I need a python package that could get the related sentence from a text, based on the keywords provided.
For example, below is the Wikipedia page of J.J Oppenheimer -
Early life
Childhood and education
J. Robert Oppenheimer was born in New York City on April 22, 1904,[note 1][7] to Julius Oppenheimer, a wealthy Jewish textile importer who had immigrated to the United States from Germany in 1888, and Ella Friedman, a painter.
Julius came to the United States with no money, no baccalaureate studies, and no knowledge of the English language. He got a job in a textile company and within a decade was an executive with the company. Ella was from Baltimore.[8] The Oppenheimer were non-observant Ashkenazi Jews.[9]
The first atomic bomb was successfully detonated on July 16, 1945, in the Trinity test in New Mexico.
Oppenheimer later remarked that it brought to mind words from the Bhagavad Gita: "Now I am become Death, the destroyer of worlds.
If my passed string is - "JJ Oppenheimer birth date", it should return "J. Robert Oppenheimer was born in New York City on April 22, 1904"
If my passed string is - "JJ Openheimer Trinity test", it should return "The first atomic bomb was successfully detonated on July 16, 1945, in the Trinity test in New Mexico"
I tried searching a lot but nothing comes closer to what I want and I don't know much about NLP vectorization techniques. It would be great if someone please suggest some package if they know(or exist).
You could use fuzzywuzzy.
fuzz.ratio(search_text, sentence).
This gives you a score of how similar two strings are.
https://github.com/seatgeek/fuzzywuzzy
I am pretty sure a Module exists that could do this for you, you could try and make it yourself by parsing through the text and creating words like: ["date of birth", "born", "birth date", etc] and you do this for multiple fields. This would thus allow you to find information that would be available.
The idea is:
you grab your text or whatever u have,
you grab what you are looking for (example date of birth)
You then assign a date of birth to a list of similar words,
you look through ur file to see if you find a sentence that has that in it.
I am pretty sure there is no module, maybe I am wrong but smth like this should work.
The task you describe looks like Information Retrieval. Given a query (the keywords) the model should return a list of document (the sentences) that best matches the query.
This is essentially what the response using fuzzywuzzy is suggesting. But maybe just counting the number of occurences of the query words in each sentence is enough (and more efficient).
The next step would be to use Tf-Idf. It is a weighting scheme, that gives high scores to words that are specific to a document with respect to a set of document (the corpus).
This results in every document having a vector associated, you will then be able to sort the documents according to their similarity to the query vector. SO Answer to do that
Check the following text piece
IN THE HIGH COURT OF GUJARAT AT AHMEDABAD
R/CRIMINAL APPEAL NO. 251 of 2009
FOR APPROVAL AND SIGNATURE:
HONOURABLE MR.JUSTICE R.P.DHOLARIA
==========================================================
1 Whether Reporters of Local Papers may be allowed to see the judgment ?
2 To be referred to the Reporter or not ?
3 Whether their Lordships wish to see the fair copy of the judgment ?
4 Whether this case involves a substantial question of law as to the interpretation of the Constitution of India or any order made thereunder ?
========================================================== STATE OF GUJARAT,S M RAO,FOOD INSPECTOR,OFFICE OF THE Versus DHARMESHBHAI NARHARIBHAI GANDHI ========================================================== Appearance: MS HB PUNANI, APP (2) for the Appellant(s) No. 1 MR DK MODI(1317) for the Opponent(s)/Respondent(s) No. 1 ==========================================================
CORAM: HONOURABLE MR.JUSTICE R.P.DHOLARIA
Date : 12/03/2019
ORAL JUDGMENT
1. The appellant State of Gujarat has
preferred the present appeal under section 378(1)
(3) of the Code of Criminal Procedure, 1973
against the judgment and order of acquittal dated
Page 1 of 12
R/CR.A/251/2009 JUDGMENT
17.11.2008 rendered by learned 2nd Additional
Civil Judge and Judicial Magistrate, First Class,
Nadiad in Food Case No.1 of 2007.
The short facts giving rise to the
present appeal are that on 10.11.2006 at about
18.00 hours, the complainant visited the place of
the respondent accused situated at Juna
Makhanpura, Rabarivad, Nadiad along with panch
witness and the respondent was found dealing in
provisional items. The complainant identified
himself as a Food Inspector and after giving
intimation in Form No.6 has purchased muddamal
sample of mustard seeds in the presence of the
panchas for the purpose of analysis. Thereafter,
the complainant Food Inspector has divided the
said sample in equal three parts and after
completing formalities of packing and sealing
obtained signatures of the vendor and panchas and
out of the said three parts, one part was sent to
the Public Analyst, Vadodara for analysis and
remaining two parts were sent to the Local Health
Authority, Gandhinagar. Thereafter, the Public
Analyst forwarded his report. In the said report,
it is stated that the muddamal sample of mustard
seeds is misbranded which is in breach of the
provisions of the Food Adulteration Act, 1954
(for short “the Act”) and the Rules framed
thereunder. It is alleged that, therefore, the
sample of mustard seeds was misbranded and,
thereby, the accused has committed the offence.
**Page 2 of 12
R/CR.A/251/2009* JUDGMENT*
Hence, the complaint came to be lodged against
the respondent accused.
I want to be able to write a program such that it follows the given constraints. Be wary of the fact that this is only a single file i have like 40k files and it should run on all the files. All the files have some difference but the basic format for every file is the same.
Constraints.
It should start the text extraction process from after the "metadata" . Metadata is the data about the file from the starting of the file i.e " In the high court of gujarat" till Oral Judgment. In all the files i have , there are various POINTS after the string ends. So i need all these points as a separate paragraph ( see the text has 2 points , i need it in different paragraphs ).
Check the lines in italics, these are the panes in the text/pdf file. I need to remove these as these donot have any meaning to the text content i want.
These files are both available in TEXT or PDF format so i can use either. But i am new to python so i dont know how and where to start. I just have basic knowledge in python.
This data is going to be made into a "corpus" for further processes in building a huge expert system so you know what needs to be done i hope.
Read the official python docs!
Start with python's basic str type and its methods. One of its methods, find, will find substrings in your text.
Use the python slicing notation to extract the portion of text you need, e.g.
text = """YOUR TEXT HERE..."""
meta_start = 'In the high court of gujarat'
meta_end = 'ORAL JUDGMENT'
pos1 = text.find(meta_start)
pos2 = text.find(meta_end)
if pos2 > pos1 and pos1 > -1:
# text is found, extract it
text1 = text[meta_start + len(meta_start):meta_end - 1]
After that, you can go ahead and save your extracted text to a database.
Of course, a better and more complicated solution would be to use regular expressions, but that's another story -- try finding the right way for yourself!
As to italics and other text formatting, you won't ever be able to mark it out in plain text (unless you have some 'meta' markers, like e.g. [i] tags).
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 4 years ago.
Improve this question
I am trying to transform pdfs from conference/journal papers into .txt files. I basically want to have a structure a bit cleaner that the current pdf: no line break before the end of a sentence and highlighting sections of the paper. The problem I am dealing with currently is to try and detect sections automatically. That is, in the following image, I want to be able to find ABSTRACT, CSS CONCEPT, 1 INTRODUCTION, 2 THE BODY OF THE PAPER..
If currently use a simply idea which works-ish. I basically let pdf miner do its job and then use NTLK to find sentences.
def convert_pdf_to_txt(path, year):
rsrcmgr = PDFResourceManager()
retstr = StringIO()
codec = 'utf-8'
laparams = LAParams()
device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)
fp = open(path, 'rb')
interpreter = PDFPageInterpreter(rsrcmgr, device)
password = ""
maxpages = 0
caching = True
pagenos=set()
for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password,caching=caching, check_extractable=True):
interpreter.process_page(page)
text = retstr.getvalue()
sentences = sent_tokenize(text)
size = len(sentences)
i = 0
path = path[:-3]
output = open("out.txt", 'w')
for s in sentences:
s = s.replace("-\n", '') #remove hyphens
lines = s.split("\n")
for line in lines:
if(line.isupper()): #section are only uppercase.
#however, other things are also only uppercase hence my errors
line = "--SECTION-- " +line
output.write("\n\n"+line+"\n")
else:
output.write(line)
output.write("\n")
fp.close()
device.close()
retstr.close()
This gives me on the whole file the following output:
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758SIG Proceedings Paper in LaTeX Format∗Extended Abstract†
--SECTION-- G.K.M.
Tobin§Dublin, OhioSean Fogartywebmaster#marysville-ohio.comLars Thørväld¶The Thørväld GroupHekla, Icelandlarst#affiliation.orgCharles PalmerInstitute for Clarity in DocumentationInstitute for Clarity in DocumentationBen Trovato‡Dublin, Ohiotrovato#corporation.comLawrence P. LeipunerBrookhaven Laboratorieslleipuner#researchlabs.orgJohn SmithPalmer Research LaboratoriesSan Antonio, Texascpalmer#prl.comJulius P. KumquatNASA Ames Research CenterMoffett Field, Californiafogartys#amesres.orgThe Thørväld Groupjsmith#affiliation.orgThe Kumquat Consortiumjpkumquat#consortium.netcolumns), a specified set of fonts (Arial or Helvetica and TimesRoman) in certain specified sizes, a specified live area, centeredon the page, specified size of margins, specified column width andgutter size.
--SECTION-- ABSTRACT
This paper provides a sample of a LATEX document which conforms,somewhat loosely, to the formatting guidelines for ACM SIG Proceedings.1Unpublishedworkingdraft.
Notfordistribution.
--SECTION-- CCS CONCEPTS
• Computer systems organization → Embedded systems; Redundancy; Robotics; • Networks → Network reliability;
--SECTION-- KEYWORDS
ACM proceedings, LATEX, text taggingACM Reference Format:Ben Trovato, G.K.M.
Tobin, Lars Thørväld, Lawrence P. Leipuner, SeanFogarty, Charles Palmer, John Smith, and Julius P. Kumquat.
1997.
--SECTION-- SIG
Proceedings Paper in LaTeX Format: Extended Abstract.
In Proceedings ofACM Woodstock conference (WOODSTOCK’97).
ACM, New York, NY, USA,5 pages.
https://doi.org/10.475/123_4
--SECTION-- 2 THE BODY OF THE PAPER
Typically, the body of a paper is organized into a hierarchical structure, with numbered or unnumbered headings for sections, subsections, sub-subsections, and even smaller sections.
The command\section that precedes this paragraph is part of such a hierarchy.3LATEX handles the numbering and placement of these headings foryou, when you use the appropriate heading commands aroundthe titles of the headings.
If you want a sub-subsection or smallerpart to be unnumbered in your output, simply append an asteriskto the command name.
Examples of both numbered and unnumbered headings will appear throughout the balance of this sampledocument.
Because the entire article is contained in the document environment, you can indicate the start of a new paragraph with a blankline in your input file; that is why this sentence forms a separateparagraph.
--SECTION-- 1 INTRODUCTION
The proceedings are the records of a conference.2 ACM seeks to givethese conference by-products a uniform, high-quality appearance.
To do this, ACM has some rigid requirements for the format of theproceedings documents: there is a specified format (balanced double∗Produces the permission block, and copyright information†The full version of the author’s guide is available as acmart.pdf document‡Dr.
Trovato insisted his name be first.
§The secretary disavows any knowledge of this author’s actions.
¶This author is the one who did all the really hard work.
1This is an abstract footnote2This is a footnotePermission to make digital or hard copies of part or all of this work for personal orUnpublished working draft.
Not for distribution.
classroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page.
Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
WOODSTOCK’97, July 1997, El Paso, Texas USA© 2016 Copyright held by the owner/author(s).
--SECTION-- ACM ISBN 123-4567-24-567/08/06.
https://doi.org/10.475/123_4Submission ID: 123-A12-B3.
2018-10-20 12:29.
Page 1 of 1–5.
2.1 Type Changes and Special CharactersWe have already seen several typeface changes in this sample.
You can indicate italicized words or phrases in your text with thecommand \textit; emboldening with the command \textbf andtypewriter-style (for instance, for computer code) with \texttt.
But remember, you do not have to indicate typestyle changes whensuch changes are part of the structural elements of your article;for instance, the heading of this subsection will be in a sans serif4typeface, but that is handled by the document class file.
Take carewith the use of5 the curly braces in typeface changes; they mark thebeginning and end of the text that is to be in the different typeface.
3This is a footnote.
4Another footnote here.
Let’s make this a rather long one to see how it looks.
5Another footnote.
5960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116WOODSTOCK’97, July 1997, El Paso, Texas USAB. Trovato et al.
You can use whatever symbols, accented characters, or nonEnglish characters you need anywhere in your document; you canfind a complete list of what is available in the LATEX User’s Guide[26].
2.2 Math EquationsYou may want to display math equations in three distinct styles:inline, numbered or non-numbered display.
Each of the three arediscussed in the next sections.
Table 1: Frequency of Special CharactersNon-English or Math
--SECTION-- Ø
π$2
--SECTION-- Ψ
1Frequency Comments1 in 1,0001 in 54 in 5For Swedish namesCommon in mathUsed in business1 in 40,000 Unexplained usagemand, using \cite.
This article shows only the plainest form of the citation comInline (In-text) Equations.
A formula that appears in the2.2.1running text is called an inline or in-text formula.
It is producedby the math environment, which can be invoked with the usual\begin .
.
.
\end construction or with the short form $ .
.
.
$.
You can use any of the symbols and structures, from α to ω, availablein LATEX [26]; this section will simply show a few examples of intext equations in context.
Notice how this equation: limn→∞ x = 0,set here in in-line math style, looks slightly different when set indisplay style.
(See next section).
2.2.2 Display Equations.
A numbered display equation—one setoff by vertical space from the text and centered horizontally—isproduced by the equation environment.
An unnumbered displayequation is produced by the displaymath environment.
Again, in either environment, you can use any of the symbolsand structures available in LATEX; this section will just give a coupleof examples of display equations in context.
First, consider theequation, shown as an inline equation above:Some examples.
A paginated journal article [2], an enumeratedjournal article [11], a reference to an entire issue [10], a monograph(whole book) [25], a monograph/whole book in a series (see 2ain spec.
document) [18], a divisible-book such as an anthology orcompilation [13] followed by the same example, however we onlyoutput the series if the volume number is given [14] (so Editor00a’sseries should NOT be present since it has no vol.
no.
), a chapterin a divisible book [37], a chapter in a divisible book in a series[12], a multi-volume work as book [24], an article in a proceedings(of a conference, symposium, workshop for example) (paginatedproceedings article) [4], a proceedings article with all possible elements [36], an example of an enumerated proceedings article [16],an informally published work [17], a doctoral dissertation [9], amaster’s thesis: [5], an online document / world wide web resource[1, 30, 38], a video game (Case 1) [29] and (Case 2) [28] and [27] and(Case 3) a patent [35], work accepted for publication [31], ’YYYYb’test for prolific author [32] and [33].
Other cites might contain’duplicate’ DOI and URLs (some SIAM articles) [23].
Boris / BarbaraBeeton: multi-volume works as books [21] and [20].
Unpublishedworkingdraft.
Notfordistribution.
2.4 TablesBecause tables cannot be split across pages, the best placement forthem is typically the top of the page nearest their initial cite.
To ensure this proper “floating” placement of tables, use the environmenttable to enclose the table’s contents and the table caption.
The contents of the table itself must go in the tabular environment, to bealigned properly in rows and columns, with the desired horizontaland vertical rules.
Again, detailed instructions on tabular materialare found in the LATEX User’s Guide.
Immediately following this sentence is the point at which Table 1is included in the input file; compare the placement of the tablehere with the table in the printed output of this document.
Notice how it is formatted somewhat differently in the displaymath environment.
Now, we’ll enter an unnumbered equation:A couple of citations with DOIs: [22, 23].
Online citations: [38–40].
just to demonstrate LATEX’s able handling of numbering.
and follow it with another numbered equation:x + 1∫ π +2(1)(2)limn→∞ x = 0∞i =0∞i =0xi =0f2.3 CitationsCitations to articles [6–8, 19], conference proceedings [8] or maybebooks [26, 34] listed in the Bibliography section of your article willoccur throughout the text of your article.
You should use BibTeX toautomatically produce this bibliography; you simply need to insertone of several citation commands with a key of the item cited in theproper location in the .tex file [26].
The key is a short referenceyou invent to uniquely identify each work; in this sample document,the key is the first author’s surname and a word from the title.
Thisidentifying key is included with each item in the .bib file for yourarticle.
The details of the construction of the .bib file are beyond thescope of this sample document, but more information can be foundin the Author’s Guide, and exhaustive details in the LATEX User’sGuide by Lamport [26].
To set a wider table, which takes up the whole width of the page’slive area, use the environment table* to enclose the table’s contentsand the table caption.
As with a single-column table, this widetable will “float” to a location deemed more desirable.
Immediatelyfollowing this sentence is the point at which Table 2 is included inthe input file; again, it is instructive to compare the placement ofthe table here with the table in the printed output of this document.
It is strongly recommended to use the package booktabs [15]and follow its main principles of typography with respect to tables:(1) Never, ever use vertical rules.
(2) Never use double rules.
Submission ID: 123-A12-B3.
2018-10-20 12:29.
Page 2 of 1–5.
117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232SIG Proceedings Paper in LaTeX FormatWOODSTOCK’97, July 1997, El Paso, Texas USATable 2: Some Typical CommandsCommand A Number Comments\author\table\table*100300400AuthorFor tablesFor wider tablesDefinition 2.2.
If z is irrational, then by ez we mean the uniquenumber that has logarithm z:f (x)д(x) = L.(cid:21)log ez = z.
The pre-defined theorem-like constructs are theorem, conjecture, proposition, lemma and corollary.
The pre-defined definition-like constructs are example and definition.
You can add yourown constructs using the amsthm interface [3].
The styles used inthe \theoremstyle command are acmplain and acmdefinition.
Another construct is proof, for example,Proof.
Suppose on the contrary there exists a real number Lsuch thatFigure 1: A sample black and white graphic.
It is also a good idea not to overuse horizontal rules.
Figure 2: A sample black and white graphic that has beenresized with the includegraphics command.
Unpublishedworkingdraft.
Notfordistribution.
lim(cid:20)x→∞дx · f (x)д(x)f (x) = limx→c2.5 FiguresLike tables, figures cannot be split across pages; the best placementfor them is typically the top or the bottom of the page nearest theirinitial cite.
To ensure this proper “floating” placement of figures,use the environment figure to enclose the figure and its caption.
This sample document contains examples of .eps files to bedisplayable with LATEX.
If you work with pdfLATEX, use files in the.pdf format.
Note that most modern TEX systems will convert .epsto .pdf for you on the fly.
More details on each of these are foundin the Author’s Guide.
As was the case with tables, you may want a figure that spans twocolumns.
To do this, and still to ensure proper “floating” placementof tables, use the environment figure* to enclose the figure and itscaption.
And don’t forget to end the environment with figure*, notfigure!
= limx→cд(x)· limx→cf (x)д(x) = 0·L = 0,Thenl = limx→cwhich contradicts our assumption that l (cid:44) 0.
--SECTION-- 3 CONCLUSIONS
This paragraph will end the body of this sample document.
Remember that you might still have Acknowledgments or Appendices;brief samples of these follow.
There is still the Bibliography to dealwith; and we will make a disclaimer about that here: with the exception of the reference to the LATEX book, the citations in this paperare to articles which have nothing to do with the present subjectand are used as examples only.
□
--SECTION-- A HEADINGS IN APPENDICES
The rules about hierarchical headings discussed above for the bodyof the article are different in the appendices.
In the appendix environment, the command section is used to indicate the start ofeach Appendix, with alphabetic order designation (i.e., the first isA, the second B, etc.)
and a title (if you include one).
So, if you needhierarchical structure within an Appendix, start with subsectionas the highest level.
Here is an outline of the body of this documentin Appendix-appropriate form:2.6 Theorem-like ConstructsOther common constructs that may occur in your article are theforms for logical constructs like theorems, axioms, corollaries andproofs.
ACM uses two types of these constructs: theorem-like anddefinition-like.
Here is a theorem:Theorem 2.1.
Let f be continuous on [a, b].
If G is an antiderivative for f on [a, b], then∫ baHere is a definition:f (t) dt = G(b) − G(a).
Submission ID: 123-A12-B3.
2018-10-20 12:29.
Page 3 of 1–5.
A.1 IntroductionA.2 The Body of the PaperA.2.1 Type Changes and Special Characters.
A.2.2 Math Equations.
Inline (In-text) Equations.
Display Equations.
A.2.3 Citations.
233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348WOODSTOCK’97, July 1997, El Paso, Texas USAB. Trovato et al.
Figure 4: A sample black and white graphic that has beenresized with the includegraphics command.
A.2.4 Tables.
--SECTION-- A.2.5
Figures.
A.2.6 Theorem-like Constructs.
Figure 3: A sample black and white graphic that needs to span two columns of text.
Unpublishedworkingdraft.
Notfordistribution.
(Nov. 1996).
booktabs.
Mathematical Society.
http://www.ctan.org/pkg/amsthm.
[2] Patricia S. Abril and Robert Plant.
2007.
The patent holder’s dilemma: Buy, sell,or troll?
Commun.
ACM 50, 1 (Jan. 2007), 36–44.
https://doi.org/10.1145/1188913.
1188915[3] American Mathematical Society 2015.
Using the amsthm Package.
American[4] Sten Andler.
1979.
Predicate Path expressions.
In Proceedings of the 6th.
--SECTION-- ACM
SIGACT-SIGPLAN symposium on Principles of Programming Languages (POPL ’79).
ACM Press, New York, NY, 226–236.
https://doi.org/10.1145/567752.567774[5] David A. Anisi.
2003.
Optimal Motion Control of a Ground Vehicle.
Master’s thesis.
[6] Mic Bowman, Saumya K. Debray, and Larry L. Peterson.
1993.
Reasoning AboutNaming Systems.
ACM Trans.
Program.
Lang.
Syst.
15, 5 (November 1993), 795–825. https://doi.org/10.1145/161468.161471[7] Johannes Braams.
1991.
Babel, a Multilingual Style-Option System for Use withRoyal Institute of Technology (KTH), Stockholm, Sweden.
LaTeX’s Standard Document Styles.
TUGboat 12, 2 (June 1991), 291–301.
TeX Users Group, 84–89.
[8] Malcolm Clark.
1991.
Post Congress Tristesse.
In TeX90 Conference Proceedings.
[9] Kenneth L. Clarkson.
1985.
Algorithms for Closest-Point Problems (ComputationalGeometry).
Ph.D. Dissertation.
Stanford University, Palo Alto, CA.
UMI OrderNumber: AAT 8506171.
[10] Jacques Cohen (Ed.).
1996.
Special issue: Digital Libraries.
Commun.
--SECTION-- ACM 39, 11
[11] Sarah Cohen, Werner Nutt, and Yehoshua Sagic.
2007.
Deciding equivalancesamong conjunctive aggregate queries.
J. ACM 54, 2, Article 5 (April 2007),50 pages.
https://doi.org/10.1145/1219092.1219093[12] Bruce P. Douglass, David Harel, and Mark B. Trakhtenbrot.
1998.
Statecarts inuse: structured analysis and object-orientation.
In Lectures on Embedded Systems,Grzegorz Rozenberg and Frits W. Vaandrager (Eds.).
Lecture Notes in ComputerScience, Vol.
1494.
Springer-Verlag, London, 368–394.
https://doi.org/10.1007/3-540-65193-4_29[13] Ian Editor (Ed.).
2007.
The title of book one (1st.
ed.).
The name of the seriesone, Vol.
9.
University of Chicago Press, Chicago.
https://doi.org/10.1007/3-540-09237-4Chicago, Chapter 100. https://doi.org/10.1007/3-540-09237-4[14] Ian Editor (Ed.).
2008.
The title of book two (2nd.
ed.).
University of Chicago Press,[15] Simon Fear.
2005.
Publication quality tables in LATEX.
http://www.ctan.org/pkg/[16] Matthew Van Gundy, Davide Balzarotti, and Giovanni Vigna.
2007.
Catch me, ifyou can: Evading network signatures with web-based polymorphic worms.
InProceedings of the first USENIX workshop on Offensive Technologies (WOOT ’07).
USENIX Association, Berkley, CA, Article 7, 9 pages.
[17] David Harel.
1978.
LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER.
MIT Research Lab Technical Report TR-200.
Massachusetts Institute of Technology, Cambridge, MA.
[18] David Harel.
1979.
First-Order Dynamic Logic.
Lecture Notes in Computer Science,Vol.
68.
Springer-Verlag, New York, NY.
https://doi.org/10.1007/3-540-09237-4[19] Maurice Herlihy.
1993.
A Methodology for Implementing Highly ConcurrentData Objects.
ACM Trans.
Program.
Lang.
Syst.
15, 5 (November 1993), 745–770.
https://doi.org/10.1145/161468.161469[20] Lars Hörmander.
1985.
The analysis of linear partial differential operators.
--SECTION-- III.
Grundlehren der Mathematischen Wissenschaften [Fundamental Principles ofMathematical Sciences], Vol.
275.
Springer-Verlag, Berlin, Germany.
viii+525pages.
Pseudodifferential operators.
[21] Lars Hörmander.
1985.
The analysis of linear partial differential operators.
--SECTION-- IV.
Grundlehren der Mathematischen Wissenschaften [Fundamental Principles ofMathematical Sciences], Vol.
275.
Springer-Verlag, Berlin, Germany.
vii+352pages.
Fourier integral operators.
A Caveat for the TEX Expert.
A.3 ConclusionsA.4 ReferencesGenerated by bibtex from your .bib file.
Run latex, then bibtex, thenlatex twice (to resolve references) to create the .bbl file.
Insert that.bbl file into the .tex source file and comment out the command\thebibliography.
--SECTION-- B MORE HELP FOR THE HARDY
Of course, reading the source code is always useful.
The file acmart.
pdf contains both the user guide and the commented code.
--SECTION-- ACKNOWLEDGMENTS
The authors would like to thank Dr. Yuhua Li for providing theMATLAB code of the BEPS method.
The authors would also like to thank the anonymous refereesfor their valuable comments and helpful suggestions.
The work issupported by the National Natural Science Foundation of Chinaunder Grant No.
: 61273304 and Young Scientists’ Support Program(http://www.nnsf.cn/youngscientists).
--SECTION-- REFERENCES
[1] Rafal Ablamowicz and Bertfried Fauser.
2007.
CLIFFORD: a Maple 11 Package forClifford Algebra Computations, version 11.
Retrieved February 28, 2008 fromhttp://math.tntech.edu/rafal/cliff11/index.htmlSubmission ID: 123-A12-B3.
2018-10-20 12:29.
Page 4 of 1–5.
349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464SIG Proceedings Paper in LaTeX FormatWOODSTOCK’97, July 1997, El Paso, Texas USAAlgorithms (3rd.
ed.).
Addison Wesley Longman Publishing Co., Inc.New York, NY.
--SECTION-- [22] IEEE 2004.
IEEE TCSC Executive Committee.
In Proceedings of the IEEE International Conference on Web Services (ICWS ’04).
IEEE Computer Society, Washington,
--SECTION-- DC, USA, 21–22.
https://doi.org/10.1109/ICWS.2004.64[23] Markus Kirschmer and John Voight.
2010.
Algorithmic Enumeration of IdealClasses for Quaternion Orders.
SIAM J. Comput.
39, 5 (Jan. 2010), 1714–1747.
https://doi.org/10.1137/080734467[24] Donald E. Knuth.
1997.
The Art of Computer Programming, Vol.
1: Fundamental[25] David Kosiur.
2001.
Understanding Policy-Based Networking (2nd.
ed.).
Wiley,[26] Leslie Lamport.
1986.
LATEX: A Document Preparation System.
Addison-Wesley,[27] Newton Lee.
2005.
Interview with Bill Kinder: January 13, 2005.
Video.
Comput.
Entertain.
3, 1, Article 4 (Jan.-March 2005).
https://doi.org/10.1145/1057270.
1057278[28] Dave Novak.
2003.
Solder man.
Video.
In ACM SIGGRAPH 2003 Video Review onAnimation theater Program: Part I - Vol.
145 (July 27–27, 2003).
ACM Press, NewYork, NY, 4. https://doi.org/99.9999/woot07-S422[29] Barack Obama.
2008.
A more perfect union.
Video.
Retrieved March 21, 2008Reading, MA.
from http://video.google.com/videoplay?docid=6528042696351994555[30] Poker-Edge.Com.
2006.
Stats and Analysis.
Retrieved June 7, 2006 from http://www.poker-edge.com/stats.phpArticle 5 (July 2008).
To appear.
[31] Bernard Rous.
2008.
The Enabling of Digital Libraries.
Digital Libraries 12, 3,[32] Mehdi Saeedi, Morteza Saheb Zamani, and Mehdi Sedighi.
2010.
A library-basedsynthesis methodology for reversible logic.
Microelectron.
--SECTION-- J.
41, 4 (April 2010),185–194.
[33] Mehdi Saeedi, Morteza Saheb Zamani, Mehdi Sedighi, and Zahra Sasanian.
2010.
Synthesis of Reversible Circuit Using Cycle-Based Approach.
J. Emerg.
Technol.
Comput.
Syst.
6, 4 (Dec. 2010).
--SECTION-- [34] S.L.
Salas and Einar Hille.
1978.
Calculus: One and Several Variable.
John Wileyand Sons, New York.
[35] Joseph Scientist.
2009.
The fountain of youth.
Patent No.
12345, Filed July 1st.,Unpublishedworkingdraft.
Notfordistribution.
2008, Issued Aug.
9th., 2009.
[36] Stan W. Smith.
2010.
An experiment in bibliographic mark-up: Parsing metadatafor XML export.
In Proceedings of the 3rd.
annual workshop on Librarians andComputers (LAC ’10), Reginald N. Smythe and Alexander Noble (Eds.
), Vol.
3.
Paparazzi Press, Milan Italy, 422–431.
https://doi.org/99.9999/woot07-S422In DistributedSystems (2nd.
ed.
), Sape Mullender (Ed.).
ACM Press, New York, NY, 19–33.
https://doi.org/10.1145/90417.90738[38] Harry Thornburg.
2001.
Introduction to Bayesian Statistics.
Retrieved March 2,[37] Asad Z. Spector.
1990.
Achieving application requirements.
2005 from http://ccrma.stanford.edu/~jos/bayes/bayes.html
--SECTION-- [39] TUG 2017.
Institutional members of the TEX Users Group.
Retrieved May 27,[40] Boris Veytsman.
[n. d.].
acmart—Class for typesetting publications of ACM.
2017 from http://wwtug.org/instmem.htmlRetrieved May 27, 2017 from http://www.ctan.org/pkg/acmartSubmission ID: 123-A12-B3.
2018-10-20 12:29.
Page 5 of 1–5.
465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580
This output is partially right. I mean all section are rightfully detected but I also get a lot of false positives. Can you think of any better way (less false-positive prone) to implement this.
PS: if you need the pdf, it is available here, filename is
sample-sigconf-authordraft.pdf
To understand why you're getting false positives, you need to understand more about PDF.
PDF (unlike what you may think) is not a WYSIWYG format. In fact, you should think of PDF as a container of instructions. It has its own programming language that tells viewing (or rendering) software what to do.
Example:
go to position 500, 30
set the font to Arial
set the fontsize to 12
draw the glyph for character 'H'
go to position 500, 35
draw the glyphs for characters 'ello'
Some blocks of instructions are grouped together in what is called an object.
Each object gets its own number, and objects can reference each other.
Objects are indexed in the cross reference table (known as the XREF table).
Some of the challenges when doing text-extraction from PDF:
objects don't need to appear in logical reading order
sometimes whitespace is explicitly written, sometimes it is achieved by simply moving the drawing cursor
PDFs do not always have logical structure embedded in them. So whilst you may be able to clearly see a 2-column layout, from the viewpoint of the software this is less clear.
I have worked at iText, a software company that focuses solely on PDF related technology. And extracting text in the way you describe, from a random document, in a foolproof way is something that simply isn't possible with current software packages.
There are tons of exceptions that may break your workflow:
formulas appearing in text
tables
tables with row span and column span
images with text flowing around them
footnotes
headers
footers
I have news dataset which contains almost 10,000 news over the last 3 years.
I also have a list of companies (names of companies) which are registered in NYSE. Now I want to check whether list of company names in the list have appeared in the news dataset or not.
Example:
company Name: 'E.I. du Pont de Nemours and Company'
News: 'Monsanto and DuPont settle major disputes with broad patent-licensing deal, with DuPont agreeing to pay at least $1.75 billion over 10 years for rights to technology for herbicide-resistant soybeans.'
Now, I can find the news contains company name if the exact company name is in the news but you can see from the above example it is not the case.
I also tried another way i.e. I took the integral name in the company's full name i.e. in the above example 'Pont' is a word which should be definitely a part of the text when this company name is called. So it worked for majority of the times but then problem occurs in the following example:
Company Name: Ennis, Inc.
News: L D`ennis` Kozlowski, former chief executive convicted of looting nearly $100 million from Tyco International, has emerged into far more modest life after serving six-and-a-half year sentence and probation; Kozlowski, who became ultimate symbol of corporate greed in era that included scandals at Enron and WorldCom, describes his personal transformation and more humble pleasures that have replaced his once high-flying lifestyle.
Now you can see Ennis is matching with Dennis in the text so it giving irrelevant news results.
Can someone help in telling the right way of doing this ? Thanks.
Use a regex with boundaries for exact matches whether you choose the full name or some partial part you think is unique is up to you but using word boundaries D'ennis' won't match Ennis :
companies = ["name1", "name2",...]
companies_re = re.compile(r"|".join([r"\b{}\b".format(name) for name in companies]))
Depending on how many matches per news item, you may want to use companies_re.search(artice) or companies_re.find_all(article).
Also for case insensitive matches pass re.I to compile.
If the only line you want to check is also always the one starting with company company Name: you can narrow down the search:
for line in all_lines:
if line.startswith("company Name:"):
name = companies_re.search(line)
if name:
...
break
It sounds like you need the Aho-Corasick algorithm. There is a nice and fast implementation for python here: https://pypi.python.org/pypi/pyahocorasick/
It will only do exact matching, so you would need to index both "Du pont" and "Dupont", for example. But that's not too hard, you can use the Wikidata to help you find aliases: for example, look at the aliases of Dupont's entry: it includes both "Dupont" and "Du pont".
Ok so let's assume you have the list of company names with their aliases:
import ahocorasick
A = ahocorasick.Automaton()
companies = ["google", "apple", "tesla", "dupont", "du pont"]
for idx, key in enumerate(companies):
A.add_word(key, idx)
Next, make the automaton (see the link above for details on the algorithm):
A.make_automaton()
Great! Now you can simply search for all companies in some text:
your_text = """
I love my Apple iPhone. Do you know what a Googleplex is?
I ate some apples this morning.
"""
for end_index, idx in A.iter(your_text.lower()):
print(end_index, companies[idx])
This is the output:
15 apple
49 google
74 apple
The numbers correspond to the index of the last character of the company name in the text.
Easy, right? And super fast, this algorithm is used by some variants of GNU grep.
Saving/loading the automaton
If there are a lot of company names, creating the automaton may take some time, so you may want to create it just once, save it to disk (using pickle), then load it every time you need it:
# create_company_automaton.py
# ... create the automaton (see above)
import pickle
pickle.dump(A, open('company_automaton.pickle', 'wb'))
In the program that will use this automaton, you start by loading the automaton:
# use_company_automaton.py
import ahocorasick
import pickle
A = pickle.load(open("company_automaton.pickle", "rb"))
# ... use the automaton
Hope this helps! :)
Bonus details
If you want to match "Apple" in "Apple releases a new iPhone" but not in "I ate an apple this morning", you are going to have a hard time. But it is doable: for example, you could gather a set of articles containing the word "apple" and about the company, and a set of articles not about the company, then identify words (or n-grams) that are more likely when it's about the company (e.g. "iPhone"). Unfortunately you would need to do this for every company whose name is ambiguous.
You can try
difflib.get_close_matches
with the full company name.