I am looking for some general guidance here.
The high-level use case is such that I receive some product documents
from which I need to extract some information and process it. Before
doing that, I need to verify that the document is actually referring
to the correct product. For that I need to validate the product
heading/description from document against what I know to be correct.
So I have 2 texts
Text 1 - this refers to the product information extracted from some document
Text 2 - this is the actual product heading/description available with me, which can be considered as correct.
I need to validate that both texts refer to same product or object.
Example:
Text 1 (to be validated) - Optimus Prime Costume, Blue, with good packaging and warranty
Text 2 (correct info) - Optimus Prime Blue Costume, Medium Size`
You see, I need to validate that both text refer to Optimus Prime Costume.
I tried following methods -
Cosine Similarity
TF-IDF similarity
Overlapping words between strings
But the problem with them is that they depends on the entire text rather than the primary object being referred in the text.
I was thinking of processing as follows:-
Remove colors, size info etc. from the text 2. The text 2, is very concise and does not contain random data. It contains product name and size, colour info.
Validate that the remaining elements from Text 2 are present in Text 1, or atleast a majority of them are.
I am not quite sure what different NLP techniques might be there, which would be better than this approach, so any suggestions would be appreciated.
Depending on your target this can be medium-hard or very hard.
There are a couple of things you can use:
NER would definitely be helpful:
Wikifier could be useful too: http://cogcomp.org/page/demo_view/Wikifier
And also Semantic-Role-Labeling. See more annotations here: http://nlp.cogcomp.org/
It's hard to judge from one single example what is the exact algorithm, but if you have more examples might be easier to come up with a better formalization.
An extension of this can be found in what has been used
in this work.
Related
I have text data from two different groups. In total I have around 4000 text passages with around 300 words.
I am searching for a tool that allows me to analyze the difference between these two groups.
In the best case, this tool can analyze different dimensions, e.g. the length of sentences, usage of superlatives, perspective of the narrator, usage of passive form, clear and objective writing VS hedging and imprecise writing.
In Python, you can use the nltk or spacey packages to process the texts so that you can analyze them (using pandas, for example). But there's not ready-made software (as far as I know) that will do all of that for you. You're going to have to write your own code.
For example, you would create a pandas dataframe with a row for all of the texts, with their group ('A' or 'B' or whatever) as one of the columns and the raw text as the other. Then you use nltk to tokenize the text and do whatever other preprocessing you want to do, storing the clean, tokenized text in another column. Then you can have a column for, for example, sentence length (which you can compute using nltk). From there you'll be able to get the means of the two groups, standard deviation, statistical significance of difference, etc.
It's straightforward for something like sentence length, but the other features you mention are more difficult. What does it mean for a text to be clear and objective, or hedged and imprecise? That means nothing on its own: you have to decide what exactly you mean by that, and what features characterize it. For example, you could make a list of hedgers ('I think', 'may', 'might', 'I'm not sure but', etc.) and then count their frequency in each text.
Something like "perspective of the narrator" might need to be annotated manually, depending on what you mean by it. If you just mean 1st person vs. 3rd person, that could be easy to identify (compare the 'I's vs. the 'he/she's), but anything more subtle than that, I'm not sure how you'd do it.
Good luck with your project!
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 want to generate plausible (or less than plausible is okay too) nonsense text similar to the way that a markov chain approach would do, but I want the nouns and verbs of the generated text to come from a different source than the analyzed text.
So, for example, let's say that text 1 is from Little Red Riding Hood, and my list of nouns/verbs is something like the ones listed here: nouns, verbs. I'm looking for a way to swap out some/all of the nouns/verbs in text 1 with the new nouns/verbs. Then I would generate a new text from the mashup (perhaps using the markov chain approach).
I'm guessing that I need some sort of initial grammar analysis for text 1, and then perhaps do a swap with appropriately coded words of the insertion noun/verb lists?
I'm not familiar with text generation but I'd suggest a language modelling approach. You should check out the first 1-2 lectures for inspiration :)
You can try creating a language model, independent on the nouns and verbs (i.e. replacing them with _noun and _verb). Then you can try generating text from it, based on a factor of randomness since the suggested model just counts words and phrases.
I haven't tried it and I hope it works for you.
What are the recommended methods for extracting locations from free text?
What I can think of is to use regex rules like "words ... in location". But are there better approaches than this?
Also I can think of having a lookup hash table table with names for countries and cities and then compare every extracted token from the text to that of the hash table.
Does anybody know of better approaches?
Edit: I'm trying to extract locations from tweets text. So the issue of high number of tweets might also affect my choice for a method.
All rule-based approaches will fail (if your text is really "free"). That includes regex, context-free grammars, any kind of lookup... Believe me, I've been there before :-)
This problem is called Named Entity Recognition. Location is one of the 3 most studied classes (with Person and Organization). Stanford NLP has an open source Java implementation that is extremely powerful: http://nlp.stanford.edu/software/CRF-NER.shtml
You can easily find implementations in other programming languages.
Put all of your valid locations into a sorted list. If you are planning on comparing case-insensitive, make sure the case of your list already is normalized.
Then all you have to do is loop over individual "words" in your input text and at the start of each new word, start a new binary search in your location list. As soon as you find a no-match, you can skip the entire word and proceed with the next.
Possible problem: multi-word locations such as "New York", "3rd Street", "People's Republic of China". Perhaps all it takes, though, is to save the position of the first new word, if you find your bsearch leads you to a (possible!) multi-word result. Then, if the full comparison fails -- possibly several words later -- all you have to do is revert to this 'next' word, in relation to the previous one where you started.
As to what a "word" is: while you are preparing your location list, make a list of all characters that may appear inside locations. Only phrases that contain characters from this list can be considered a valid 'word'.
How fast are the tweets coming in? As in is it the full twitter fire hose or some filtering queries?
A bit more sophisticated approach, that is similar to what you described is using an NLP tool that is integrated to a gazetteer.
Very few NLP tools will keep up to twitter rates, and very few do very well with twitter because of all of the leet speak. The NLP can be tuned for precision or recall depending on your needs, to limit down performing lockups in the gazetteer.
I recommend looking at Rosoka(also Rosoka Cloud through Amazon AWS) and GeoGravy