Replace words marked with offsets - string

I have a sentence like that:
"My name is Bond. It's a fake name."
and I have to replace some words in a list with offsets of each word:
name, 29-33; Bond, 11-15; name, 3-7
In addition, each word must replace with a specific word:
name -> noun
Bond -> proper
I have to obtain this output:
"My noun is proper. It's a fake noun."
I tried to manage the offsets with a post-offset variable that I update after each replacement but it is not valid because is an unordered list. Note that find method is not valid due to names repetition. Is there any algorithm to do it? Any vectorial implementation (String, Numpy, NLTK) that computes it in one step?


Bro Check this one :
string = "My name is Bond. It's a fake name."
y=list()
y=string.split(" ") #now it will break your strings into words
Now traverse the list and set the condition
for i in y:
if(i==name):
i="noun"
if(i==Bond):
i="Proper"
Now the list values will be changed and use the Join() method to make back the list into string
For more Please refer to this website https://www.tutorialspoint.com/python/python_strings.htm
This page contains all the data related to string processing in python.

Related

How do I take a string and turn it into a list using SCALA?

I am brand new to Scala and having a tough time figuring this out.
I have a string like this:
a = "The dog crossed the street"
I want to create a list that looks like below:
a = List("The","dog","crossed","the","street")
I tried doing this using .split(" ") and then returning that, but it seems to do nothing and returns the same string. Could anyone help me out here?

It's safer to split() on one-or-more whitespace characters, just in case there are any tabs or adjacent spaces in the mix.
split() returns an Array so if you want a List you'll need to convert it.
"The dog\tcrossed\nthe street".split("\\s+").toList
//res0: List[String] = List(The, dog, crossed, the, street)

String matching keywords and key phrases in Python

I am trying to perform a smart dynamic lookup with strings in Python for a NLP-like task. I have a large amount of similar-structure sentences that I would like to parse through each, and tokenize parts of the sentence. For example, I first parse a string such as "bob goes to the grocery store".
I am taking this string in, splitting it into words and my goal is to look up matching words in a keyword list. Let's say I have a list of single keywords such as "store" and a list of keyword phrases such as "grocery store".
sample = 'bob goes to the grocery store'
keywords = ['store', 'restaurant', 'shop', 'office']
keyphrases = ['grocery store', 'computer store', 'coffee shop']
for word in sample.split():
# do dynamic length lookups
Now the issue is this Sometimes my sentences might be simply "bob goes to the store" instead of "bob goes to the grocery store".
I want to find the keyword "store" for sure but if there are descriptive words such as "grocery" or "computer" before the word store I would like to capture that as well. That is why I have the keyphrases list as well. I am trying to figure out a way to basically capture a keyword at the very least then if there are words related to it that might be a possible "phrase" I want to capture those too.
Maybe an alternative is to have some sort of adjective list instead of a phrase list of multiple words?
How could I go about doing these sort of variable length lookups where I look at more than just a single word if one is captured, or is there an entirely different method I should be considering?

Here is how you can use a nested for loop and a formatted string:
sample = 'bob goes to the grocery store'
keywords = ['store', 'restaurant', 'shop', 'office']
keyphrases = ['grocery', 'computer', 'coffee']
for kw in keywords:
for kp in keyphrases:
if f"{kp} {kw}" in sample:
# Do something

How to extract relationships from a text

I am currently new with NLP and need guidance as of how I can solve this problem.
I am currently doing a filtering technique where I need to brand data in a database as either being correct or incorrect. I am given a structured data set, with columns and rows.
However, the filtering conditions are given to me in a text file.
An example filtering text file could be the following:
Values in the column ID which are bigger than 99
Values in the column Cash which are smaller than 10000
Values in the column EndDate that are smaller than values in StartDate
Values in the column Name that contain numeric characters
Any value that follows those conditions should be branded as bad.
However, I want to extract those conditions and append them to the program that I've made so far.
For instance, for the conditions above, I would like to produce
`if ID>99`
`if Cash<10000`
`if EndDate < StartDate`
`if Name LIKE %[1-9]%`
How can I achieve the above result using the Stanford NLP? (or any other NLP library).

This doesn't look like a machine learning problem; it's a simple parser. You have a simple syntax, from which you can easily extract the salient features:
column name
relationship
target value or target column
The resulting "action rule" is simply removing the "syntactic sugar" words and converting the relationship -- and possibly the target value -- to its symbolic form.
Enumerate all of your critical words for each position in a lexicon. Then use basic string manipulation operators in your chosen implementation language to find the three needed fields.
EXAMPLE
Given the data above, your lexicons might be like this:
column_trigger = "Values in the column"
relation_dict = {
"are bigger than" : ">",
"are smaller than" : "<",
"contain" : "LIKE",
...
}
value_desc = {
"numeric characters" : "%[1-9]%",
...
}
From here, use these items in standard parsing. If you're not familiar with that, please look up the basics of a simple sentence grammar in your favourite programming language, with rules such as such as
SENTENCE => SUBJ VERB OBJ
Does that get you going?

Lua: Search a specific string

Hi all tried all the string pattrens and library arguments but still stuck.
i want to get the name of the director from the following string i have tried the string.matcH but it matches the from the first character it finD from the string
the string is...
fixstrdirector = {id:39254,cast:[{id:15250,name:Hope Davis,character:Aunt Debra,order:5,cast_id:10,profile_path:/aIHF11Ss8P0A8JUfiWf8OHPVhOs.jpg},{id:53650,name:Anthony Mackie,character:Finn,order:3,cast_id:11,profile_path:/5VGGJ0Co8SC94iiedWb2o3C36T.jpg},{id:19034,name:Evangeline Lilly,character:Bailey Tallet,order:2,cast_id:12,profile_path:/oAOpJKgKEdW49jXrjvUcPcEQJb3.jpg},{id:6968,name:Hugh Jackman,character:Charlie Kenton,order:0,cast_id:13,profile_path:/wnl7esRbP3paALKn4bCr0k8qaFu.jpg},{id:79072,name:Kevin Durand,character:Ricky,order:4,cast_id:14,profile_path:/c95tTUjx5T0D0ROqTcINojpH6nB.jpg},{id:234479,name:Dakota Goyo,character:Max Kenton,order:1,cast_id:15,profile_path:/7PU6n4fhDuFwuwcYVyRNVEZE7ct.jpg},{id:8986,name:James Rebhorn,character:Marvin,order:6,cast_id:16,profile_path:/ezETMv0YM0Rg6YhKpu4vHuIY37D.jpg},{id:930729,name:Marco Ruggeri,character:Cliff,order:7,cast_id:17,profile_path:/1Ox63ukTd2yfOf1LVJOMXwmeQjO.jpg},{id:19860,name:Karl Yune,character:Tak Mashido,order:8,cast_id:18,profile_path:/qK315vPObCNdywdRN66971FtFez.jpg},{id:111206,name:Olga Fonda,character:Farra Lemkova,order:9,cast_id:19,profile_path:/j1qabOHf3Pf82f1lFpUmdF5XvSp.jpg},{id:53176,name:John Gatins,character:Kingpin,order:10,cast_id:41,profile_path:/A2MqnSKVzOuBf8MVfNyve2h2LxJ.jpg},{id:1126350,name:Sophie Levy,character:Big Sister,order:11,cast_id:42,profile_path:null},{id:1126351,name:Tess Levy,character:Little Sister,order:12,cast_id:43,profile_path:null},{id:1126352,name:Charlie Levy,character:Littlest Sister,order:13,cast_id:44,profile_path:null},{id:187983,name:Gregory Sims,character:Bill Panner,order:14,cast_id:45,profile_path:null}],crew:[{id:58726,name:Leslie Bohem,department:Writing,job:Screenplay,profile_path:null},{id:53176,name:John Gatins,department:Writing,job:Screenplay,profile_path:/A2MqnSKVzOuBf8MVfNyve2h2LxJ.jpg},{id:17825,name:Shawn Levy,department:Directing,job:Director,profile_path:/7f2f8EXdlWsPYN0HPGcIlG21xU.jpg},{id:12415,name:Richard Matheson,department:Writing,job:Story,profile_path:null},{id:57113,name:Dan Gilroy,department:Writing,job:Story,profile_path:null},{id:25210,name:Jeremy Leven,department:Writing,job:Story,profile_path:null},{id:17825,name:Shawn Levy,department:Production,job:Producer,profile_path:/7f2f8EXdlWsPYN0HPGcIlG21xU.jpg},{id:34970,name:Susan Montford,department:Production,job:Producer,profile_path:/1XJt51Y9ciPhkHrAYE0j6Jsmgji.jpg},{id:3183,name:Don Murphy,department:Production,job:Producer,profile_path:null},{id:34967,name:Rick Benattar,department:Production,job:Producer,profile_path:null},{id:1126348,name:Eric Hedayat,department:Production,job:Producer,profile_path:null},{id:186721,name:Ron Ames,department:Production,job:Producer,profile_path:null},{id:10956,name:Josh McLaglen,department:Production,job:Executive Producer,profile_path:null},{id:57634,name:Mary McLaglen,department:Production,job:Executive Producer,profile_path:null},{id:23779,name:Jack Rapke,department:Production,job:Executive Producer,profile_path:null},{id:488,name:Steven Spielberg,department:Production,job:Executive Producer,profile_path:/cuIYdFbEe89PHpoiOS9tmo84ED2.jpg},{id:30,name:Steve Starkey,department:Production,job:Executive Producer,profile_path:null},{id:24,name:Robert Zemeckis,department:Production,job:Executive Producer,profile_path:/isCuZ9PWIOyXzdf3ihodXzjIumL.jpg},{id:531,name:Danny Elfman,department:Sound,job:Original Music Composer,profile_path:/pWacZpYPos8io22nEiim7d3wp2j.jpg},{id:18265,name:Mauro Fiore,department:Crew,job:Cinematography,profile_path:null},{id:54271,name:Dean Zimmerman,department:Editing,job:Editor,profile_path:null},{id:25365,name:Richard Hicks,department:Production,job:Casting,profile_path:null},{id:5490,name:David Rubin,department:Production,job:Casting,profile_path:null},{id:52088,name:Tom Meyer,department:Art,job:Production Design,profile_path:null}]}
i have tried string.match(fixstrdirector,"name:(.+),department:Directing")
but it gives me the from the first occurace it find the name to the end of thr string
output:
Hope Davis,character:Aunt Debra,order:5,cast_id:10,profile_path:/aIHF11Ss8P0A8JUfiWf8OHPVhOs.jpg},{id:53650,name:Anthony Mackie,character:Finn,order:3,cast_id:11,profile_path:/5VGGJ0Co8SC94iiedWb2o3C36T.jpg},{id:19034,name:Evangeline Lilly,character:Bailey Tallet,order:2,cast_id:12,profile_path:/oAOpJKgKEdW49jXrjvUcPcEQJb3.jpg},{id:6968,name:Hugh Jackman,character:Charlie Kenton,order:0,cast_id:13,profile_path:/wnl7esRbP3paALKn4bCr0k8qaFu.jpg},{id:79072,name:Kevin Durand,character:Ricky,order:4,cast_id:14,profile_path:/c95tTUjx5T0D0ROqTcINojpH6nB.jpg},{id:234479,name:Dakota Goyo,character:Max Kenton,order:1,cast_id:15,profile_path:/7PU6n4fhDuFwuwcYVyRNVEZE7ct.jpg},{id:8986,name:James Rebhorn,character:Marvin,order:6,cast_id:16,profile_path:/ezETMv0YM0Rg6YhKpu4vHuIY37D.jpg},{id:930729,name:Marco Ruggeri,character:Cliff,order:7,cast_id:17,profile_path:/1Ox63ukTd2yfOf1LVJOMXwmeQjO.jpg},{id:19860,name:Karl Yune,character:Tak Mashido,order:8,cast_id:18,profile_path:/qK315vPObCNdywdRN66971FtFez.jpg},{id:111206,name:Olga Fonda,character:Farra Lemkova,order:9,cast_id:19,profile_path:/j1qabOHf3Pf82f1lFpUmdF5XvSp.jpg},{id:53176,name:John Gatins,character:Kingpin,order:10,cast_id:41,profile_path:/A2MqnSKVzOuBf8MVfNyve2h2LxJ.jpg},{id:1126350,name:Sophie Levy,character:Big Sister,order:11,cast_id:42,profile_path:null},{id:1126351,name:Tess Levy,character:Little Sister,order:12,cast_id:43,profile_path:null},{id:1126352,name:Charlie Levy,character:Littlest Sister,order:13,cast_id:44,profile_path:null},{id:187983,name:Gregory Sims,character:Bill Panner,order:14,cast_id:45,profile_path:null}],crew:[{id:58726,name:Leslie Bohem,department:Writing,job:Screenplay,profile_path:null},{id:53176,name:John Gatins,department:Writing,job:Screenplay,profile_path:/A2MqnSKVzOuBf8MVfNyve2h2LxJ.jpg},{id:17825,name:Shawn Levy

You're searching from the first occurrence of "name:" until the "department:Directing" with everything in between.
Instead, you need to restrict what can be between the two strings. Here for example I'm saying that the characters that make up the name can only be alphanumeric or a space:
string.match(fixstrdirector,"name:([%w ]+),department:Directing")
Alternatively, given that there's a comma separating the parameters, a better approach would be to search for "name:" followed by any characters other than a comma, followed by "department:Directing":
string.match(fixstrdirector,"name:([^,]+),department:Directing")
Of course that wouldn't work if the name had a comma it in!

Lua patterns provides - modifier for tasks as you have above. As stated on PiL - Section 20.2:
The + modifier matches one or more characters of the original class.
It will always get the longest sequence that matches the pattern.
Like *, the modifier - also matches zero or more occurrences of
characters of the original class. However, instead of matching the
longest sequence, it matches the shortest one.
Next, when you are using . to match, it'll find any and all characters satisfying the pattern. Therefore, you'll get the result from first occurence of name until the ,department:Directing is found. Since you know that it is a JSON data, you can try to match for [^,]; that is, non-comma characters.
So, for your case try:
local tAllNames = {}
for sName in fixstrdirector:gmatch( "name:([^,]-),department:Directing" ) do
tAllNames[ #tAllNames + 1 ] = sName
end
and all your required names will be stored in the table tAllNames. An example of the above can be seen at codepad.

Access list element using get()

I'm trying to use get() to access a list element in R, but am getting an error.
example.list <- list()
example.list$attribute <- c("test")
get("example.list") # Works just fine
get("example.list$attribute") # breaks
## Error in get("example.list$attribute") :
## object 'example.list$attribute' not found
Any tips? I am looping over a vector of strings which identify the list names, and this would be really useful.

Here's the incantation that you are probably looking for:
get("attribute", example.list)
# [1] "test"
Or perhaps, for your situation, this:
get("attribute", eval(as.symbol("example.list")))
# [1] "test"
# Applied to your situation, as I understand it...
example.list2 <- example.list
listNames <- c("example.list", "example.list2")
sapply(listNames, function(X) get("attribute", eval(as.symbol(X))))
# example.list example.list2
# "test" "test"

Why not simply:
example.list <- list(attribute="test")
listName <- "example.list"
get(listName)$attribute
# or, if both the list name and the element name are given as arguments:
elementName <- "attribute"
get(listName)[[elementName]]

If your strings contain more than just object names, e.g. operators like here, you can evaluate them as expressions as follows:
> string <- "example.list$attribute"
> eval(parse(text = string))
[1] "test"
If your strings are all of the type "object$attribute", you could also parse them into object/attribute, so you can still get the object, then extract the attribute with [[:
> parsed <- unlist(strsplit(string, "\\$"))
> get(parsed[1])[[parsed[2]]]
[1] "test"

flodel's answer worked for my application, so I'm gonna post what I built on it, even though this is pretty uninspired. You can access each list element with a for loop, like so:
#============== List with five elements of non-uniform length ================#
example.list=
list(letters[1:5], letters[6:10], letters[11:15], letters[16:20], letters[21:26])
#===============================================================================#
#====== for loop that names and concatenates each consecutive element ========#
derp=c(); for(i in 1:length(example.list))
{derp=append(derp,eval(parse(text=example.list[i])))}
derp #Not a particularly useful application here, but it proves the point.
I'm using code like this for a function that calls certain sets of columns from a data frame by the column names. The user enters a list with elements that each represent different sets of column names (each set is a group of items belonging to one measure), and the big data frame containing all those columns. The for loop applies each consecutive list element as the set of column names for an internal function* applied only to the currently named set of columns of the big data frame. It then populates one column per loop of a matrix with the output for the subset of the big data frame that corresponds to the names in the element of the list corresponding to that loop's number. After the for loop, the function ends by outputting that matrix it produced.
Not sure if you're looking to do something similar with your list elements, but I'm happy I picked up this trick. Thanks to everyone for the ideas!
"Second example" / tangential info regarding application in graded response model factor scoring:
Here's the function I described above, just in case anyone wants to calculate graded response model factor scores* in large batches...Each column of the output matrix corresponds to an element of the list (i.e., a latent trait with ordinal indicator items specified by column name in the list element), and the rows correspond to the rows of the data frame used as input. Each row should presumably contain mutually dependent observations, as from a given individual, to whom the factor scores in the same row of the ouput matrix belong. Also, I feel I should add that if all the items in a given list element use the exact same Likert scale rating options, the graded response model may be less appropriate for factor scoring than a rating scale model (cf. http://www.rasch.org/rmt/rmt143k.htm).
'grmscores'=function(ColumnNameList,DataFrame) {require(ltm) #(Rizopoulos,2006)
x = matrix ( NA , nrow = nrow ( DataFrame ), ncol = length ( ColumnNameList ))
for(i in 1:length(ColumnNameList)) #flodel's magic featured below!#
{x[,i]=factor.scores(grm(DataFrame[, eval(parse(text= ColumnNameList[i]))]),
resp.patterns=DataFrame[,eval(parse(text= ColumnNameList[i]))])$score.dat$z1}; x}
Reference
*Rizopoulos, D. (2006). ltm: An R package for latent variable modelling and item response theory analyses, Journal of Statistical Software, 17(5), 1-25. URL: http://www.jstatsoft.org/v17/i05/

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