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Is there a way for NLP parsers to identify a list?
For example, "a tiger, a lion and a gorilla" should be identified as a list
(I don't need it to be identified as a list of animals; just a list would be sufficient).
My ultimate aim is to link a common verb/word to all the items in the list.
For example, consider the sentence "He found a pen, a book and a flashlight". Here, "found" verb should be linked to all the 3 items.
Another example, "He was tested negative for cancer, anemia and diabetes". Here, the word "negative" should be linked to the three diseases.
Is this possible with any of the open-source NLP packages like OpenNLP or Stanford CoreNLP? Any other solution?
EDIT:
Like mentioned in one of the answers, my initial idea was to manually parse the list and find the items by looking at the placement of commas, etc.
But then I discovered Stanford NLP's OpenIE model. This seems to be doing a pretty good job. For example, "He has a pen and a book" gives the 2 relations (He;has;a pen) and (He;has;a book).
The problem with the model is that it doesn't work for incomplete sentences like, "has a pen and a book". (From what I understood, this is because OpenIE can only extract triples)
It also fails when negations are involved. Eg, "He has no pens".
Is there a solution to these problems? What are the best solutions available currently for information extraction?
I'm afraid the full answer could fill the better part of a PhD thesis :)
There are no generic tools to do what you need. You will need to write it yourself. If you look at this example, you can see that you can extract the list by starting from the token and or the comma and then traversing the graph around it to build the list. In this particular case you can look at the conj and appos relations that link smaller noun phrases.
You could also look at POS tag patterns like (N*, ,, N*, CC, N*) -- this is a hack but it's probably your best approach if you want fast results and you are willing to miss out on recall.
As for your requirement to include modifiers such as negation -- this is a separate task that should come after you've identified the list.
What you are trying to do is called Information Extraction.
In your case, the task is to extract basic propositions about a set of entities (given as an enumeration) instead of just one entity (which is the usual scenario). For example, you want to extract the following three propositions from the sentence He found a pen, a book and a flashlight.:
find(X, pen)
find(X, book)
find(X, flashlight)
X stands for the entity referred to as He. As Mr. Savkov already pointed out, information extraction is a quite hard problem whose solution lies beyond a Stack Overflow answer.
There are many approaches to information extraction. As suggested by Mr. Savkov, a solution based on POS tags might be a good starting point. I suggest taking a look at this nice tutorial based on NLTK (especially section 2.2. "Tag Patterns") and this paper.
I work in a health care company and I have trouble with the hospitalization report data. I have the data are coming from various sources: Excel Reports, Plain Text File, and in some cases paper. I managed to get all the data into an Excel File. But I am running into a problem where each person spelled and referred to the same hospital.
For Example: New York Presbyterian Hospital, I have seen more than 10 variation.
New York Presbyterian Hospital
NY Presbyterian Hospital
Presbyterian Hospital
Presb Hospital
PresbHosp
New_York_Presb_Hosp
NYPresbHosp
Columbia Presbyterian Medical Center
NYP/Columbia University Medical Center
New York Presbyterian Hospital Columbia University Medical
A more more cases where the hospital name is misspelled
A few of the different system string limit and cut off the string in random places, or maybe they copy and pasted incorrectly.
Different nurses refer to the Hospital in a differently
In my effect I am trying to create a true database that can store all the membership's information, but I am running into a wall because each staff/department are naming the hospital in a different way. (There is a provider ID unique to each hospital), but most of the reports I received only included "name". I have over 2000 members with about 100-150 hospitals, but 3 or 4 times the amount of different names.
I know Levenshtein distance could be in use, but in such extreme case, is there a strategy to build a match? There are too much data to do by hands (time consuming), since this is one of the dozens or reports I am assigned. Any suggestion would be appreciated.
This is a pretty standard and pretty difficult problem. Entire companies exist to solve it for big data.
The usual strategy is to encode what is known about the data domain in a heuristic algorithm to classify the data before putting it in the database.
A standard classification method would be to create a set of pattern strings for each hospital. The examples you gave might go in the pattern set initially.
Then for each incoming string and each pattern, calculate a metric that's the difference between the string and pattern. Levenshtein is a good starting point. The set containing the least distance pattern (in this case Columbia Presbyterian) wins. An excessive least distance means your pattern set is no good. (You get to tweak what "excessive" means.) More than one low distance (you get to define "low," too) means the pattern set has inadvertent overlaps.
Both problems may be handled in various ways, usually involving human intervention either to classify the data or enhance the pattern sets or both.
A second possibility is to use regexes as patterns. Then a match is equivalent to distance zero above, and a non-match is distance infinity. As you might expect, this makes the algorithm less flexible. Yet for some kinds of data - probably not yours though - it's the best choice.
You should look for "specific patterns" which your data is forming. What i have observed is, out of the strings that you've revealed-- "Presb" is the sub-string which is used in all strings (variations of hospital fields that you have been provided with). #M-ohem's comment is a nice approach as well. But for the starters, you can put up a regular expression which checks if any input string has the pattern "Persb" in it. Learn More
I'm looking to do the opposite to what is described here: Tools for text simplification (Java)
Finding meaningful sub-sentences from a sentence
That is, take two simple sentences and combine them as a compound sentence.
Are there any algorithms to do this?
I'm particularly sure that you will not be able to compound sentences like in the example from the linked question (John played golf. John was the CEO of a company. -> John, who was the CEO of a company, played golf), because it requires such language understanding that is too far from now.
So, it seems that the best option is to bluntly replace dot by comma and concatenate simple sentences (if you have to choose sentences to be compounded from text, you can try simple heuristics like approximating semantic similarity by number of common words or tools like those based on WordNet). I guess, in most cases human readers can infer missed conjunction from the context.
Of course, you could develop more sophisticated solutions, but it requires either narrow domain (e.g. all sentences share very similar structure), or tools that can determine relations between sentences, e.g. relationship of cause and effect. I'm not aware of such tools and doubt in their existence, because this level (sentences and phrases) are much more diverse and sparse than the level of words and collocations.
I'm trying to find out if there is a known algorithm that can detect the "key concept" of a sentence.
The use case is as follows:
User enters a sentence as a query (Does chicken taste like turkey?)
Our system identifies the concepts of the sentence (chicken, turkey)
And it runs a search of our corpus content
The area that we're lacking in is identifying what the core "topic" of the sentence is really about. The sentence "Does chicken taste like turkey" has a primary topic of "chicken", because the user is asking about the taste of chicken. While "turkey" is a helper topic of less importance.
So... I'm trying to find out if there is an algorithm that will help me identify the primary topic of a sentence... Let me know if you are aware of any!!!
I actually did a research project on this and won two competitions and am competing in nationals.
There are two steps to the method:
Parse the sentence with a Context-Free Grammar
In the resulting parse trees, find all nouns which are only subordinate to Noun-Phrase-like constituents
For example, "I ate pie" has 2 nouns: "I" and "pie". Looking at the parse tree, "pie" is inside of a Verb Phrase, so it cannot be a subject. "I", however, is only inside of NP-like constituents. being the only subject candidate, it is the subject. Find an early copy of this program on http://www.candlemind.com. Note that the vocabulary is limited to basic singular words, and there are no verb conjugations, so it has "man" but not "men", has "eat" but not "ate." Also, the CFG I used was hand-made an limited. I will be updating this program shortly.
Anyway, there are limitations to this program. My mentor pointed out in its currents state, it cannot recognize sentences with subjects that are "real" NPs (what grammar actually calls NPs). For example, "that the moon is flat is not a debate any longer." The subject is actually "that the moon is flat." However, the program would recognize "moon" as the subject. I will be fixing this shortly.
Anyway, this is good enough for most sentences...
My research paper can be found there too. Go to page 11 of it to read the methods.
Hope this helps.
Most of your basic NLP parsing techniques will be able to extract the basic aspects of the sentence - i.e., that chicken and turkey a NPs and they are linked by and adjective 'like', etc. Getting these to a 'topic' or 'concept' is more difficult
Technique such as Latent Semantic Analysis and its many derivatives transform this information into a vector (some have methods of retaining in some part the hierarchy/relations between parts of speech) and then compares them to existing, usually pre-classified by concept, vectors. See http://en.wikipedia.org/wiki/Latent_semantic_analysis to get started.
Edit Here's an example LSA app you can play around with to see if you might want to pursue it further . http://lsi.research.telcordia.com/lsi/demos.html
For many longer sentences its difficult to say what exactly is a topic and also there may be more than one.
One way to get approximate ans is
1.) First tag the sentence using openNLP, stanford Parser or any one.
2.) Then remove all the stop words from the sentence.
3.) Pick up Nouns( proper, singular and plural).
Other way is
1.) chuck the sentence into phrases by any parser.
2.) Pick up all the noun phrases.
3.) Remove the Noun phrases that doesn't have the Nouns as a child.
4.) Keep only adjectives and Nouns, remove all words from remaining Noun Phrases.
This might give approx. guessing.
"Key concept" is not a well-defined term in linguistics, but this may be a starting point: parse the sentence, find the subject in the parse tree or dependency structure that you get. (This doesn't always work; for example, the subject of "Is it raining?" is "it", while the key concept is likely "rain". Also, what's the key concept in "Are spaghetti and lasagna the same thing?")
This kind of problem (NLP + search) is more properly dealt with by methods such as LSA, but that's quite an advanced topic.
On the most basic level, a question in English is usually in the form of <verb> <subject> ... ? or <pronoun> <verb> <subject> ... ?. This is by no means a good algorithm, especially considering that the subject could span several words, but depending on how sophisticated a solution you need, it might be a useful starting point.
If you need precision, ignore this answer.
If you're willing to shell out money, http://www.connexor.com/ is supposed to be able to do this type of semantic analysis for a wide variety of languages, including English. I have never directly used their product, and so can't comment on how well it works.
There's an article about Parsing Noun Phrases in the MIT Computational Linguistics journal of this month: http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00076
Compound or complex sentences may have more than one key concept of a sentence.
You can use stanfordNLP or MaltParser which can give the dependency structure of a sentence. It also gives the parts of speech tagging including subject, verb , object etc.
I think most of the times the object will be the key concept of the sentence.
You should look at Google's Cloud Natural Language API. It's their NLP service.
https://cloud.google.com/natural-language/
Simple solution is to tag your sentence with part-of-speach tagger (e.g. from NLTK library for Python) then find matches with some predefined part-of-speach patterns in which it's clear where is main subject of the sentence
One option is to look into something like this as a first step:
http://www.abisource.com/projects/link-grammar/
But how you derive the topic from these links is another problem in itself. But as Abiword is trying to detect grammatical problems, you might be able to use it to determine the topic.
By "primary topic" you're referring to what is termed the subject of the sentence.
The subject can be identified by understanding a sentence through natural language processing.
The answer to this question is the same as that for How to determine subject, object and other words? - this is a currently unsolved problem.
I assume a natural language processor would need to be used to parse the text itself, but what suggestions do you have for an algorithm to detect a user's mood based on text that they have written? I doubt it would be very accurate, but I'm still interested nonetheless.
EDIT: I am by no means an expert on linguistics or natural language processing, so I apologize if this question is too general or stupid.
This is the basis of an area of natural language processing called sentiment analysis. Although your question is general, it's certainly not stupid - this sort of research is done by Amazon on the text in product reviews for example.
If you are serious about this, then a simple version could be achieved by -
Acquire a corpus of positive/negative sentiment. If this was a professional project you may take some time and manually annotate a corpus yourself, but if you were in a hurry or just wanted to experiment this at first then I'd suggest looking at the sentiment polarity corpus from Bo Pang and Lillian Lee's research. The issue with using that corpus is it is not tailored to your domain (specifically, the corpus uses movie reviews), but it should still be applicable.
Split your dataset into sentences either Positive or Negative. For the sentiment polarity corpus you could split each review into it's composite sentences and then apply the overall sentiment polarity tag (positive or negative) to all of those sentences. Split this corpus into two parts - 90% should be for training, 10% should be for test. If you're using Weka then it can handle the splitting of the corpus for you.
Apply a machine learning algorithm (such as SVM, Naive Bayes, Maximum Entropy) to the training corpus at a word level. This model is called a bag of words model, which is just representing the sentence as the words that it's composed of. This is the same model which many spam filters run on. For a nice introduction to machine learning algorithms there is an application called Weka that implements a range of these algorithms and gives you a GUI to play with them. You can then test the performance of the machine learned model from the errors made when attempting to classify your test corpus with this model.
Apply this machine learning algorithm to your user posts. For each user post, separate the post into sentences and then classify them using your machine learned model.
So yes, if you are serious about this then it is achievable - even without past experience in computational linguistics. It would be a fair amount of work, but even with word based models good results can be achieved.
If you need more help feel free to contact me - I'm always happy to help others interested in NLP =]
Small Notes -
Merely splitting a segment of text into sentences is a field of NLP - called sentence boundary detection. There are a number of tools, OSS or free, available to do this, but for your task a simple split on whitespaces and punctuation should be fine.
SVMlight is also another machine learner to consider, and in fact their inductive SVM does a similar task to what we're looking at - trying to classify which Reuter articles are about "corporate acquisitions" with 1000 positive and 1000 negative examples.
Turning the sentences into features to classify over may take some work. In this model each word is a feature - this requires tokenizing the sentence, which means separating words and punctuation from each other. Another tip is to lowercase all the separate word tokens so that "I HATE you" and "I hate YOU" both end up being considered the same. With more data you could try and also include whether capitalization helps in classifying whether someone is angry, but I believe words should be sufficient at least for an initial effort.
Edit
I just discovered LingPipe that in fact has a tutorial on sentiment analysis using the Bo Pang and Lillian Lee Sentiment Polarity corpus I was talking about. If you use Java that may be an excellent tool to use, and even if not it goes through all of the steps I discussed above.
No doubt it is possible to judge a user's mood based on the text they type but it would be no trivial thing. Things that I can think of:
Capitals tends to signify agitation, annoyance or frustration and is certainly an emotional response but then again some newbies do that because they don't realize the significance so you couldn't assume that without looking at what else they've written (to make sure its not all in caps);
Capitals are really just one form of emphasis. Others are use of certain aggressive colours (eg red) or use of bold or larger fonts;
Some people make more spelling and grammar mistakes and typos when they're highly emotional;
Scanning for emoticons could give you a very clear picture of what the user is feeling but again something like :) could be interpreted as happy, "I told you so" or even have a sarcastic meaning;
Use of expletives tends to have a clear meaning but again its not clearcut. Colloquial speech by many people will routinely contain certain four letter words. For some other people, they might not even say "hell", saying "heck" instead so any expletive (even "sucks") is significant;
Groups of punctuation marks (like ##$#$#) tend to be replaced for expletives in a context when expletives aren't necessarily appropriate, so thats less likely to be colloquial;
Exclamation marks can indicate surprise, shock or exasperation.
You might want to look at Advances in written text analysis or even Determining Mood for a Blog by Combining Multiple Sources of Evidence.
Lastly it's worth noting that written text is usually perceived to be more negative than it actually is. This is a common problem with email communication in companies, just as one example.
I can't believe I'm taking this seriously... assuming a one-dimensional mood space:
If the text contains a curse word,
-10 mood.
I think exclamations would tend to be negative, so -2 mood.
When I get frustrated, I type in
Very. Short. Sentences. -5 mood.
The more I think about this, the more it's clear that a lot of these signifiers indicate extreme mood in general, but it's not always clear what kind of mood.
If you support fonts, bold red text is probably an angry user. Green regular sized texts with butterfly clip art a happy one.
My memory isn't good on this subject, but I believe I saw some research about the grammar structure of the text and the overall tone. That could be also as simple as shorter words and emotion expression words (well, expletives are pretty obvious).
Edit: I noted that the first person to answer had substantially similar post. There could be indeed some serious idea about shorter sentences.
Analysis of mood and behavior is very serious science. Despite the other answers mocking the question law enforcement agencies have been investigating categorization of mood for years. Uses in computers I have heard of generally had more context (timing information, voice pattern, speed in changing channels). I think that you could--with some success--determine if a user is in a particular mood by training a Neural Network with samples from two known groups: angry and not angry. Good luck with your efforts.
I think, my algorythm is rather straightforward, yet, why not calculating smilics through the text :) vs :(
Obviously, the text ":) :) :) :)" resolves to a happy user, while ":( :( :(" will surely resolve to a sad one. Enjoy!
I agree with ojblass that this is a serious question.
Mood categorization is currently a hot topic in the speech recognition area. If you think about it, an interactive voice response (IVR) application needs to handle angry customers far differently than calm ones: angry people should be routed quickly to human operators with the right experience and training. Vocal tone is a pretty reliable indicator of emotion, practical enough so that companies are eager to get this to work. Google "speech emotion recognition", or read this article to find out more.
The situation should be no different in web-based GUIs. Referring back to cletus's comments, the analogies between text and speech emotion detection are interesting. If a person types CAPITALS they are said to be 'shouting', just as if his voice rose in volume and pitch using a voice interface. Detecting typed profanities is analogous to "keyword spotting" of profanity in speech systems. If a person is upset, they'll make more errors using either a GUI or a voice user interface (VUI) and can be routed to a human.
There's a "multimodal" emotion detection research area here. Imagine a web interface that you can also speak to (along the lines of the IBM/Motorola/Opera XHTML + Voice Profile prototype implementation). Emotion detection could be based on a combination of cues from the speech and visual input modality.
Yes.
Whether or not you can do it is another story. The problem seems at first to be AI complete.
Now then, if you had keystroke timings you should be able to figure it out.
Fuzzy logic will do I guess.
Any way it will be quite easy to start with several rules of determining the user's mood and then extend and combine the "engine" with more accurate and sophisticated ones.