018 a jury acquitted her of the charges that
she had foreknowledge of the attack.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Christopher J. Wright, Ph.D., is an Assistant Professor at Austin Peay State University where he
teaches in the Homeland Security Concentration.
ORCID
Christopher J. Wright http://orcid.org/0000-0003-0043-6616
References
Byman, D. (2015). The homecomings: What happens when Arab foreign fighters in Iraq and Syria
return? Studies in Conflict & Terrorism, 38(8), 581–602.
Byman, D. (2016). The Jihadist returnee threat: Just how dangerous? Political Science Quarterly, 131(1),
69–99.
Byman, D., & Shapiro, J. (2014). Be afraid. Be a little afraid: The threat of terrorism from Western foreign
fighters in Syria and Iraq. Foreign Policy at Brookings. Washington, DC: Brookings. Retrieved from
https://www.brookings.edu/wp-content/uploads/2016/06/Be-Afraid-web.pdf
The approach
The key here is to determine the regular markers that precede each section, and then to use them as tags in a call to corpus_segment(). It's the tags that will need tweaking, based on their degree of regularity across documents.
Based on what you supplied above, I pasted that into a plain text file that I named example.txt. This code extracted the Introduction and what I think is the body of the article, but for that I had to decide a tag that marked its ending. Below, I used "Disclosure Statement". So:
library("quanteda")
crp <- readtext::readtext("~/tmp/example.txt") %>%
corpus()
pat <- c("\nIntroduction?", "\nCONTACT", "©", "\nDisclosure statement")
crpextracted <- corpus_segment(crp, pattern = pat)
summary(crpextracted)
## Corpus consisting of 4 documents:
##
## Text Types Tokens Sentences pattern
## example.txt.1 62 74 5 Introduction:
## example.txt.2 18 21 2 CONTACT
## example.txt.3 156 253 11 ©
## example.txt.4 101 180 19 Disclosure statement
##
## Source: /Users/kbenoit/Dropbox (Personal)/GitHub/quanteda/quanteda/* on x86_64 by kbenoit
## Created: Fri Jul 6 19:51:01 2018
## Notes: corpus_segment.corpus(crp, pattern = pat)
When you examine the text in the "Introduction:" tagged segment, you can see that everything from that string until the next tag was extracted to that as a new document:
corpus_subset(crpextracted, pattern == "\nIntroduction:") %>%
texts() %>% cat()
## being afraid. Being a little afraid
##
## How great of a threat do would-be jihadis pose to their home country? And do those who
##
## return home after gaining experience fighting abroad in Islamist insurgencies or attending
##
## terror training camps pose a greater risk than other jihadi militants? The fear, as first outlined
##
## by Hegghammer (2013), is two-fold. First, individuals that have gone abroad to fight might
How to remove pdf junk
All pdf conversions produce unwanted junk such as running headers, footers, etc. Here's how to remove them. (Note: You will want to do this before the step above.) How to construct the toreplace pattern? You will need to understand something about regular expressions, and use some experimentation.
library("stringr")
toreplace <- '\\n*\" \" BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION,{0,1} \\d+\\n*'
texts(crp) <- str_replace_all(texts(crp), regex(toreplace), "")
cat(texts(crp))
To demonstrate this on a section from your example:
# demonstration
x <- '
" " BEHAVIORAL SCIENCES OF TERRORISM AND POLITICAL AGGRESSION 3
'
str_replace_all(x, regex(toreplace), "")
## [1] ""
I have a data frame with a column of "text." Each row of this column is filled with text from media articles.
I am trying to extract a string that occurs like this: "term" (including the double quotes around the term). I tried the following regular expression to capture instances where a word is sandwiched between two double quotes:
stri_extract_all_regex(df$text, '"(.+?)"')
This seems to capture some instances of what I am looking for, but in other cases - where I know the criteria are met - it does not. It also captures what seem to be just quotes of longer text (and not other instances of quoted text). Here are the results of using the above:
[[19]]
[1] "\"play a constructive and positive role\""
[2] "\"active and hectic reception\""
[3] "\"[term]\""
I would like to have only exactly "term" as the output (including the double quotes). I am trying to find instances when the term is used alone in quotes.
Example in R:
test <- c(df$text[12], df$text[18])
res <- stri_extract_all_regex(test, '"\\S+"')
unlist(res)
[1] "\"Rohingya\"" "\"Bengali\"" NA
print(test)
[1] "Former UN general secretary Kofi Annan will advise Myanmar's government on resolving conflicts in Rakhine State, the office of the state counsellor announced today.Former UN Secretary General Kofi Annan speaks at the opening of the Consciouness Summit on climate change in Paris, France on July 21, 2015. Photo: EPARakhine State, one of the poorest in the Union, was wracked by sectarian violence in 2012 that forced more than 100,000 – mostly Muslims who ethnically identify as Rohingya – into squalid displacement camps where they face severe restrictions on movement as well as access to health care, education, and other other basic services.Addressing the ongoing crises has posed one of the most troubling challenges to Daw Aung San Suu Kyi's National League for Democracy-led government.Earlier today, the government announced the formation of an advisory panel that will be chaired by former UN chief, and focus on \"finding lasting solutions to the complex and delicate issues in the Rakhine State\".The board will submit recommendations to the government on \"conflict prevention, humanitarian assistance, rights and reconciliation, institution-building and promotion of development of Rakhine State,\" a statement from the state counsellor's office said.The statement did not use the word \"Rohingya\". Daw Aung San Suu Kyi has come under fire both at home and from international rights groups for failing prioritise to address the group's plight and seeking to placate hardline Buddhist nationalists by avoiding the politically-charged term. The government has already requested that the US Embassy and other diplomatic groups avoid the term Rohingya, and in June, she proposed \"Muslim community of Rakhine State\".The proposed neutral terminology, which the state counsellor ordered government officials to adopt, sparked mass protests in Rakhine State and in Yangon by hardline nationalists, who insist on use of the term \"Bengali\" that was also preferred by the previous government's to suggest the group's origins in neighbouring Bangladesh.In July UN special rapporteur for human rights Yanghee Lee urged the government to make ending \"institutionalised discrimination\" against the Rohingya and other Muslims in Rakhine an urgent priority.Myanmar also announced this week that current UN Secretary General Ban Ki-moon will attend the highly-anticipated 21st Century Panglong conference at the end of the month.The five-day talks, aimed at ending a host of complicated border ethnic conflicts that have lasted for decades, will begin on August 31."
[2] "Thousands of Kaman Muslims from the Rakhine State capital Sittwe obtained identity cards this week, some two years after they applied for the documents.“Now the problem is solved. The Kaman got national ID cards. We had proposed to the government and immigration office to work on the process of giving ID cards to Kaman,” said U Tin Hlaing Win, general secretary of the Kaman National Development Party (KNDP).The Kaman, as one of 135 officially recognised ethnic groups in Myanmar entitled to full citizenship, had struggled to get authorities to grant them the IDs due to the complex ethnic demographics and fraught identity politics of Rakhine State.Complicating the process for the Kaman applicants has been the population of more than 1 million Muslims in Rakhine State who self-identify as Rohingya, most of whom are stateless. “Citizenship scrutiny” programs to issue some form of identification to the minority group by the previous and current governments have been met with resistance by Rakhine nationalists.The shared Muslim faith of the Kaman and Rohingya became just one aspect of a contentious debate over terminology earlier this year, when State Counsellor Daw Aung San Suu Kyi put forward the phrase “the Muslim community in Rakhine State” to refer to self-identifying Rohingya in an attempt to chart a middle course on the issue of lexicon. “Rohingya” stirs passions among Buddhist nationalists, who insist that they be called “Bengalis” to imply that they are illegal immigrants from neighbouring Bangladesh, despite many tracing familial lineage in Rakhine State back generations.Some Kaman have since viewed the state counsellor’s edict warily, concerned that the Kaman identity might be conflated with that of the Rohingya – who are not entitled to citizenship – and could jeopardise Kaman prospects for ID cards and full rights under the law.Violence in 2012 between Buddhists and Muslims in Rakhine State affected Rakhine Buddhists, Kaman and Rohingya, but the latter suffered the brunt of casualties and displacement.U Tin Hlaing Win told The Myanmar Times last week that some of the Kaman Muslims displaced by the conflict in the island town of Rambre had also recently received national ID cards.“The Kaman are ethnics belonging to Myanmar,” said U Than Htun Aung, a senior immigration officer for Rakhine State. “Township immigration officers will examine [legitimate Kaman claims to citizenship] according to the process and they will ensure they get their rights.”Around 2000 Kaman applied for national ID cards in 2014, but only 38 people were issued the documents.The others were told they had not received IDs because of the purported existence of “fake Kaman”.More than 100,000 people are thought to hold government-issued national ID cards identifying them as Kaman, but KNDP research in 2013 estimated the actual ethnic Kaman population to be about 50,000.U Tin Hlaing Win said sorting out the “fake Kaman” issue was not solely the responsibility of Kaman people, adding that immigration officers through the years, and generations of ethnically mixed marriages and the offspring they produced, were also to blame for the confusion.“According to our research and knowledge tracing family trees, some Kaman identity-card holders were Rakhine plus Bengali or Rakhine plus Indian, not Kaman. It [identity problems] should be solved by three groups – we Kaman, the Rakhine and immigration authorities,” U Tin Hlaing Win told The Myanmar Times last week.What most seem to agree on is that “real Kaman” deserve the documentation they need to enjoy the full rights of citizenship.Ethnic Rakhine youth leader Ko Khine Lamin said, “The Rakhine objected to national ID cards for Kaman because of the controversy over fake Kaman. But there are real Kaman who have lived in Rakhine State since a long, long time ago. They should get their ethnic rights through careful examination by immigration officers.”Kaman politicians are not satisfied with their victory this week and are trying to meet with Rakhine State Chief Minister U Nyi Pu to raise other difficulties Kaman people face, such as transportation barriers. They also intend to ask the chief minister for rehabilitation programs for Kaman internally displaced people, as well as education and health support for the broader Kaman community."
The above code can only return terms in [1].
The "(.+?)" pattern matches ", then any char other than line break chars, as few as possible, up to the closest (leftmost) ". It means it can match whitespaces, too, and thus matches "play a constructive and positive role" and "active and hectic reception".
To match a streak of non-whitespace chars in-between double quotes, you need to use
stri_extract_all_regex(df$text, '"\\S+"')
The "\S+" pattern matches ", then 1 or more non-whitespace chars, and then a closing ".
See the regex demo.
If you only want to match word chars (letters, digits, _) in between double quotes, use
'"\\w+"'
See another regex demo.
To match curly quotes, use '["“]\\S+["”]' regex:
> res <- stri_extract_all_regex(test, '["“]\\S+["”]')
> unlist(res)
[1] "\"Rohingya\"" "\"Bengali\"" "\u0093Rohingya\u0094"
[4] "\u0093Bengalis\u0094"
And if you need to "normalize" the double quotes, use
> gsub("[“”]", '"', unlist(res))
[1] "\"Rohingya\"" "\"Bengali\"" "\"Rohingya\"" "\"Bengalis\""
Does anyone know how to replicate the (pg_trgm) postgres trigram similarity score from the similarity(text, text) function in R? I am using the stringdist package and would rather use R to calculate these on a matrix of text strings in a .csv file than run a bunch of postgresql quires.
Running similarity(string1, string2) in postgres give me a number score between 0 and 1.
I tired using the stringdist package to get a score but I think I still need to divide the code below by something.
stringdist(string1, string2, method="qgram",q = 3 )
Is there a way to replicate the pg_trgm score with the stringdist package or another way to do this in R?
An example would be getting the similarity score between the description of a book and the description of a genre like science fiction. For example, if I have two book descriptions and the using the similarity score of
book 1 = "Area X has been cut off from the rest of the continent for decades. Nature has reclaimed the last vestiges of human civilization. The first expedition returned with reports of a pristine, Edenic landscape; the second expedition ended in mass suicide, the third expedition in a hail of gunfire as its members turned on one another. The members of the eleventh expedition returned as shadows of their former selves, and within weeks, all had died of cancer. In Annihilation, the first volume of Jeff VanderMeer's Southern Reach trilogy, we join the twelfth expedition.
The group is made up of four women: an anthropologist; a surveyor; a psychologist, the de facto leader; and our narrator, a biologist. Their mission is to map the terrain, record all observations of their surroundings and of one anotioner, and, above all, avoid being contaminated by Area X itself.
They arrive expecting the unexpected, and Area X delivers—they discover a massive topographic anomaly and life forms that surpass understanding—but it’s the surprises that came across the border with them and the secrets the expedition members are keeping from one another that change everything."
book 2= "From Wall Street to Main Street, John Brooks, longtime contributor to the New Yorker, brings to life in vivid fashion twelve classic and timeless tales of corporate and financial life in America
What do the $350 million Ford Motor Company disaster known as the Edsel, the fast and incredible rise of Xerox, and the unbelievable scandals at GE and Texas Gulf Sulphur have in common? Each is an example of how an iconic company was defined by a particular moment of fame or notoriety; these notable and fascinating accounts are as relevant today to understanding the intricacies of corporate life as they were when the events happened.
Stories about Wall Street are infused with drama and adventure and reveal the machinations and volatile nature of the world of finance. John Brooks’s insightful reportage is so full of personality and critical detail that whether he is looking at the astounding market crash of 1962, the collapse of a well-known brokerage firm, or the bold attempt by American bankers to save the British pound, one gets the sense that history repeats itself.
Five additional stories on equally fascinating subjects round out this wonderful collection that will both entertain and inform readers . . . Business Adventures is truly financial journalism at its liveliest and best."
genre 1 = "Science fiction is a genre of fiction dealing with imaginative content such as futuristic settings, futuristic science and technology, space travel, time travel, faster than light travel, parallel universes, and extraterrestrial life. It often explores the potential consequences of scientific and other innovations, and has been called a "literature of ideas".[1] Authors commonly use science fiction as a framework to explore politics, identity, desire, morality, social structure, and other literary themes."
How can I get a similarity score for the description of each book against the description of the science fiction genre like pg_trgm using an R script?
How about something like this?
library(textcat)
?textcat_xdist
# Compute cross-distances between collections of n-gram profiles.
round(textcat_xdist(
list(
text1="hello there",
text2="why hello there",
text3="totally different"
),
method="cosine"),
3)
# text1 text2 text3
#text1 0.000 0.078 0.731
#text2 0.078 0.000 0.739
#text3 0.731 0.739 0.000