As for college project I am required to build a software that, given some comments concerning a virtual construction site, detects its actual state (just started, in construction, terminated).
For example, given the comments:
"Happy to hear we can walk through the English Channel bridge"
"Yesterday I went to the newly built bridge to have a trip to France with my friends"
"They just finished the site and there are already cracks in the 5th miles. What a letdown!"
The system should detect that the "English Channel bridge" construction site has ended.
At the moment I'm trying to choose what word classification algorithm to use for this project. I searched online looking for the best classification algorithm to use. I've read about SVC but, since I'm not really an expert in this field, I am unsure about the compliance/goodness of SVC with my scenario.
What I'm trying to obtain is not the solution to my problem, but a list of available algorithms with their pros and cons.
You are formulating your problem incorrectly, making it difficult for people to give you a list of pros and cons.
The problem you are describing is not really a word classification problem since you are not classifying words. What you are trying to do is:
Named Entity Recognition for construction projects
Classify each construction Named Entity into 3 different types based on the mention context.
The algorithm is not the real issue. Most classification algorithms (linear regression, decision trees, SVM, etc...) will work.
The problem you actually have (but don't realize based on your question) is that you have no training data for either finding construction project named entities or classifying those entities once you have them into your 3 categories.
My suggestion would be that you use one of the freely available NER toolkits/libraries out there, add in dictionary features related to construction projects (words like bridge, tower, etc...) and see how well you can do at the first part of your task.
More important considerations are:
How much time/money do you have to get annotated data?
What sort of performance do you need?
What language/libraries are you willing toconsider (the least important question IMHO)
I'm sorry, I realize this is probably not the answer you want to hear but I suspect it is the answer you need. ;)
Related
I'm writing up a Smart Home software for my bachelor's degree, that will only simulate the actual house, but I'm stuck at the NLP part of the project. The idea is to have the client listen to voice inputs (already done), transform it into text (done) and send it to the server, which does all the heavy lifting / decision making.
So all my inputs will be fairly short (like "please turn on the porch light"). Based on this, I want to take the decision on which object to act, and how to act. So I came up with a few things to do, in order to write up something somewhat efficient.
Get rid of unnecessary words (in the previous example "please" and "the" are words that don't change the meaning of what needs to be done; but if I say "turn off my lights", "my" does have a fairly important meaning).
Deal with synonyms ("turn on lights" should do the same as "enable lights" -- I know it's a stupid example). I'm guessing the only option is to have some kind of a dictionary (XML maybe), and just have a list of possible words for one particular object in the house.
Detecting the verb and subject. "turn on" is the verb, and "lights" is the subject. I need a good way to detect this.
General implementation. How are these things usually developed in terms of algorithms? I only managed to find one article about NLP in Smart Homes, which was very vague (and had bad English). Any links welcome.
I hope the question is unique enough (I've seen NLP questions on SO, none really helped), that it won't get closed.
If you don't have a lot of time to spend with the NLP problem, you may use the Wit API (http://wit.ai) which maps natural language sentences to JSON:
It's based on machine learning, so you need to provide examples of sentences + JSON output to configure it to your needs. It should be much more robust than grammar-based approaches, especially because the voice-to-speech engine might make mistakes that will break your grammar (but the machine learning module can still get the meaning of the sentence).
I am no way a pioneer in NLP(I love it though) but let me try my hand on this one. For your project I would suggest you to go through Stanford Parser
From your problem definition I guess you don't need anything other then verbs and nouns. SP generates POS(Part of speech tags) That you can use to prune the words that you don't require.
For this I can't think of any better option then what you have in mind right now.
For this again you can use grammatical dependency structure from SP and I am pretty much sure that it is good enough to tackle this problem.
This is where your research part lies. I guess you can find enough patterns using GD and POS tags to come up with an algorithm for your problem. I hardly doubt that any algorithm would be efficient enough to handle every set of input sentence(Structured+unstructured) but something that is more that 85% accurate should be good enough for you.
First, I would construct a list of all possible commands (not every possible way to say a command, just the actual function itself: "kitchen light on" and "turn on the light in the kitchen" are the same command) based on the actual functionality the smart house has available. I assume there is a discrete number of these in the order of no more than hundreds. Assign each some sort of identifier code.
Your job then becomes to map an input of:
a sentence of english text
location of speaker
time of day, day of week
any other input data
to an output of a confidence level (0.0 to 1.0) for each command.
The system will then execute the best match command if the confidence is over some tunable threshold (say over 0.70).
From here it becomes a machine learning application. There are a number of different approaches (and furthermore, approaches can be combined together by having them compete based on features of the input).
To start with I would work through the NLP book from Jurafsky/Manning from Stanford. It is a good survey of current NLP algorithms.
From there you will get some ideas about how the mapping can be machine learned. More importantly how natural language can be broken down into a mathematical structure for machine learning.
Once the text is semantically analyzed, the simplest ML algorithm to try first would be of the supervised ones. To generate training data have a normal GUI, speak your command, then press the corresponding command manually. This forms a single supervised training case. Make some large number of these. Set some aside for testing. It is also unskilled work so other people can help. You can then use these as your training set for your ML algorithm.
I've tried making a hierarchy of all forms of knowledge - including physical objects, numbers, procedures etc. How could this be improved? How would a sentence such as "Jack is producing music sitting on a tree" fall into this chart? Jack would go into humans, tree into place , but where would music go?
I dont have any experience in this field but looking at it from purely linguistic and logical way, you can put "producing " totally into work. Or maybe branch out your "objects" into different kinds like "products"->{"tech","healthcare"...} etc and "art objects"->{"music","paintings"...}
But yeah you can argue that this is a little more specific just to fit in the sentence.
I do not have a direct answer to your question, but I think I know where you would get more information on this tops. If I am not mistaken, you are trying to develop an ontology.
In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships among those concepts. It can be used to reason about the entities within that domain and may be used to describe the domain.
The most general ontology is developed at Cycorp, although the whole ontology is not freely available, a (large) subset of the onotlogy is available by Opencyc. They have an instalaltion prepared, which enables you to make queries to the ontology via a web-browser. Maybe you should explore this ontology and get some new ideas where to go next.
As stated in the title, I'm simply looking for algorithms or solutions one might use to take in the twitter firehose (or a portion of it) and
a) identify questions in general
b) for a question, identify questions that could be the same, with some degree of confidence
Thanks!
(A)
I would try to identify questions using machine learning and the Bag of Words model.
Create a labeled set of twits, and label each of them with a binary
flag: question or not question.
Extract the features from the training set. The features are traditionally words, but at least for any time I tried it - using bi-grams significantly improved the results. (3-grams were not helpful for my cases).
Build a classifier from the data. I usually found out SVM gives better performance then other classifiers, but you can use others as well - such as Naive Bayes or KNN (But you will probably need feature selection algorithm for these).
Now you can use your classifier to classify a tweet.1
(B)
This issue is referred in the world of Information-Retrieval as "duplicate detection" or "near-duplicate detection".
You can at least find questions which are very similar to each other using Semantic Interpretation, as described by Markovitch and Gabrilovich in their wonderful article Wikipedia-based Semantic Interpretation for Natural Language Processing. At the very least, it will help you identify if two questions are discussing the same issues (even though not identical).
The idea goes like this:
Use wikipedia to build a vector that represents its semantics, for a term t, the entry vector_t[i] is the tf-idf score of the term i as it co-appeared with the term t. The idea is described in details in the article. Reading the 3-4 first pages are enough to understand it. No need to read it all.2
For each tweet, construct a vector which is a function of the vectors of its terms. Compare between two vectors - and you can identify if two questions are discussing the same issues.
EDIT:
On 2nd thought, the BoW model is not a good fit here, since it ignores the position of terms. However, I believe if you add NLP processing for extracting feature (for examples, for each term, also denote if it is pre-subject or post-subject, and this was determined using NLP procssing), combining with Machine Learning will yield pretty good results.
(1) For evaluation of your classifier, you can use cross-validation, and check the expected accuracy.
(2) I know Evgeny Gabrilovich published the implemented algorithm they created as an open source project, just need to look for it.
Simple item-to-item recommendation systems are well-known and frequently implemented. An example is the Slope One algorithm. This is fine if the user hasn't rated many items yet, but once they have, I want to offer more finely-grained recommendations. Let's take a music recommendation system as an example, since they are quite popular. If a user is viewing a piece by Mozart, a suggestion for another Mozart piece or Beethoven might be given. But if the user has made many ratings on classical music, we might be able to make a correlation between the items and see that the user dislikes vocals or certain instruments. I'm assuming this would be a two-part process, first part is to find correlations between each users' ratings, the second would be to build the recommendation matrix from these extra data. So the question is, are they any open-source implementations or papers that can be used for each of these steps?
Taste may have something useful. It's moved to the Mahout project:
http://taste.sourceforge.net/
In general, the idea is that given a user's past preferences, you want to predict what they'll select next and recommend it. You build a machine-learning model in which the inputs are what a user has picked in the past and the attributes of each pick. The output is the item(s) they'll pick. You create training data by holding back some of their choices, and using their history to predict the data you held back.
Lots of different machine learning models you can use. Decision trees are common.
One answer is that any recommender system ought to have some of the properties you describe. Initially, recommendations aren't so good and are all over the place. As it learns tastes, the recommendations will come from the area the user likes.
But, the collaborative filtering process you describe is fundamentally not trying to solve the problem you are trying to solve. It is based on user ratings, and two songs aren't rated similarly because they are similar songs -- they're rated similarly just because similar people like them.
What you really need is to define your notion of song-song similarity. Is it based on how the song sounds? the composer? Because it sounds like the notion is not based on ratings, actually. That is 80% of the problem you are trying to solve.
I think the question you are really answering is, what items are most similar to a given item? Given your item similarity, that's an easier problem than recommendation.
Mahout can help with all of these things, except song-song similarity based on its audio -- or at least provide a start and framework for your solution.
There are two techniques that I can think of:
Train a feed-forward artificial neural net using Backpropagation or one of it's successors (e.g. Resilient Propagation).
Use version space learning. This starts with the most general and the most specific hypotheses about what the user likes and narrows them down when new examples are integrated. You can use a hierarchy of terms to describe concepts.
Common characteristics of these methods are:
You need a different function for
each user. This pretty much rules
out efficient database queries when
searching for recommendations.
The function can be updated on the fly
when the user votes for an item.
The dimensions along which you classify
the input data (e.g. has vocals, beats
per minute, musical scales,
whatever) are very critical to the
quality of the classification.
Please note that these suggestions come from university courses in knowledge based systems and artificial neural nets, not from practical experience.
I've read the book Programming Collective Intelligence and found it fascinating. I'd recently heard about a challenge amazon had posted to the world to come up with a better recommendation engine for their system.
The winner apparently produced the best algorithm by limiting the amount of information that was being fed to it.
As a first rule of thumb I guess... "More information is not necessarily better when it comes to fuzzy algorithms."
I know's it's subjective, but ultimately it's a measurable thing (clicks in response to recommendations).
Since most of us are dealing with the web these days and search can be considered a form of recommendation... I suspect I'm not the only one who'd appreciate other peoples ideas on this.
In a nutshell, "What is the best way to build a recommendation ?"
You don't want to use "overall popularity" unless you have no information about the user. Instead, you want to align this user with similar users and weight accordingly.
This is exactly what Bayesian Inference does. In English, it means adjusting the overall probability you'll like something (the average rating) with ratings from other people who generally vote your way as well.
Another piece of advice, but this time ad hoc: I find that there are people where if they like something I will almost assuredly not like it. I don't know if this effect is real or imagined, but it might be fun to build in a kind of "negative effect" instead of just clumping people by similarity.
Finally there's a company specializing in exactly this called SenseArray. The owner (Ian Clarke of freenet fame) is very approachable. You can use my name if you call him up.
There is an entire research area in computer science devoted to this subject. I'd suggest reading some articles.
Agree with #Ricardo. This question is too broad, like asking "What's the best way to optimize a system?"
One common feature to nearly all existing recommendation engines is that making the final recommendation boils down to multiplying some number of matrices and vectors. For example multiply a matrix containing proximity weights between users by a vector of item ratings.
(Of course you have to be ready for most of your vectors to be super sparse!)
My answer is surely too late for #Allain but for other users finding this question through search -- send me a PM and ask a more specific question and I will be sure to respond.
(I design recommendation engines professionally.)
#Lao Tzu, I agree with you.
According to me, recommendation engines are made up of:
Context Input fed from context aware systems (logging all your data)
Logical reasoning to filter the most obvious
Expert systems that improve your subjective data over the period of time based on context inputs, and
Probabilistic reasoning to do decision-making close-to-proximity based on weighted sum of previous actions(beliefs, desires, & intentions).
P.S.
I made such recommendation engine.