What I want to do is to parse raw natural text and find all the phrases that describe dates.
I've got a fairly big corpus with all the references to dates marked up:
I met him <date>yesterday</date>.
Roger Zelazny was born <date>in 1937</date>
He'll have a hell of a hangover <date>tomorrow morning</date>
I don't want to interpret the date phrases, just locate them. The fact that they're dates is irrelevant (in real life they're not even dates but I don't want to bore you with the details), basically it's just an open-ended set of possible values. The grammar of the values themselves can be approximated as context-free, however it's quite complicated to build manually and with increasing complexity it gets increasingly hard to avoid false positives.
I know this is a bit of a long shot so I'm not expecting an out-of-the-box solution to exist out there, but what technology or research can I potentially use?
One of the generic approaches used in academia and in industry is based on Conditional Random Fields. Basically, it is a special probabilistic model, you train it first with your marked up data and then it can label certain types of entities in a given text.
You can even try one of the systems from Stanford Natural Language Processing Group: Stanford Named Entity Recognizer
When you download the tool, note there are several models, you need the last one:
Included with the Stanford NER are a 4 class model trained for CoNLL,
a 7 class model trained for MUC, and a 3 class model trained on both
data sets for the intersection of those class sets.
3 class Location, Person, Organization
4 class Location, Person, Organization, Misc
7 class Time, Location, Organization, Person, Money, Percent, Date
Update. You can actually try that tool online here. Select the muc.7class.distsim.crf.ser.gz classifier and try some text with dates. It doesn't seem to recognize "yesterday", but it recognizes "20th century", for example. In the end, this is a matter of CRF training.
Keep in mind CRFs are rather slow to train and require human-annotated data, so doing it yourself is not easy. Read the answers to this for another example of how people often do it in practice- not much in common with current academic research.
Related
Which method of text analysis should I use if I need to get a number of multiword keywords, say (up to) 5 per text, analysing a scientific text of some length? In particular, the text could be
a title,
or an abstract.
Preferably a method already scripted on Python.
Thank you!
You could look into keyword extraction, collocation finding or text summarization. Depending on what you want to use it for you could also look into general terminology extraction. These are just some methods, there are also other approaches like topic modeling etc.
Collocation finding/terminology extraction are more about finding domain-specific terminology and require a larger amount of corpora, but they can help to unify the generated tags. Basically you would first run this kind of analysis to find ngrams which are domain-specific and therefore in scientific literature indicative of the topic and in a second step you would mark the occurence of these extracted ngrams in the original texts.
Keyword extraction and text summarization lean more towards being applied to single texts, but obviously the resulting tags are going to be less unified.
It's difficult to say which method makes the most sense for you as this depends on the amount of data you have, the diversity of topics within the data you have, what you are planning to do with the keywords/tags and how much time you want to spend to optimize this extraction.
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 have a collection of bills and Invoices, so there is no context in the text (i mean they don't tell a story).
I want to extract people names from those bills.
I tried OpenNLP but the quality of trained model is not good because i don't have context.
so the first question is: can I train model contains only people names without context? and if that possible can you give me good article for how i build that new model (most of the article that i read didn't explain the steps that i should made to build new model).
I have database name with more than 100,000 person name (first name, last name), so if the NER systems don't work in my case (because there is no context), what is the best way to search for those candidates (I mean searching for every first name with all other last names?)
thanks.
Regarding "context", I guess you mean that you don't have entire sentences, i.e. no previous / next tokens, and in this case you face quite a non-standard NER. I am not aware of available software or training data for this particular problem, if you found none you'll have to build your own corpus for training and/or evaluation purposes.
Your database of names will probably greatly help, depending indeed on what proportion of bill names are actually present in the database. You'll also probably have to rely on character-level morphology of names, as patterns (see for instance patterns in [1]). Once you have a training set with features (presence in database, morphology, other information of bill) and solutions (actual names of annotated bills), using standard machine-learning as SVM will be quite straightforward (if you are not familiar with this, just ask).
Some other suggestions:
You may most probably also use other bill's information: company name, positions, tax mentions, etc.
You may also proceed in a a selective manner - if all bills should mention (exactly?) one person name, you may exclude all other texts (e.g. amounts, tax names, positions etc.) or assume in a dedicated model that among all text in a bill, only one should be guessed as a name.
[1] Ranking algorithms for named-entity extraction: Boosting and the voted perceptron (Michael Collins, 2002)
I'd start with some regular expressions, then possibly augment that with a dictionary-based approach (i.e., big list of names).
No matter what you do, it won't be perfect, so be sure to keep that in mind.
I want to manually implement a classifier for certain short strings of words, getting a "goodness" rank for each of them. I have made a naive Bayesian classifier which is basically spam-filter-like and scores strings based on previous "good"/"bad" ratings. So far so good.
Now, there are two problems that I want to solve (by properly understanding things)...
The question is - what would be good introductory material for below, not of "cookbook" variety but more systematic, and yet ideally shorter than a university statistics course :) Set of articles that is shorter than the book, or a good book. Aimed at programmers ideally.
The problems are:
first, in my system there are actually 3 types of user feedback - "good", "bad", and "neutral". Most items are neutral, and right now I simply don't include them in the ranking. I am wondering how these things are properly handled (I still need to obtain a single "goodness probability" per item, so if I calculate probability of good and bad separately, are there any pitfalls/proper methods to combining those).
Then, I want to remove the naive part from my classifier (i.e. take relations between words into account), so some different classifier may be in order. Or, I could add all pairs-triples-etc. of words as features, since the strings are short - this feels like a hack, but then again my CS/maths background is rusty enough and/or insufficient to say whether this is a valid technique.
I just have a set of sentences, which I have generated based on painting analysis. However I need to test how natural they sound. Is there any api or application which does this?
I am using the Standford Parser to give me a breakdown, but this doesn't exactly do the job I want!
Also can one test how similar sentences are? As I randomly generating parts of sentences and want to check the variety of the sentences produced.
A lot of NLP stuff works using things called 'Language Models'.
A language model is something that can take in some text and return a probability. This probability should typically be indicative of how "likely" the given text is.
You typically build a language model by taking a large chunk of text (which we call the "training corpus") and computing some statistics out of it (which represent your "model"), and then using those statistics to take in new, previously unseen sentences and returning probabilities for them.
You should probably google for "language models", "unigram models", "n-gram models" and click on some of the results to find some article or presentation which helps you understand the previous sentence. (Its hard for me to recommend an appropriate tutorial for you because I don't know what your existing background is)
Anyway, one way to think about language models is that they are systems that take in new text and tell you how similar the new text is to the training corpus the language model was made out of. So if you build 2 language models, one out of all the plays written by Shakespeare and another out of a large number of legal documents, then the second one should be giving you a much higher probability to sentences for some new legal document that just got released (as compared to the first model) while the first model should give you a much higher probability for some other old english play (written by some other author) because that play is probably more similar to Shakespeare (in terms of the kind of words used, sentence lengths, grammar, etc) than it is to modern legal language.
All the things you see the stanford parser give you back for a sentence you give it are generated using language models. One way to think about how those features are built is to pretend that the computer tried every possible combination of tags and every possible parse tree for the sentence you gave it, and used some clever language model to identify which is most probable sequence of tags and most probable parse tree out there, and returned those back to you.
Getting back to your problem, you need to build a language model out of what you consider natural sounding text and then use that language model to evaluate the sentences you want to measure the naturalness of. To do this, you will have to identify a good training corpus and decide on what type of language model you want to build.
If you can't think of anything better, a collection of wikipedia articles might serve to be a good training corpus representing what natural sounding english looks like.
As for model type, an "n-gram model" would probably be good enough for your task. More complicated models like "Hidden Markov Models" and "PCFG's" (the stuff that is powering the stanford page you linked to) would definitely make things even better, but n-grams are definitely the most simple thing you could start with.