I am currently integrating the BERT model listed on https://developer.apple.com/machine-learning/models/#text into an iOS application and have had difficulty removing answers that have low certainty.
I have used the sample code found at the link above but because I wanted to answer questions based on larger volumes of text, I loop over an array of paragraphs and predict an answer for each one. However, the model does not return nil or "No Answer" if an answer is not found and instead returns a (seemingly) random substring. I suppose what I am trying to ask is: is it possible to access the certainty of BERT's response to filter out unlikely results? Or is there another way to get BERT to only return results above a set certainty threshold?
After hours of searching, I've now found a solution. Ironically it only took three lines of code, but here it is anyway:
if bestSum < 7.5 {
return nil
}
I implemented this in the findBestLogitPair() method in the BERTOutput.swift file as provided in Apple's sample code for text analysis using BERT. I have now discovered that the word logit does kind of mean probability in statistics - but being a programmer, I had no idea!
Related
I'm trying to compare J48 and MLP on a variety of datasets using WEKA. One of these is: https://archive.ics.uci.edu/ml/datasets/primary+tumor. I have converted this to CSV form which can be easily imported into WEKA. You can download this file here: https://ufile.io/8nj13
I used the "numeric to nominal" on the class and all the attributes to fit the natural structure of the data. However, when I ran J48 (and MLP), I got a bunch of question marks "?" in my output, presumably due to not having enough observations/instances of the appropriate type.
How can I get around this? I'm sure there must be a filter for this kind of thing. I've attached a picture below.
The detailed accuracy table is displaying a question mark since no instance was actually classified as that specific class. This for example means that since no instance was classified as class 16, WEKA can not provide you with details regarding said class 16 classifications. This image might help you understand.
In regards to the amount of instances of the appropriate class, you can use the ClassBalancer filter under, found at weka/filters/supervised/instance/ClassBalancer. This should help balance out the amount of the various classes.
Also note that your dataset contains some missing values, this could be solved by either discarding the instances with missing data or running the ReplaceMissingValues filter, found at weka/filters/unsupervised/attribute/ReplaceMissingValues.
I saw how to do this from a DataSet object, and I saw a setLabel method, and I saw a getLabelMaskArrays, but none of these are what I'm looking for.
Am I just blind or is there not a way?
Thanks
Masking is for variable length time series in RNNs. Most of the time you don't need it. Our built in sequence dataset iterators also tend to handle these cases. For more details see our rnn page: https://deeplearning4j.org/usingrnns
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.
Here's the problem -- I have a few thousand small text snippets, anywhere from a few words to a few sentences - the largest snippet is about 2k on disk. I want to be able to compare each to each, and calculate a relatedness factor so that I can show users related information.
What are some good ways to do this? Are there known algorithms for doing this that are any good, are there any GPL'd solutions, etc?
I don't need this to run in realtime, as I can precalculate everything. I'm more concerned with getting good results than runtime.
I just thought I would ask the Stack Overflow community before going and writing my own thing. There HAVE to be people out there who have found good solutions to this before.
These articles on semantic relatedness and semantic similarity may be helpful. And this SO question about Latent Semantic Analysis.
You could also look into Soundex for words that "sound alike" phonetically.
I've never used it, but you might want to look into Levenshtein distance
Jeff talked about something like this on the pod cast to find the Related questions listed on the right side here. (in podcast 32)
One big tip was to remove all common words, like "the" "and" "this" etc. This will leave you with more meaningful words to compare.
And here is a similar question Is there an algorithm that tells the semantic similarity of two phrases
This is quite doable for reasonable large texts, however harder for smaller texts.
I did it once like this, and it worked pretty well:
Filter all "general" words (like a, an, the, in, etc...) (filters about 10-30% of the words)
Count the frequencies of the remaining words, store the top x of most frequent words, these are your topics.
As an extra step you can create groups of 2/3/4 subsequent words and compare them with the groups in other texts. I used it as a measure for plagerism.
See Manning and Raghavan course notes about MinHashing and searching for similar items, and a C#(?) version. I believe the techniques come from Ullman and Motwani's research.
This book may be relevant.
Edit: here is a related SO question
Phonetic algorithms
The article, Beyond SoundEx - Functions for Fuzzy Searching in MS SQL Server, shows how to install and use the SimMetrics library into SQL Server. This library lets you find relative similarity between strings and includes numerous algorithms.
I ended up mostly using Jaro Winkler to match on names. Here's more information where I asked about matching names on SO: Matching records based on Person Name
A few algorithms based on Levenshtein Distance are also available in the SimMetric library and would probably be useful in your application.