I am a newbie of MWS API. During my learning, I found that GetLowestPricedOffersForASIN and GetLowestOfferListingsForASIN output something similar. However, some of the offers which are in GetLowestPricedOffersForASIN did not appear in GetLowestOfferListingsForASIN. Therefore, I want to know what is the differences between "lowest offer" and "lowest priced offers". Thanks.
GetLowestPricedOffersForASIN returns an Offer element with much more information, particularly the IsFBA, IsFeatured, IsBuyBoxWinner, etc. This one is meant for one ASIN at a time, while GetLowestOfferListingsForASIN allows you to pass in 20 ASIN's at a time and just basically gives you pricing info. It really depends on your use case. Check out the differences in the Response elements and choose.
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Let's suppose a simple ontology with the following classes/subclasses:
Subject
Subject_under_60
Subject_over_60
I have a set of incoming data which is transformed into a set of Subject individuals having a data property "has_age".
I'm looking for some nice workaround how to define the Subject's subclasses in the way that reasoners would be able to classify the subject individuals. So far, I was thinking about two approaches:
Data pre-processing where I would add a flag if the subject is or is not over 60 (e.g. subject01 dataProperty:isOver60 True).
Data post-processing, i.e. solve it later in the process in SPARQL queries.
But what I would really appreciate is something like
Subject_over_60 isEquivalent Subject AND (has_age valueOver "60"^^xsd:int).
My goal is to simplify the pre-processing as much as possible (in best case leave there just a date of birt) and leave most of the classification work up to reasoners. Since specific value comparison is limited to "exact value" only, I'm aware that it won't be so easy. I would appreciate any idea or your best practice to solve a similar problem.
I am automating a process which asks questions (via SMS but shouldn't matter) to real people. The questions have yes/no answers, but the person might respond in a number of ways such as: sure, not at this time, yeah, never or in any other way that they might. I would like to attempt to parse this text and determine if it was a yes or no answer (of course it might not always be right).
I figured the ideas and concepts to do this might already exist as it seems like a common task for an AI, but don't know what it might be called so I can't find information on how I might implement it. So my questions is, have algorithms been developed to do this kind of parsing and if so where can I find more information on how to implement them?
This can be viewed as a binary (yes or no) classification task. You could write a rule-based model to classify or a statistics-based model.
A rule-based model would be like if answer in ["never", "not at this time", "nope"] then answer is "no". When spam filters first came out they contained a lot of rules like these.
A statistics-based model would probably be more suitable here, as writing your own rules gets tiresome and does not handle new cases as well.
For this you need to label a training dataset. After a little preprocessing (like lowercasing all the words, removing punctuation and maybe even a little stemming) you could get a dataset like
0 | never in a million years
0 | never
1 | yes sir
1 | yep
1 | yes yes yeah
0 | no way
Now you can run classification algorithms like Naive Bayes or Logistic Regression over this set (after you vectorize the words in either binary, which means is the word present or not, word count, which means the term frequency, or a tfidf float, which prevent bias to longer answers and common words) and learn which words more often belong to which class.
In the above example yes would be strongly correlated to a positive answer (1) and never would be strongly related to a negative answer (0). You could work with n-grams so a not no would be treated as a single token in favor of the positive class. This is called the bag-of-words approach.
To combat spelling errors you can add a spellchecker like Aspell to the pre-processing step. You could use a charvectorizer too, so a word like nno would be interpreted as nn and no and you catch errors like hellyes and you could trust your users to repeat spelling errors. If 5 users make the spelling error neve for the word never then the token neve will automatically start to count for the negative class (if labeled as such).
You could write these algorithms yourself (Naive Bayes is doable, Paul Graham has wrote a few accessible essays on how to classify spam with Bayes Theorem and nearly every ML library has a tutorial on how to do this) or make use of libraries or programs like Scikit-Learn (MultinomialNB, SGDclassifier, LinearSVC etc.) or Vowpal Wabbit (logistic regression, quantile loss etc.).
Im thinking on top of my head, if you get a response which you dont know if its yes / no, you can keep the answers in a DB like unknown_answers and 2 more tables as affirmative_answers / negative_answers, then in a little backend system, everytime you get a new unknown_answer you qualify them as yes or no, and there the system "learns" about it and with time, you will have a very big and good database of affirmative / negative answers.
I'm not sure what exactly I'm trying to ask. I want to be able to make some code that can easily take an initial and final state and some rules, and determine paths/choices to get there.
So think, for example, in a game like Starcraft. To build a factory I need to have a barracks and a command center already built. So if I have nothing and I want a factory I might say ->Command Center->Barracks->Factory. Each thing takes time and resources, and that should be noted and considered in the path. If I want my factory at 5 minutes there are less options then if I want it at 10.
Also, the engine should be able to calculate available resources and utilize them effectively. Those three buildings might cost 600 total minerals but the engine should plan the Command Center when it would have 200 (or w/e it costs).
This would ultimately have requirements similar to 10 marines # 5 minutes, infantry weapons upgrade at 6:30, 30 marines at 10 minutes, Factory # 11, etc...
So, how do I go about doing something like this? My first thought was to use some procedural language and make all the decisions from the ground up. I could simulate the system and branching and making different choices. Ultimately, some choices are going quickly make it impossible to reach goals later (If I build 20 Supply Depots I'm prob not going to make that factory on time.)
So then I thought weren't functional languages designed for this? I tried to write some prolog but I've been having trouble with stuff like time and distance calculations. And I'm not sure the best way to return the "plan".
I was thinking I could write:
depends_on(factory, barracks)
depends_on(barracks, command_center)
builds_from(marine, barracks)
build_time(command_center, 60)
build_time(barracks, 45)
build_time(factory, 30)
minerals(command_center, 400)
...
build(X) :-
depends_on(X, Y),
build_time(X, T),
minerals(X, M),
...
Here's where I get confused. I'm not sure how to construct this function and a query to get anything even close to what I want. I would have to somehow account for rate at which minerals are gathered during the time spent building and other possible paths with extra gold. If I only want 1 marine in 10 minutes I would want the engine to generate lots of plans because there are lots of ways to end with 1 marine at 10 minutes (maybe cut it off after so many, not sure how you do that in prolog).
I'm looking for advice on how to continue down this path or advice about other options. I haven't been able to find anything more useful than towers of hanoi and ancestry examples for AI so even some good articles explaining how to use prolog to DO REAL THINGS would be amazing. And if I somehow can get these rules set up in a useful way how to I get the "plans" prolog came up with (ways to solve the query) other than writing to stdout like all the towers of hanoi examples do? Or is that the preferred way?
My other question is, my main code is in ruby (and potentially other languages) and the options to communicate with prolog are calling my prolog program from within ruby, accessing a virtual file system from within prolog, or some kind of database structure (unlikely). I'm using SWI-Prolog atm, would I be better off doing this procedurally in Ruby or would constructing this in a functional language like prolog or haskall be worth the extra effort integrating?
I'm sorry if this is unclear, I appreciate any attempt to help, and I'll re-word things that are unclear.
Your question is typical and very common for users of procedural languages who first try Prolog. It is very easy to solve: You need to think in terms of relations between successive states of your world. A state of your world consists for example of the time elapsed, the minerals available, the things you already built etc. Such a state can be easily represented with a Prolog term, and could look for example like time_minerals_buildings(10, 10000, [barracks,factory])). Given such a state, you need to describe what the state's possible successor states look like. For example:
state_successor(State0, State) :-
State0 = time_minerals_buildings(Time0, Minerals0, Buildings0),
Time is Time0 + 1,
can_build_new_building(Buildings0, Building),
building_minerals(Building, MB),
Minerals is Minerals0 - MB,
Minerals >= 0,
State = time_minerals_buildings(Time, Minerals, Building).
I am using the explicit naming convention (State0 -> State) to make clear that we are talking about successive states. You can of course also pull the unifications into the clause head. The example code is purely hypothetical and could look rather different in your final application. In this case, I am describing that the new state's elapsed time is the old state's time + 1, that the new amount of minerals decreases by the amount required to build Building, and that I have a predicate can_build_new_building(Bs, B), which is true when a new building B can be built assuming that the buildings given in Bs are already built. I assume it is a non-deterministic predicate in general, and will yield all possible answers (= new buildings that can be built) on backtracking, and I leave it as an exercise for you to define such a predicate.
Given such a predicate state_successor/2, which relates a state of the world to its direct possible successors, you can easily define a path of states that lead to a desired final state. In its simplest form, it will look similar to the following DCG that describes a list of successive states:
states(State0) -->
( { final_state(State0) } -> []
; [State0],
{ state_successor(State0, State1) },
states(State1)
).
You can then use for example iterative deepening to search for solutions:
?- initial_state(S0), length(Path, _), phrase(states(S0), Path).
Also, you can keep track of states you already considered and avoid re-exploring them etc.
The reason you get confused with the example code you posted is essentially that build/1 does not have enough arguments to describe what you want. You need at least two arguments: One is the current state of the world, and the other is a possible successor to this given state. Given such a relation, everything else you need can be described easily. I hope this answers your question.
Caveat: my Prolog is rusty and shallow, so this may be off base
Perhaps a 'difference engine' approach would be appropriate:
given a goal like 'build factory',
backwards-chaining relations would check for has-barracks and tell you first to build-barracks,
which would check for has-command-center and tell you to build-command-center,
and so on,
accumulating a plan (and costs) along the way
If this is practical, it may be more flexible than a state-based approach... or it may be the same thing wearing a different t-shirt!
I've trained a system on SVM,that is given a question,whether the webpage is a good one for answering this question.
The feature I selected are "Term frequency in webpage","Whether term matches with the webpage title", "number of images in the webpage", "length of the webpage","is it a wikipedia page?","the position of this webpage in the list returned by the search engine".
Currently,my system will maintain a precision around 0.4 and recall at 1.It has a large portion of false positive error(that many bad links were classified as good link by my classifier).
Since the accuracy could be improved a bit,I would like to ask for some help here on considering refine the features that I selected for training/testing,could remove some or adding more in there.
Thanks in advance.
Hmm...
How large is your training set? i.e., how many training documents are you using?
What is your test set composed of?
Since you're getting too many FPs, I would try training with more (and varied) "bad" webpages
Can you give more details about your different features, like "tf in webpage," etc.?
I'm trying to implement application that can determine meaning of sentence, by dividing it to smaller pieces. So I need to know what words are subject, object etc. so that my program can know how to handle this sentence.
This is an open research problem. You can get an overview on Wikipedia, http://en.wikipedia.org/wiki/Natural_language_processing. Consider phrases like "Time flies like an arrow, fruit flies like a banana" - unambiguously classifying words is not easy.
You should look at the Natural Language Toolkit, which is for exactly this sort of thing.
See this section of the manual: Categorizing and Tagging Words - here's an extract:
>>> text = nltk.word_tokenize("And now for something completely different")
>>> nltk.pos_tag(text)
[('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'),
('completely', 'RB'), ('different', 'JJ')]
"Here we see that and is CC, a coordinating conjunction; now and completely are RB, or adverbs; for is IN, a preposition; something is NN, a noun; and different is JJ, an adjective."
I guess there is not "simple" way to do this. You have to build a linguistic analyzer (which is quite possible), however, a language as a lot of exceptional cases. And that is what makes implementing a linguistic analyzer that hard.
The specific problem you mention, the identification of the subject and objects of a clause, is accomplished by syntactic parsing. You can get a good idea of how parsing works by using this demo of parsing software developed by Stanford University.
However, syntactic parsing does not determine the meanining of a sentence, only its structure. Determining meaning (semantics) is a very hard problem in general and there is no technology that can really 'understand' a sentence in the same way that a human would. Although there is no general solution, you may be able to do something in a very restricted subject domain. For example, is the data you want to analyse about a narrow topic with a limited set of 'things' that people talk about?
StompChicken has given the right answer to this question, but I'd like to add that the concepts of subject and object are known as grammatical relations, and that Briscoe and Carroll's RASP is a parser that can go the extra step of deducing a list of relations from the parse.
Here's some example output from their demo page. It's an extract from the output for a sentence that begins "We describe a robust accurate domain-independent approach...":
(|ncsubj| |describe:2_VV0| |We:1_PPIS2| _)
(|dobj| |describe:2_VV0| |approach:7_NN1|)