Take the phrase "A Pedestrian wishes to cross the road".
I learnt english in England and, according to the old rules, the word 'Pedestrian' is a noun. Stanford CoreNLP finds it to be an adjective, regardless of capitalization.
I don't want to contradict the big-brains of Stanford, USA, but that is just wrong. I am new to this semantic stuff but, by finding the word to be an adjective, the sentence lacks a valid noun phrase.
Have I missed the point of CoreNLP, lost the point of the english language, or should I be seeking more effective analysis tools?
I ask as the example sentence is the very first sentence, of my very first processing experiment, and it is most discouraging.
CoreNLP is a statistical analysis tool. It is trained on many texts that have been annotated by pools of human experts. These experts agree on about 90% of the cases. Thus the CoreNLP system cannot beat that percentage and your sentence is part of the 10% wrong parses.
Related
Does Stanford NLP provide a train method for the DocumentPreprocessor to train with own corpora and creating own models for sentence splitting?
I am working with German sentences and I need to create my own German model for sentence splitting tasks. Therefore, I need to train the sentence splitter, DocumentPreprocessor.
Is there a way I can do it?
No. At present, tokenization of all European languages is done by a (hand-written) finite automaton. Machine learning-based tokenization is used for Chinese and Arabic. At present, sentence splitting for all languages is done by rule, exploiting the decisions of the tokenizer. (Of course, that's just how things are now, not how they have to be.)
At present we have no separate German tokenizer/sentence splitter. The current properties file just re-uses the English ones. This is clearly sub-optimal. If someone wanted to produce something for German, that would be great to have. (We may do it at some point, but German development is not currently at the top of the list of priorities.)
I need to perform sentiment analysis on news articles about a specific topic using the Stanford NLP tool.
Such tool only allows sentence based sentiment analysis while I would like to extract a sentiment evaluation of the whole articles with respect to my topic.
For instance, if my topic is Apple, I would like to know the sentiment of a news article with respect to Apple.
Just computing the average of the sentences in my articles won't do. For instance, I might have an article saying something along the lines of "Apple is very good at this, and this and that. While Google products are very bad for these reasons". Such an article would result in a Neutral classification using the average score of sentences, while it is actually a Very positive article about Apple.
On the other hand filtering my sentences to include only the ones containing the word Apple would miss articles along the lines of "Apple's product A is pretty good. However, it lacks the following crucial features: ...". In this case the effect of the second sentence would be lost if I were to use only the sentences containing the word Apple.
Is there a standard way of addressing this kind of problems? Is Stanford NLP the wrong tool to accomplish my goal?
Update: You might want to look into
http://blog.getprismatic.com/deeper-content-analysis-with-aspects/
This is a very active area of research so it would be hard to find an off-the-shelf tool to do this (at least nothing is built in the Stanford CoreNLP). Some pointers: look into aspect-based sentiment analysis. In this case, Apple would be an "aspect" (not really but can be modeled that way). Andrew McCallum's group at UMass, Bing Liu's group at UIC, Cornell's NLP group, among others, have worked on this problem.
If you want a quick fix, I would suggest to extract sentiment from sentences that have reference to Apple and its products; use coref (check out dcoref annotator in Stanford CoreNLP), which will increase the recall of sentences and solve the problem of sentences like "However, it lacks..".
I don't have much background in sentiment analysis or natural language processing at all, but I have been reading a bit about it in my spare time. I would like to conduct and experiment to analyze forum threads/comments such as reddit, digg, blogs, etc. I'm particularity interested in doing something like counting the number of for, against, and neutral comments for threads of heated religious and political debates. Here's what I am thinking.
1) Find a thread that the original poster has defined a touchy political or religious topic.
2) For each comment categorize it as supporting the original poster or otherwise taking a contradicting or neutral stance.
3) Compare various mediums with the numbers of for or against arguments to determine what platforms are good "debate platforms" (i.e. balanced argument counts).
One big problem that I'm anticipating is that heated topics will invoke strong reactions from both supporting and contradicting parties so a simple happy/sad sentiment analysis won't cut it. I'm just sort of interested in this project for my own curiosities, so if anyone knows of similar research or utilities to conduct this experiment I'd be interested to hear more.
Can someone recommend a good sentiment analysis, word dictionary, training set, etc. for this task?
IMHO this is not possible without running into semantics. Consider the sentence:
Unlike many others, I am not against the abolishment of capital punishment.
Your AI may need to recognise idiomatic subfrases like "not against", or other "not ..." snippets. This is not impossible ;-)
An additional problem is, that "not" is more or less a stopword, its rank will probably be in the top-100, causing a low entropy (though it has a high "semantic" value to every sentence where it is unsed). Also note that omitting "the abolishment of", will cause the "polarity" of the sentence to flip as well.
You can try to use the bag of words [or even better: use n-grams as tokens to the bag]
The approach is basically:
Classify a set of examples, let your algorithm extract the relevant
words from the classified examples.
When a new comment is given, extract the relevant words, and use
k-nearest neighbors to decide if the new comment is a
pro/against/neutral.
Also, you might want to have a look on Apache Mahout.
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.