Tagged Questions
0
votes
1answer
46 views
How to adjust machine learning training data set with time
I'm using machine learning to do text classification right now, I first use a training data to train my classifier, then use this classifier to classify text document into different classes. With the ...
3
votes
0answers
40 views
Latent Semantic Analysis - Co-occurrence of words
Let $A[n\times m]$ represents the term-document matrix, where, $n$ is the number of terms and $m$ is the number of documents. This matrix can be composed into 3 matrices (SVD decomposition) such as,
...
0
votes
1answer
126 views
understanding of libsvm output
I applied libsvm to build a text classifier. The output looks like as follows:
...
1
vote
1answer
61 views
Derivation of the posterior over topics in LDA
When studying the latent Dirichlet allocation, I am not very clear about some procedures in their deriving equations. Please refer to the attached figure, how to understand those two steps, marked as ...
1
vote
0answers
51 views
Choosing a multiclass strategy
What would be the best approach to choosing the multi-class strategy (MSVM, OVA, ECOC) for classifying a large number of classes with limited examples of each class?
Another two factors that define ...
2
votes
1answer
164 views
Simple text classifier: classification taking forever?
I work for a small tech startup, and I want to classify or users into demographics based on the domain of their email address. When users sign up to our site, they can enter a job category, or pick ...
0
votes
0answers
56 views
LIBSVM-based classifier assign very low score to positive validation files
Recently, I have been applying the LIBSVM to build a classifier based on a set of documents. The positive set has about 20000 files and negative set has about 50000 files. The built classifier is then ...
2
votes
0answers
42 views
Measuring dispersion of tokens within a text file
I am working on a code analyzer application. It is essentially a piece of software that parses and interprets other programs' code and comes up with various metrics, findings, statistics, and ...
0
votes
0answers
53 views
the effects of feature matrix format on the training time of LIBSVM
I am using Libsvm to perform text classification tasks. I normally uses binary occurrence, TF/IDF to build feature set for the input documents. It normally takes quite longer for Libsvm to finish ...
1
vote
1answer
66 views
Relationship between number of training set and classification performance
Are there any research/paper on the relationship between the number of documents for training and the classification performance using support vector machine?
15
votes
1answer
208 views
I want to build a crime index and political instability index based in news stories
I have this side project where I crawl the local news websites in my country and want to build a crime index and political instability index.
I have already covered the information retrieval part of ...
3
votes
1answer
212 views
How to optimize hyper-parameters in LDA?
After reading Hanna Wallach's paper Rethinking LDA: Why Priors Matter, I want to add hyper-parameter optimization to my own implementation of LDA. However, the paper doesn't given any details about ...
0
votes
1answer
92 views
Regarding the feature generation method with SVM-based classification method
When using SVM to build classifier for a collection of documents, we can use term occurrence, term frequency or even TF/IDF. I would like to know what are the main disadvantages of using term ...
1
vote
1answer
108 views
The general approaches for improving a SVM-based classifier which is low precision and high recall
I built a SVM-based classifier against a data set, the precision is about 66% and the recall is about 88%. Generally, what are the options to tune the parameter that can increase the precision?
0
votes
0answers
74 views
Adding training examples to Bayesian classifier reduces accuracy
I'm working on a problem to predict/classify overall sentiment of a large amount of text, which I can verify on the next day. Each data point is a day and is composed of multiple articles. I bin the ...
9
votes
1answer
322 views
Machine learning techniques for parsing strings?
I have a lot of address strings:
1600 Pennsylvania Ave, Washington, DC 20500 USA
I want to parse them into their components:
...
1
vote
1answer
99 views
Possible reason for failing to build a support vector machine
I was trying to build a classifier for a set of documents using a support vector machine. I choose to build the feature space using term occurrence. While experimenting, I found the following ...
6
votes
1answer
117 views
What are Effective Regression Techniques for Linguistic Analysis of Linked Data?
Cross-post from MathOverflow where it was suggested that I might get better results here.
I am in the early stages of a problem that involves parsing a large number ($\approx 5 \times 10^9$) of ...
5
votes
3answers
425 views
Support vector machine for text classification
I am currently having a data set, class 1 with about 8000 short text files and class 2 with about 3000 short text files. I applied LibSVM and tried a couple of parameter combinations in the ...
6
votes
2answers
476 views
Topic models and word co-occurrence methods
Popular topic models like LDA usually cluster words that tend to co-occur together into the same topic (cluster).
What is the main difference between such topic models, and other simple ...
3
votes
1answer
290 views
Bag of words vs vector space model?
What is/are the difference/s between these text representation models: Bag of words and vector space model?
1
vote
2answers
142 views
Building the vocabulary in document classification
Assume I have a collection of documents and I want to use tf-idf as my document weighting measure. If I want my vocabulary to be of size 100, how do I choose those 100 words in the vocabulary? ...
3
votes
2answers
154 views
Which weighting factor to use for text categorization
I am working on a text categorization task, and I possess 21,000 documents for training, and (for the time being), 7000 documents for testing. I construct the doc-term matrix for both training corpus ...
4
votes
5answers
3k views
Is cosine similarity a classification or a clustering technique?
In document classification, is cosine similarity considered a classification or a clustering technique? But you need training data with the cosine similarity for creation of the centroid right?
2
votes
0answers
61 views
Software library for Hidden Markov Modeling of a large text database
Given
we have a large database of texts (e.g. product descriptions)
and we want to extract multiple types of information (e.g. brand, release date, features, price, etc.)
what's a good library to ...
3
votes
1answer
392 views
Feature selection methods for document classtification
I have a simple document classification problem where i need to classify some documents to a definite set of classes.
I need to perform a feature selection (where I will select the most important ...
3
votes
1answer
275 views
Regarding using bigram (N-gram) model to build feature vector for text document
A traditional approach of feature construction for text mining is bag-of-words approach, and can be enhanced using tf-idf for setting up the feature vector characterizing a given text document. At ...
2
votes
1answer
157 views
Software packages that can construct feature representation for a given text file using N-gram model
Are there any open source software packages, (Java, Matlab, R) that can generate a feature representation for a text document using N-gram model?
1
vote
1answer
117 views
Regarding the R packages that share the similar functionalities of NLTK toolkit
Are there any R packages that share the similar functionalities of NLTK toolkit?
4
votes
2answers
334 views
Feature selection for the text mining?
Before performing the task of text mining, we need to select the features for characterizing each given document. Are there any systematic guidance on choosing the document features? How does the ...
4
votes
2answers
158 views
How to classify country names given possible alternate spellings or abbreviations?
Let's say I have a list of users who have specified the country they reside in by typing in something. I want to find the total number of users who came from the US, the UK, and everywhere else. But, ...
3
votes
1answer
486 views
The input parameters for using latent Dirichlet allocation
When using topic modeling (Latent Dirichlet Allocation), the number of topics is an input parameter that the user need to specify.
Looks to me that we should also provide a collection of candidate ...
7
votes
2answers
202 views
Understanding and applying sentiment analysis
I was just having been assigned a project of conducting sentiment analysis for some document collections. By Googling, a lot of sentiment-related research has popped up.
My questions are:
What are ...
8
votes
2answers
2k views
Two R packages for topic modeling, LDA and topicmodels?
It seems that there have two R packages for running Latent Dirichlet Allocation. One is LDA, authored by Jonathan Chang; and another is authored by Bettina Grün and Kurt Hornik. What are the ...
1
vote
0answers
29 views
The approach of labelling a collection of documents using a shared topic
I have a collection of documents, and know they may share a single topic. Is there a way to identify this topic? I know LDA (Latent Dirichlet Allocation) is an approach. But LDA result is to associate ...
6
votes
1answer
61 views
Classification of conversations based on content
I'd like to be able to design a classifier that can distinguish between different types of conversations (not necessarily tell anything about mood, sincerity, or outcome, that's a bit too far ...
2
votes
1answer
159 views
Text mining “power calculations”?
I have a list of 6,500 or so medical treatments, of which I have classified 700 or so as involving a physician or not. I am interested in both the specific question of how to calculate whether 700 ...
14
votes
2answers
244 views
Why does Natural Language Processing not fall under Machine Learning domain? [closed]
I encounter it in many books as well as web. Natural Language Processing and Machine Learning are said to be different subsets of Artificial Intelligence. Why is it? We can achieve results of Natural ...
0
votes
0answers
90 views
Keyword ranking using Bayes theorem
I have news read organization. I have a set of articles. I listed article by its title. I bring the list to customer. Customer can select article of his own interest.
Now the problem is, customer ...
1
vote
0answers
32 views
Building document exemplar training models for SVM
What are the best methods for building document exemplar training sets for classification of unstructured data (documents and emails) using SVM? How do I optimize F-scores for these models when using ...
3
votes
0answers
73 views
Word ranking using Bayes theorem
I have problem with find ranking of words while computing multiple contents.
I'm splitting each words from the contents except stop words.
I have individual word counting with all contents, i.e ...
5
votes
2answers
207 views
Sophisticated models for classifying short pieces of texts
I have about 30000 book names assigned to 6 categories, and I want to build scalable and accurate classifiers. So far I have only been able to use Naive Baye's and LibLINEAR classifiers and they both ...
0
votes
1answer
220 views
Is there a corpus specifically for categories like sports, entertainment, or health?
I am experimenting with Classification algorithms in ML and am looking for some corpus to train my model to distinguish among the different text categories like sports, weather, technology, football, ...
10
votes
1answer
431 views
Is cross validation a proper substitute for validation set?
In text classification, I have a training set with about 800 samples, and a test set with about 150 samples. The test set has never been used, and waiting to be used until the end.
I am using the ...
5
votes
2answers
606 views
Why does ridge regression classifier work quite well for text classification?
During an experiment for text classification, I found ridge classifier generating results that constantly top the tests among those classifiers that are more commonly mentioned and applied for text ...
3
votes
0answers
113 views
Category selection for text classification
It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
2
votes
0answers
59 views
Statistical analysis on categories before text classification
I want to classify text by different topics.
However, one of the current problems is that there are several topics/categories that are quite intuitively independent and statistically standalone, but ...
5
votes
2answers
958 views
Semi-supervised learning, active learning and deep learning for classification
Final edit with all resources updated:
For a project, I am applying machine learning algorithms for classification.
Challenge:
Quite limited labeled data and much more unlabeled data.
Goals:
...
4
votes
2answers
293 views
Machine learning in web application?
There are a lot of outstanding machine learning/data mining standalone applications available in different languages like Java, Python, and others.
However, I wonder, practically, in case of applying ...
2
votes
3answers
298 views
Automatic text quality grading
I came up with an idea to use machine learning for automatic grading of topic-specific texts.
More specifically, I will first use normal text classification techniques to sort all candidate texts ...
