I got a list of sentences like below:
They are some sentences I extracted from job descriptions. I want to extract information like: degree type, major, required or preferred.
There are
The result should be like :
{
degree: Bachelor,
major : Computer Science,
required: True
}
Thers are no obvious rules in these sentences. How can I achieve this goal?
Bachelor ’ s degree in Computer Science or equivalent
Pursuing B.S. or advanced degree in computer science or related technical/engineering degree .
Bachelor 's Degree in Computer Science or equivalent experience
Youre educated ( BS/MS in Computer Science or other technical degree ) .
•BS in Computer Science , Digital Media or similar technical degree with 3 + years of experience
· Bachelors degree .
Bachelor 's degree in computer science , design or related field
Ability to absorb , master and leverage emerging technologies
BA/BS degree or equivalent practical experience
Education Required : Bachelors Degree
• Bachelor 's degree in related field , OR four ( 4 ) years of experience in a directly related field .
So you are dealing with unstructured data, I hope using following steps you may reach to a decent accuracy level.
Create a lookup table of list of all keywords that may occur in each of your required variables like degree, education etc. You need to mine various online sources to grab these keywords.
Split your data into sentence or line by line and Iterate over the list.
While iterating, look for the key words into your lookup tables and find the useful lines.
Create hierarchal rules to accurately extract the variables, and append them in your result.
Overview of hierarchal rules:
for example, Degree name will be completely alphabetic.
Experience will be alphanumeric.
Terms like pursuing will point towards variable name Major
Try to modify these rules on each iteration of code. Keep adding new rules.
This is just the basic approach, I believe that if you do some iterations over your methodology, you will be able to extract information.
You probably need to gather a list of majors and degrees (for example : http://en.wikipedia.org/wiki/List_of_tagged_degrees ) to extract the degree and major. Then based on some general rules (or designing a classifier decide on "required" or "not required").
Another suggestion to do this would be:
First: clean up the data - remove all punctuation, stop
words,unwanted symbols etc.
Second: make a list of keywords are interested in.
Third: split your data into words (word_tokenize in nltk)
Fourth: make a dictionary of values you are looking in.
Fifth: lookup in the dictionary as you read the words list matching
it with your keywords list and then append it into new output
dictionary.
Hope this helps.
Related
Preface: I'm a self-taught coder, so a lot of my knowledge is limited to my research. I'm hoping to have other opinions as I want to build things right the first time. I need help with determining an appropriate solution and how to implement the solution.
I'm looking to build a least cost alternative model (essentially a shortest path) for delivering between locations (nodes), based on different modes of transportation (vehicles) and the different roads taken (paths). Another consideration is the product price (value) to determine the least cost path.
Here are my important data items:
nodes: cities where the product will travel to and from.
paths: roads have different costs, depending on the road.
vehicles: varying vehicles have differing rental costs when transporting (motorbike, car, truck). Note that the cost of a vehicle is not constant, it is highly dependent on the to/from nodes. For example, using a car to go from city A to city B will have a different cost than using a car to go from city B to A or city A to city C.
value: Product value. Again, a product's value is highly dependent on its destination node. The same product can have a different value at City A, B or C.
Problem Statement
How to setup data structure to best determine where the least cost path would be to get a product from one location to every other location.
Possible Solutions
From my research, I believe a weighted graph data structure would be most suitable for my situation in combination with dijkstra's algorithm. I believe breaking the problem down simpler would be essential, to first create a simple weighted graph of only nodes and paths.
From there, adding the vehicle cost and the product value considerations afterwards. Perhaps just adding the two values as a cost to "visit" a node? (aka incorporate it into the path cost?)
Thoughts on my current solution? Other considerations I overlooked? Perhaps a better solution?
Implementation
I'd love to be able to build this within Excel VBA (as that is how I learned how to code) and Excel is what I use for my tools. Would VBA be too limited in this task? How else can I incorporate my analysis with Excel with another language?
Try the book Practical Management Science by Winston & Albright and check out the chapter on Operations Management - lots of models explained in there from the simple onwards. Available online as a pdf : http://ingenieria-industrial.net/downloads/practicalmanagementscience.pdf
VBA is more a scripting language than a full-fledged one, though one may contend that the underlying framework is .NET. Why don't you give a shot at C++ or Java? If you intuitively understand the data structure and the algorithm, then it'll be a breeze coding in these. Chapter 4 of Algorithms by Sedgewick and Wayne has a beautiful explanation of Shortest Paths. You may also consider studying Bellman-Ford algorithm if you foresee any negative weight cycles on a vertex.
I'm embarking on a project for a non-profit organization to help process and classify 1000's of reports annually from their field workers / contractors the world over. I'm relatively new to NLP and as such wanted to seek the group's guidance on the approach to solve our problem.
I'll highlight the current process, and our challenges and would love your help on the best way to solve our problem.
Current process: Field officers submit reports from locally run projects in the form of best practices. These reports are then processed by a full-time team of curators who (i) ensure they adhere to a best-practice template and (ii) edit the documents to improve language/style/grammar.
Challenge: As the number of field workers increased the volume of reports being generated has grown and our editors are now becoming the bottle-neck.
Solution: We would like to automate the 1st step of our process i.e., checking the document for compliance to the organizational best practice template
Basically, we need to ensure every report has 3 components namely:
1. States its purpose: What topic / problem does this best practice address?
2. Identifies Audience: Who is this for?
3. Highlights Relevance: What can the reader do after reading it?
Here's an example of a good report submission.
"This document introduces techniques for successfully applying best practices across developing countries. This study is intended to help low-income farmers identify a set of best practices for pricing agricultural products in places where there is no price transparency. By implementing these processes, farmers will be able to get better prices for their produce and raise their household incomes."
As of now, our approach has been to use RegEx and check for keywords. i.e., to check for compliance we use the following logic:
1 To check "states purpose" = we do a regex to match 'purpose', 'intent'
2 To check "identifies audience" = we do a regex to match with 'identifies', 'is for'
3 To check "highlights relevance" = we do a regex to match with 'able to', 'allows', 'enables'
The current approach of RegEx seems very primitive and limited so I wanted to ask the community if there is a better way to solving this problem using something like NLTK, CoreNLP.
Thanks in advance.
Interesting problem, i believe its a thorough research problem! In natural language processing, there are few techniques that learn and extract template from text and then can use them as gold annotation to identify whether a document follows the template structure. Researchers used this kind of system for automatic question answering (extract templates from question and then answer them). But in your case its more difficult as you need to learn the structure from a report. In the light of Natural Language Processing, this is more hard to address your problem (no simple NLP task matches with your problem definition) and you may not need any fancy model (complex) to resolve your problem.
You can start by simple document matching and computing a similarity score. If you have large collection of positive examples (well formatted and specified reports), you can construct a dictionary based on tf-idf weights. Then you can check the presence of the dictionary tokens. You can also think of this problem as a binary classification problem. There are good machine learning classifiers such as svm, logistic regression which works good for text data. You can use python and scikit-learn to build programs quickly and they are pretty easy to use. For text pre-processing, you can use NLTK.
Since the reports will be generated by field workers and there are few questions that will be answered by the reports (you mentioned about 3 specific components), i guess simple keyword matching techniques will be a good start for your research. You can gradually move to different directions based on your observations.
This seems like a perfect scenario to apply some machine learning to your process.
First of all, the data annotation problem is covered. This is usually the most annoying problem. Thankfully, you can rely on the curators. The curators can mark the specific sentences that specify: audience, relevance, purpose.
Train some models to identify these types of clauses. If all the classifiers fire for a certain document, it means that the document is properly formatted.
If errors are encountered, make sure to retrain the models with the specific examples.
If you don't provide yourself hints about the format of the document this is an open problem.
What you can do thought, is ask people writing report to conform to some format for the document like having 3 parts each of which have a pre-defined title like so
1. Purpose
Explains the purpose of the document in several paragraph.
2. Topic / Problem
This address the foobar problem also known as lorem ipsum feeling text.
3. Take away
What can the reader do after reading it?
You parse this document from .doc format for instance and extract the three parts. Then you can go through spell checking, grammar and text complexity algorithm. And finally you can extract for instance Named Entities (cf. Named Entity Recognition) and low TF-IDF words.
I've been trying to do something very similar with clinical trials, where most of the data is again written in natural language.
If you do not care about past data, and have control over what the field officers write, maybe you can have them provide these 3 extra fields in their reports, and you would be done.
Otherwise; CoreNLP and OpenNLP, the libraries that I'm most familiar with, have some tools that can help you with part of the task. For example; if your Regex pattern matches a word that starts with the prefix "inten", the actual word could be "intention", "intended", "intent", "intentionally" etc., and you wouldn't necessarily know if the word is a verb, a noun, an adjective or an adverb. POS taggers and the parsers in these libraries would be able to tell you the type (POS) of the word and maybe you only care about the verbs that start with "inten", or more strictly, the verbs spoken by the 3rd person singular.
CoreNLP has another tool called OpenIE, which attempts to extract relations in a sentence. For example, given the following sentence
Born in a small town, she took the midnight train going anywhere
CoreNLP can extract the triple
she, took, midnight train
Combined with the POS tagger for example; you would also know that "she" is a personal pronoun and "took" is a past tense verb.
These libraries can accomplish many other tasks such as tokenization, sentence splitting, and named entity recognition and it would be up to you to combine all of these tools with your domain knowledge and creativity to come up with a solution that works for your case.
I am trying named entity recognition for the first time. I'm looking for features that will pick out English names. I am using the methods outlined in the coursera nlp course (week three) and the nltk book. In other words: I am defining features, identifying features of words and then running those words/features through a classifier that I train on labeled data.
What features are used to pick out English names?
I can imagine that you'd look for two capital words in a row, or a capital word and then an initial and then a capital word. (ex. John Smith or James P. Smith).
But what other features are used for NER?
Some common features:
Word lists for common names (John, Adam, etc)
casing
contains symbol or numeric characters (names generally don't)
person prefixes (Mr., Mrs., etc...)
person postfixes (Jr., Sr., etc...)
single letter abbreviation (ie, (J.) Smith).
analysis of surrounding words (you may find some words have a high probability of appearing near names).
Named Entities previously recognized (often it is easy to identify NE in some parts of the corpus based on context, but very hard in other parts. If previously identified, this is an excellent hint towards NER)
Depending what language you are working with there may be more language specific features as well. Frankly you can turn up a wealth of information with a simple Google query, I'm really not sure why you haven't turned there. Some starting points however:
Google
A survey of named entity recognition and classification
Named entity recognition without gazetteers
I had done something similar back in school using machine learning. I suppose that you will use a supervised algorithm and you will classify every single word independently and not words in combination. In that case I would choose some features for the word itself like the ones you mentioned (if the word begins with a capital letter, if the word is an abbreviation) but I would add some more features like if the previous or the next words also start from a capital letter, or if they are abbreviations. This way you can add some context and overcome the problems related to your basic independence assumption.
If you want have a look here. In the machine learning section you can find some more information and examples (the problem is slightly different but the method should be similar).
Whatever features you choose it is important that you use some measure to evaluate their relevance and possibly reduce them to the useful ones to avoid over-fitting. One of the measures you can use to evaluate them is the gain ratio but there are many more. Here you can find some basic information about feature extraction.
Hope it helps!
Given "violence" as input would it be possible to come up with how violence construed by a person (e.g. physical violence, a book, an album, a musical group ..) as mentioned below in Ref #1.
Assuming if the user meant an Album, what would be the best way to look for violence as an album from a set of tweets.
Is there a way to infer this via any of the NLP API(s) say OpenNLP.
Ref #1
violence/N1 - intentional harmful physical action.
violence/N2 - the property of being wild or turbulent.
Violence/N6 - a book from Neil L. Whitehead; nonfiction
Violence/N7 - an album by The Last Resort
Violence/N8 - Violence is the third album by the Washington-based Alternative metal music group Nothingface.
Violence/N9 - a musical group which produced the albums Eternal Nightmare and Nothing to Gain
Violence/N10 - a song by Aesthetic Perfection, Angel Witch, Arsenic, Beth Torbert, Brigada Flores Magon, etc on the albums A Natural Disaster, Adult Themes for Voice, I Bificus, Retribution, S.D.E., etc
Violence/N11 - an album by Bombardier, Dark Quarterer and Invisible Limits
Violence/N12 - a song by CharlElie Couture, EsprieM, Fraebbblarnir, Ian Hunter, Implant, etc on the albums All the Young Dudes, Broke, No Regrets, Power of Limits, Repercussions, etc
Violence/N18 - Violence: The Roleplaying Game of Egregious and Repulsive Bloodshed is a short, 32-page roleplaying game written by Greg Costikyan under the pseudonym "Designer X" and published by Hogshead Publishing as part of its New Style line of games.
Violence/N42 - Violence (1947) is an American drama film noir directed by Jack Bernhard.
Pure automatic inference is a little to hard in general for this problem.
Instead we might use :
Resources like WordNet, or a semantics dictionary.
For languages other than English you can look at eurowordnet (non free) dataset.
To get more meaning (i.e. for the album sense) we process some well managed resource like Wikipedia. Wikipedia as a lot of meta information that would be very useful for this kind of processing.
The reliability of the process is achieve just by combining the maximum number of data source and processing them correctly, with specialized programs.
As a last resort you may try hand processing/annotating. Long and costly, but useful in enterprise context where you need only a small part of a language.
No free lunch here.
If you're working on English NLP in python, then you can try the wordnet API as such:
from nltk.corpus import wordnet as wn
query = 'violence'
for ss in wn.synsets(query):
print query, str(ss.offset).zfill(8)+'-'+ss.pos, ss.definition
If you're working on other human languages, maybe you can take a look at the open wordnets available from http://casta-net.jp/~kuribayashi/multi/
NOTE: the reason for str(ss.offset).zfill(8)+'-'+ss.pos, it's because it is used as the unique id for each sense of a specific word. And this id is consistent across the open wordnets for every language. the first 8 digits gives the id and the character after the dash is the Part-of-Speech of the sense.
Check this out: Twitter Filtering Demo from Idilia. It does exactly what you want by first analyzing a piece of text to discover the meaning of its words and then filtering the texts that contain the sense that you are looking for. It's available as an API.
Disclaimer: I work for Idilia.
You can extract all contexts "violence" occurs in (context can be a whole document, or a window of say 50 words), then convert them into features (using say bag of words), then cluster these features. As clustering is unsupervised, you won't have names for the clusters, but you can label them with some typical context.
Then you need to see which cluster "violence" in the query belongs to. Either based on other words in the query which act as a context or by asking explicitly (Do you mean violence as in "...." or as in "....")
This will be incredibly difficult due to the fact that the proper noun uses of the word 'Violence' will be incredibly infrequent as a proportion of all words and their frequency distribution is likely highly skewed in some way. We run into these problems almost any time we want to do some form of Named Entity Disambiguation.
No tool I'm aware of will do this for you, so you will be building your own classifier. Using Wikipedia as a training resource as Mr K suggested is probably your best bet.
Help by editing my question title and tags is greatly appreciated!
Sometimes one participant in my corpus of "conversations" will refer to another participant using a nickname, usually an abbreviation or misspelling, but hereafter I'll just say "nicknames". Let's say I'm willing to manually tell my software whether or not I think various possible nicknames are in fact nicknames, but I want software to come up with a list of possible matches between the handle's that identify people, and the potential nicknames. How would I go about doing that?
Background on me and then my corpus: I have no experience doing natural language processing but I'm a competent data analyst with R. My data is produced by 70 teams, each forecasting the likelihood of 100 distinct events occurring some time in the future. The result that I have 70 x 100 = 7000 text files, containing the stream of forecasts participants make and the comments they include with their forecasts. I'll paste a very short snip of one of these text files below, this one had to do with whether the Malian government would enter talks with the MNLA:
02/12/2013 20:10: past_returns answered Yes: (50%)
I hadn't done a lot of research when I put in my previous
placeholder... I'm bumping up a lot due to DougL's forecast
02/12/2013 19:31: DougL answered Yes: (60%)
Weak President Traore wants talks if MNLA drops territorial claims.
Mali's military may not want talks. France wants talks. MNLA sugggests
it just needs autonomy. But in 7 weeks?
02/12/2013 10:59: past_returns answered No: (75%)
placeholder forecast...
http://www.irinnews.org/Report/97456/What-s-the-way-forward-for-Mali
My initial thoughts: Obviously I can start by providing the names I'm looking to match things up with... in the above example they would be past_returns and DougL (though there is no use of nicknames in the above). I wouldn't think it'd be that hard to get a computer to guess at minor misspellings (though I wouldn't personally know where to start). I can imagine that other tricks could be used, like assuming that a string is more likely to be a nickname if it is used much much more by one team, than by other teams. A nickname is more likely to refer to someone who spoke recently than someone who spoke long ago, or not at all on regarding this question. And they should be used in sentences in a manner similar to the way the full name/screenname is typically used in the corpus. But I'm interested to hear about simple approaches, as well as ones that try to consider more sophisticated techniques.
This could get about as complicated as you want to make it. From the semi-linguistic side of things, research topics would include Levenshtein Distance (for detecting minor misspellings of known names/nicknames) and Named Entity Recognition (for the task of detecting names/nicknames in the first place). Actually, NER's worth reading about, but existing systems might not help you much in your domain of forum handles and nicknames.
The first rough idea that comes to mind is that you could run a tokenized version of your corpus against an English dictionary (perhaps a dataset compiled from Wiktionary or something like WordNet) to find words that are candidates for names, then filter those through some heuristics (do they start with the same letters as known full names? Do they have a low Levenshtein distance from known names? Are they used more than once?).
You could also try some clustering or supervised ML algorithms against the non-word tokens. That might reveal some non-"word" tokens that often occur in the same threads as a given username; again, heuristics could help rule out some false positives.
Good luck; sounds like a fun problem - hope I mentioned at least one thing you hadn't already thought of.