Is there a way to to find all the sub-sentences of a sentence that still are meaningful and contain at least one subject, verb, and a predicate/object?
For example, if we have a sentence like "I am going to do a seminar on NLP at SXSW in Austin next month". We can extract the following meaningful sub-sentences from this sentence: "I am going to do a seminar", "I am going to do a seminar on NLP", "I am going to do a seminar on NLP at SXSW", "I am going to do a seminar at SXSW", "I am going to do a seminar in Austin", "I am going to do a seminar on NLP next month", etc.
Please note that there is no deduced sentences here (e.g. "There will be a NLP seminar at SXSW next month". Although this is true, we don't need this as part of this problem.) . All generated sentences are strictly part of the given sentence.
How can we approach solving this problem? I was thinking of creating annotated training data that has a set of legal sub-sentences for each sentence in the training data set. And then write some supervised learning algorithm(s) to generate a model.
I am quite new to NLP and Machine Learning, so it would be great if you guys could suggest some ways to solve this problem.
You can use dependency parser provided by Stanford CoreNLP.
Collapsed output of your sentence will look like below.
nsubj(going-3, I-1)
xsubj(do-5, I-1)
aux(going-3, am-2)
root(ROOT-0, going-3)
aux(do-5, to-4)
xcomp(going-3, do-5)
det(seminar-7, a-6)
dobj(do-5, seminar-7)
prep_on(seminar-7, NLP-9)
prep_at(do-5, -11)
prep_in(do-5, Austin-13)
amod(month-15, next-14)
tmod(do-5, month-15)
The last 5 of your sentence output are optional. You can remove one or more parts that are not essential to your sentence.
Most of this optional parts are belong to prepositional and modifier e.g : prep_in, prep_do, advmod, tmod, etc. See Stanford Dependency Manual.
For example, if you remove all modifier from the output, you will get
I am going to do a seminar on NLP at SXSW in Austin.
There's a paper titled "Using Discourse Commitments to Recognize Textual Entailment" by Hickl et al that discusses the extraction of discourse commitments (sub-sentences). The paper includes a description of their algorithm which in some level operates on rules. They used it for RTE, and there may be some minimal levels of deduction in the output. Text simplification maybe a related area to look at.
The following paper http://www.mpi-inf.mpg.de/~rgemulla/publications/delcorro13clausie.pdf processes the dependencies from the Stanford parser and contructs simple clauses (text-simplification).
See the online demo - https://d5gate.ag5.mpi-sb.mpg.de/ClausIEGate/ClausIEGate
One approach would be with a parser such as a PCFG. Trying to just train a model to detect 'subsentences' is likely to suffer from data sparsity. Also, I am doubtful that you could write down a really clean and unambiguous definition of a subsentence, and if you can't define it, you can't get annotators to annotate for it.
Related
Is there any way to find those sentences that are describing objects?
For example sentences like "This is a good product" or "You are very beautiful"
I guess I can create an algorithm by using TokenSequencePattern and filter with POS some patterns like PRONUN + VERB + ADJECTIVE but don't think would be something reliable.
I am asking you if there is something out of the box, what I am trying to do is to identify review comments on a webpage.
Instead of POS tagging, you would achieve better results by dependency parsing. By using that instead of POS tagging & patterns as you mentioned, you will have richer and accurate information about the sentence structure.
Example:
https://demos.explosion.ai/displacy/?text=The%20product%20was%20really%20very%20good.&model=en_core_web_sm&cpu=0&cph=0
Stanford NLP does support depedency parsing.
Apart from that you can also use the brilliant SpaCy.
If a single query from the user contains multiple questions belonging to different categories, how can they be identified, split and parsed?
Eg -
User - what is the weather now and tell me my next meeting
Parser - {:weather => "what is the weather", :schedule => "tell me my next meeting"}
Parser identifies the parts of sentences where the question belongs to two different categories
User - show me hotels in san francisco for tomorrow that are less than $300 but not less than $200 are pet friendly have a gym and a pool with 3 or 4 stars staying for 2 nights and dont include anything that doesnt have wifi
Parser - {:hotels => ["show me hotels in san francisco",
"for tomorrow", "less than $300 but not less than $200",
"pet friendly have a gym and a pool",
"with 3 or 4 stars", "staying for 2 nights", "with wifi"]}
Parser identifies the question belonging to only one category but has additional steps for fine tuning the answer and created an array ordered according to the steps to take
From what I can understand this requires a sentence segmenter, multi-label classifier and co-reference resolution
But the sentence segementer I have come across depend heavily on grammar, punctuations.
Multi-label classifiers, like a good trained naive bayes classifier works in most cases but since they are multi-label, most times output multiple categories for sentences which clearly belong to one class. Depending solely on the array outputs to check the labels present would fail.
If used a multi-class classifier, that is also good to check the array output of probable categories but obviously they dont tell the different parts of the sentence much accurately, much less in what fashion to proceed with the next step.
As a first step, how can I tune sentence segmenter to correctly split the sentence without any strict grammar rules. Good accuracy of this would help a lot in classification.
As a first step, how can I tune sentence segmenter to correctly split the sentence without any strict grammar rules.
Instead of doing this I'd suggest you use the parse-tree directly (either dependency parser, or constituency parse).
Here I'm showing the output of the dependency parse and you can see that the two segments are separated via a "CONJ" arrow:
(from here: http://deagol.cs.illinois.edu:8080/)
Another solution I'd give try is ClausIE:
https://gate.d5.mpi-inf.mpg.de/ClausIEGate/ClausIEGate?inputtext=what+is+the+weather+now+and+tell+me+my+next+meeting++&processCcAllVerbs=true&processCcNonVerbs=true&type=true&go=Extract
If you want something for segmentation that doesn't depend on grammar heavily, then chunking comes to mind. In the NLTK book there is a fragment on that. The approach authors take here depends only on part of speech tags.
BTW Jurafsky and Martin's 3rd ed of Speech and Language processing contains information on chunking in the parsing chapter, and it also contains a chapters on information retrieval nad chatbots.
Is there a way to generate a one-sentence summarization of Q&A pairs?
For example, provided:
Q: What is the color of the car?
A: Red
I want to generate a summary as
The color of the car is red
Or, given
Q: Are you a man?
A: Yes
to
Yes, I am a man.
which accounts for both question and answer.
What would be some of the most reasonable ways to do this?
I had to once work on solving the opposite problem, i.e. generating questions out of sentences from Wikipedia articles.
I used the Stanford Parser to generate parse trees out of all possible sentences in my training dataset.
e.g.
Go to http://nlp.stanford.edu:8080/parser/index.jsp
Enter "The color of the car is red." and click "Parse".
Then look at the Parse section of the response. The first layer of that sentence is NP VP (noun phrase followed by a verb phrase).
The second layer is NP PP VBZ ADJP.
I basically collected these patterns across 1000s of sentences, sorted them how common each patter was, and then used figured out how to best modify this parse tree to convert into each sentence in a different Wh-question (What, Who, When, Where, Why, etc)
You could you easily do something very similar. Study the parse trees of all of your training data, and figure out what patterns you could extract to get your work done. In many cases, just replacing the Wh word from the question with the answer would give you a valid albeit somewhat awkwardly phrases sentence.
e.g. "Red is the color of the car."
In the case of questions like "Are you a man?" (i.e. primary verb is something like 'are', 'can', 'should', etc), swapping the first 2 words usually does the trick - "You are a man?"
I don't know any NLP task that explicitly handles your requirement.
Broadly, there are two kinds of questions. Questions that expect a passage as the answer such as definition or explain sort: What is Ebola Fever. The second type are fill in the blank which are referred to as Factoid Questions in the literature such as What is the height of Mt. Everest?. It is not clear what kind of question you would like to summarize. I am assuming you are interested in factoid questions as your examples refer to only them.
A very similar problem arises in the task of Question Answering. One of the first stages of this task is to generate query. In the paper: An Exploration of the Principles Underlying
Redundancy-Based Factoid Question
Answering; Jimmy Lin 2007, the author claims that better performance can be achieved by reformulating the query (see section 4.1) to the form more likely to appear in free text. Let me copy some of the examples discussed in the paper.
1. What year did Alaska became a state?
2. Alaska became a state ?x
1. Who was the first person to run the miles in less than four minutes?
2. The first person to run the miles in less than four minutes was ?x
In the above examples, the query in 1 is reformulated to 2. As you might have already observed, ?x is the blank that should be filled by the answer. This reformulation is carried out through a dozen hand-written rules and are built into the software tool discussed in the paper: ARANEA. All you have to do is to find the tool and use it, the paper is a good ten years old, I cannot promise you anything though :)
Hope this helps.
I am experimenting with machine learning in general, and Bayesian analysis in particular, by writing a tool to help me identify my collection of e-books. The input data consist of a set of e-book files, whose names and in some cases contents contain hints as to the book they correspond to.
Some are obvious to the human reader, like:
Artificial Intelligence - A Modern Approach 3rd.pdf
Microsoft Press - SharePoint Foundation 2010 Inside Out.pdf
The Complete Guide to PC Repair 5th Ed [2011].pdf
Hamlet.txt
Others are not so obvious:
Vsphere5.prc (Actually 'Mastering VSphere 5' by Scott Lowe)
as.ar.pdf (Actually 'Atlas Shrugged' by Ayn Rand)
Rather than try to code various parsers for different formats of file names, I thought I would build a few dozen simple rules, each with a score.
For example, one rule would look in the first few pages of the file for something resembling an ISBN number, and if found would propose a hypothesis that the file corresponds to the book identified by that ISBN number.
Another rule would look to see if the file name is in 'Author - Title' format and, if so, would propose a hypothesis that the author is 'Author' and the title is 'Title'. Similar rules for other formats.
I thought I could also get a list of book titles and authors from Amazon or an ISBN database, and search the file name and first few pages of the file for any of these; any matches found would result in a hypothesis being suggested by that rule.
In the end I would have a set of tuples like this:
[rulename,hypothesis]
I expect that some rules, such as the ISBN match, will have a high probability of being correct, when they are available. Other rules, like matches based on known book titles and authors, would be more common but not as accurate.
My questions are:
Is this a good approach for solving this problem?
If so, is Bayesian analysis a good candidate for combining all of these rules' hypotheses into compound score to help determine which hypothesis is the strongest, or most likely?
Is there a better way to solve this problem, or some research paper or book which you can suggest I turn to for more information?
It depends on the size of your collection and the time you want to spend training the classifier. It will be difficult to get good generalization that will save you time. For any type of classifier you will have to create a large training set, and also find a lot of rules before you get good accuracy. It will probably be more efficient (less false positives) to create the rules and use them only to suggest title alternatives for you to choose from, and not to implement the classifier. But, if the purpose is learning, then go ahead.
I've studied some simple semantic network implementations and basic techniques for parsing natural language. However, I haven't seen many projects that try and bridge the gap between the two.
For example, consider the dialog:
"the man has a hat"
"he has a coat"
"what does he have?" => "a hat and coat"
A simple semantic network, based on the grammar tree parsing of the above sentences, might look like:
the_man = Entity('the man')
has = Entity('has')
a_hat = Entity('a hat')
a_coat = Entity('a coat')
Relation(the_man, has, a_hat)
Relation(the_man, has, a_coat)
print the_man.relations(has) => ['a hat', 'a coat']
However, this implementation assumes the prior knowledge that the text segments "the man" and "he" refer to the same network entity.
How would you design a system that "learns" these relationships between segments of a semantic network? I'm used to thinking about ML/NL problems based on creating a simple training set of attribute/value pairs, and feeding it to a classification or regression algorithm, but I'm having trouble formulating this problem that way.
Ultimately, it seems I would need to overlay probabilities on top of the semantic network, but that would drastically complicate an implementation. Is there any prior art along these lines? I've looked at a few libaries, like NLTK and OpenNLP, and while they have decent tools to handle symbolic logic and parse natural language, neither seems to have any kind of proabablilstic framework for converting one to the other.
There is quite a lot of history behind this kind of task. Your best start is probably by looking at Question Answering.
The general advice I always give is that if you have some highly restricted domain where you know about all the things that might be mentioned and all the ways they interact then you can probably be quite successful. If this is more of an 'open-world' problem then it will be extremely difficult to come up with something that works acceptably.
The task of extracting relationship from natural language is called 'relationship extraction' (funnily enough) and sometimes fact extraction. This is a pretty large field of research, this guy did a PhD thesis on it, as have many others. There are a large number of challenges here, as you've noticed, like entity detection, anaphora resolution, etc. This means that there will probably be a lot of 'noise' in the entities and relationships you extract.
As for representing facts that have been extracted in a knowledge base, most people tend not to use a probabilistic framework. At the simplest level, entities and relationships are stored as triples in a flat table. Another approach is to use an ontology to add structure and allow reasoning over the facts. This makes the knowledge base vastly more useful, but adds a lot of scalability issues. As for adding probabilities, I know of the Prowl project that is aimed at creating a probabilistic ontology, but it doesn't look very mature to me.
There is some research into probabilistic relational modelling, mostly into Markov Logic Networks at the University of Washington and Probabilstic Relational Models at Stanford and other places. I'm a little out of touch with the field, but this is is a difficult problem and it's all early-stage research as far as I know. There are a lot of issues, mostly around efficient and scalable inference.
All in all, it's a good idea and a very sensible thing to want to do. However, it's also very difficult to achieve. If you want to look at a slick example of the state of the art, (i.e. what is possible with a bunch of people and money) maybe check out PowerSet.
Interesting question, I've been doing some work on a strongly-typed NLP engine in C#: http://blog.abodit.com/2010/02/a-strongly-typed-natural-language-engine-c-nlp/ and have recently begun to connect it to an ontology store.
To me it looks like the issue here is really: How do you parse the natural language input to figure out that 'He' is the same thing as "the man"? By the time it's in the Semantic Network it's too late: you've lost the fact that statement 2 followed statement 1 and the ambiguity in statement 2 can be resolved using statement 1. Adding a third relation after the fact to say that "He" and "the man" are the same is another option but you still need to understand the sequence of those assertions.
Most NLP parsers seem to focus on parsing single sentences or large blocks of text but less frequently on handling conversations. In my own NLP engine there's a conversation history which allows one sentence to be understood in the context of all the sentences that came before it (and also the parsed, strongly-typed objects that they referred to). So the way I would handle this is to realize that "He" is ambiguous in the current sentence and then look back to try to figure out who the last male person was that was mentioned.
In the case of my home for example, it might tell you that you missed a call from a number that's not in its database. You can type "It was John Smith" and it can figure out that "It" means the call that was just mentioned to you. But if you typed "Tag it as Party Music" right after the call it would still resolve to the song that's currently playing because the house is looking back for something that is ITaggable.
I'm not exactly sure if this is what you want, but take a look at natural language generation wikipedia, the "reverse" of parsing, constructing derivations that conform to the given semantical constraints.