I want to recognize named entities in a specific field (e.g. baseball). I know there are tools available like StanfordNER, LingPipe, AlchemyAPI and I have done a little testing with them. But what I want them to be is field specific as I mentioned earlier. How this is possible?
One approach may be to
Use a general (non-domain specific) tool to detect people's names
Use a subject classifier to filter out texts that are not in the domain
If the total size of the data set is sufficient and the accuracy of the extractor and classifier good enough, you can use the result to obtain a list of people's names that are closely related to the domain in question (e.g. by restricting the results to those that are mentioned significantly more often in domain-specific texts than in other texts).
In the case of baseball, this should be a fairly good way of getting a list of people related to baseball. It would, however, not be a good way to obtain a list of baseball players only. For the latter it would be necessary to analyse the precise context in which the names are mentioned and the things said about them; but perhaps that is not required.
Edit: By subject classifier I mean the same as what other people might refer to simply as categorization, document classification, domain classification, or similar. Examples of ready-to-use tools include the classifier in Python-NLTK (see here for examples) and the one in LingPipe (see here).
Have a look at smile-ner.appspot.com which covers 250+ categories. In particaul, it covers a lot of persons/teams/clubs on sports. May be useful for your purpose.
Related
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 need to categorize a text or word to a particular category. For example, the text 'Pink Floyd' should be categorized as 'music' or 'Wikimedia' as 'technology' or 'Einstein' as 'science'.
How can this be done? Is there a way I can use the DBpedia for the same? If not, the database has to be trained from time to time, right?
This is a text classification problem. Manning, Raghavan and Schütze's Information Retrieval book chapter is a nice introduction. I think you do not need DBPedia nor NER for this, just a small labeled training data set with enough labeled examples for all of your classes.
Yes, DBpedia may be a good choice for this kind of problem. You'll have to
squash the DBpedia category structure so you get the right granularity (e.g., Pink Floyd is listed under Capitol Records artists and a host of other categories, but not directly under Music). Maybe pick a few large categories and try to find whether your concepts are listed indirectly in them;
normalize text; Einstein is listed as Albert Einstein, not einstein
deal with ambiguity due to terms describing multiple concepts and concepts belonging to multiple top-level categories.
These problems may be solvable using machine learning, but I only see how it can be done if you extract these terms, along with relevant features, from running text. But in that case, you might just as well classify the entire text into one of the categories you choose in step 1.
This is the well-studied named entity recognition problem. Unless you have a particular need to roll your own technology (hint: it's a hard problem in general), using Gate, or perhaps one of the online services that builds on it (e.g. TSO's Data Enrichment Service), would be a good option. An alternative online service is OpenCalais.
Mapping your categries to DBPedia.
Index with lucene selected DBPedia categories and label data with your category names.
Do search for your data - tokenization, normalization will be done by Lucene.
This approach is somehow related to KNN classification.
Yes DBpedia is a good choice for text classification, as you can use its predicates/ relations to query and to extract the meaningful information for the particular category.
You can look into the endpoint for querying Dbpedia:
http://dbpedia.org/sparql
Further, learn the basic syntax of SPARQL to query on the endpoint from the following link:
http://www.w3.org/TR/rdf-sparql-query/
I am looking for a resource similar to WordNet. However, I want to be able to look up the positive/negative connotation of a word. For example:
bribe - negative
offer - positive
I'm curious as to whether anyone has run across any tool like this in AI/NLP research, or even in linguistics.
UPDATE:
For the curious, the accepted answer below put me on the right track towards what I needed. Wikipedia listed several different resources. The two I would recommend (because of ease of use/free use for a small number of API calls) are AlchemyAPI and Lymbix. I decided to go with AlchemyAPI, since people affiliated with academic institutions (like myself) and non-profits can get even more API calls per day if they just email the company.
Start looking up topics on 'sentiment analysis': http://en.wikipedia.org/wiki/Sentiment_analysis
The are some vocabulary compilations regarding affect, aka dictionaries of affect, such as the Affective Norms of English Words (ANEW) or the Dictionary of Affect in Language (DAL). They provide a dimensional representation of affect (valence, activation and control) that may be of use in a sentiment analysis scenario (detection of positive/negative connotation). In this sense, EmoLib works with the former, by default, but may be easily extended with a more specific lexicon to tackle particular needs (for example, EmoLib provides an additional neutral label that is more appropriate than the positive/negative tag set alone in a Text-To-Speech synthesis setting).
There is also SentiWordNet, which gives you positive, negative and objective scores for each WordNet synset.
However, you should be aware that the positive and negative connotation of a term often depends on the context in which it is used. A great introduction to this topic is the book Opinion mining and sentiment analysis by Bo Pang and Lillian Lee, which is available online for free.
I want to do a riddle AI chatbot for my AI class.
So i figgured the input to the chatbot would be :
Something like :
"It is blue, and it is up, but it is not the ceiling"
Translation :
<Object X>
<blue>
<up>
<!ceiling>
</Object X>
(Answer : sky?)
So Input is a set of characteristics (existing \ not existing in the object), output is a matched, most likely object.
The domain will be limited to a number of objects, i could input all attributes myself, but i was thinking :
How could I programatically build a database of characteristics for a word?
Is there such a database available? How could i tag a word, how could i programatically find all it's attributes? I was thinking on crawling wikipedia, or some forum, but i can't see it build any reliable word tag database.
Any ideas on how i could achieve such a thing? Any ideas on some literature on the subject?
Thank you
This sounds like a basic classification problem. You're essentially asking; given N features (color=blue, location=up, etc), which of M classifications is the most likely? There are many algorithms for accomplishing this (Naive Bayes, Maximum Entropy, Support Vector Machine), but you'll have to investigate which is the most accurate and easiest to implement. The biggest challenge is typically acquiring accurate training data, but if you're willing to restrict it to a list of manually entered examples, then that should simplify your implementation.
Your example suggests that whatever algorithm you choose will have to support sparse data. In other words, if you've trained the system on 300 features, it won't require you to enter all 300 features in order to get an answer. It'll also make your training and testing files smaller, because you'll be omit features that are irrelevant for certain objects. e.g.
sky | color:blue,location:up
tree | has_bark:true,has_leaves:true,is_an_organism=true
cat | has_fur:true,eats_mice:true,is_an_animal=true,is_an_organism=true
It might not be terribly helpful, since it's proprietary, but a commercial application that's similar to what you're trying to accomplish is the website 20q.net, albeit the system asks the questions instead of the user. It's interesting in that it's trained "online" based on user input.
Wikipedia certainly has a lot of data, but you'll probably find extracting that data for your program will be very difficult. Cyc's data is more normalized, but its API has a huge learning curve. Another option is the semantic dictionary project Wordnet. It has reasonably intuitive APIs for nearly every programming language, as well as an extensive hypernym/hyponym model for thousands of words (e.g. cat is a type of feline/mammal/animal/organism/thing).
The Cyc project has very similar aims: I believe it contains both inference engines to perform the AI, and databases of facts about commonsense knowledge (like the colour of the sky).