1
vote
1answer
12 views

SVM Classification with Duplicate Training Instances

I'm using SVMs with linear kernel for sentence classification (binary). My dataset contains many duplicate instances i.e. many sentences in the training set have identical feature vectors. In the ...
3
votes
3answers
37 views

In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?

I was reading over Naive Bayes Classification today. I read, under the heading of Parameter Estimation with add 1 smoothing: "Let $c$ refer to a class (such as Positive or Negative), and let $w$ ...
1
vote
0answers
21 views

Binary classification of dated text documents with seasonality

I have a collection of training documents with publication dates, where each document is labeled as belonging (or not) to some topic T. I want to train a model that will predict for a new document ...
0
votes
0answers
42 views

What algorithms should I use to perform job classification based on resume data?

Note that I am doing everything in R. The problem goes as follow: Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don't. The goal here is to: ...
1
vote
1answer
49 views

Feature selection : how to select the Information Gain threshold?

I am trying to use Information Gain to select features when classifying text with a Support Vector Machine. For each word in our training data, we computed its information gain. Then, we should keep ...
0
votes
1answer
13 views

How to increase a particular terms's weightage?

I am doing Text classification using LibSVM in Rapid Miner. I am using TFIDF values for processing documents. I need to Increase weightage of some terms in the documents(for eg. words in BOLD and ...
0
votes
0answers
23 views

How to implement data I have to svmtrain() function in MATLAB?

I have to write a script using MATLAB which will classify my data. My data consists of 1051 web pages (rows) and 11000+ words (columns). The first 230 rows are about computer science course (to be ...
2
votes
3answers
136 views

Naive Bayes: Imbalanced Dataset in Real-time Scenario

I am using scikit-learn Multinomial Naive Bayes classifier for binary text classification (classifier tells me whether the document belongs to the category X or not). I use a balanced dataset to train ...
1
vote
1answer
35 views

How do I perform an IDF calculation?

How do I perform an IDF calculation? I am uncertain as to whether IDF should be calculated in per-class level or for the entire document set (that contains multiple classes).
2
votes
1answer
45 views

Selecting number of clustering classes automatically

I am working in text clustering. I would like to find a way to identify the number of classes for the clustering process automatically rather than proving the number of class manually. Is their any ...
0
votes
0answers
30 views

What are the mathematics I need to learn, before I start research in data mining [duplicate]

I usually use text mining, graph mining, Information retrieval, and natural lanuage processing. Also i will use the fundamental concepts of data mining like classification, association and clustering. ...
0
votes
1answer
47 views

Comparing topic distributions between corpora using Latent Dirichlet Allocation and R topicmodels or python gensim

So I am working on a problem where I want to extract a set of LDA topics from one corpus, and then compare the distribution of those topics in other corpora. So basically I want to lock-in the topics ...
1
vote
1answer
60 views

Create a matrix of tf-idf values from documents

I have a set of documents like: D1 = "The sky is blue." D2 = "The sun is bright." D3 = "The sun in the sky is bright." and a ...
0
votes
0answers
24 views

In which Data Stream Mining Algorithms do Damped Windows make sense?

For Data Stream Mining, especially in Document Classification, the most common ML algorithms are Multinomial Naive Bayes, Stochastic Gradient Descent and Ozbag (ADWIN). When looking at their ageing ...
0
votes
0answers
50 views

Training a multiclass SVM

I am using Support Vecotr Machine(SVM) with 4 Class. My corpus contain 185 documents with 4 different subjects. For each subject I defined a profile with 3 or 4 keywords (of course they are separated ...
3
votes
3answers
82 views

Alternatives to bag-of-words based classifiers for text classification?

Most of the text classifiers are based on the bag-of-words approach where you loose the context that a particular word appears. As a solution (or simple solution?) we can use n-grams as features. But ...
1
vote
0answers
33 views

Hierarchiqual prediction using R

I'm pretty new in R, and I couldn't find any information about a package who can do the following: supposing that I have a set of data (for instance, different text documents), which can have several ...
0
votes
1answer
54 views

Clustering structured data: Assessing the similarity of documents that appear in tree structure

Usually when performing text document clustering, similarities across documents are assessed based on the lexical content of documents. But, in my problem, I wish to consider both the lexical content ...
2
votes
1answer
60 views

How can I evaluate the performance of a system that generates word clusters?

The word2vec tool uses deep learning to compute vector representations of words. They've mentioned that - "The word vectors can be also used for deriving word classes from huge data sets. This is ...
3
votes
1answer
70 views

Why can we use entropy to measure the quality of a language model?

I am reading the < Foundations of Statistical Natural Language Processing >. It has the following statement about the relationship between information entropy and language model: ...The ...
0
votes
1answer
36 views

Data Conversion to Standard data format in hierarchical Dirichlet process

I'm trying to test the performance of posterior inference on a set of documents with hierarchical Dirichlet process for topic modeling. How can i convert my data (document) to standard data format ...
3
votes
1answer
135 views

Machine learning techniques for spam detection, and in general for text classification

I am going to configure a system for spam detection. What I have is a dataset of labeled (spam/not-spam) strings containing, mostly, sentences. I have a background in machine learning techniques, but ...
0
votes
0answers
19 views

Posterior distribution for LDA and Newdata

I am using the 'topicmodels' package in R. I tested the posteriori probability for newdata over jss_LDA result by this code : ...
0
votes
1answer
45 views

Automatic labeling of training set

I have once meet the following question, given a training set, is that possible to do the automatic labelling? In addition, if this training set consists of plain text files, is that possible to know ...
2
votes
2answers
106 views

Dataset and papers for baseline [closed]

I'm doing a project about Topic Detection and Tracking in text. I need to perform a baseline so I can compare existing results with mine. I read some papers where they use datasets that are not so ...
0
votes
0answers
40 views

Which diffusion of latent Dirichlet allocation is helpful for assign the words corresponds to each topic?

In my corpus documents I have two different subjects. Which diffusion of LDA (asymmetric or symmetric) could help for assigning the words corresponds to Subject 1 and Subject 2 in my Topics? Here is ...
1
vote
0answers
45 views

How to extract structured information from a text string?

I have a text string containing unstructured data and I would like to analyze it in order to extract structured information. In particular, this text string specifies when a service is operational ...
0
votes
0answers
42 views

price prediction

In the project I am working we have a couple of items with different prices. Each item has description but it is possible to find items with similar description. I would like to know how can I ...
0
votes
1answer
36 views

Would one frequent term affect the textual document classification quality?

I'm trying to classify twitter messages. For example I collected some tweets about an earthquake and trained a classifier over it. A specific hashtag about the earthquake appears in almost all of the ...
3
votes
1answer
164 views

Keyword clustering

I have one million of keywords (from search queries in google), and I need to group them semantically. I have already done some research and I have found information about how to extract keywords and ...
2
votes
2answers
111 views

improve precision in text classification

I am working on binary text classification using sklearn: The length of each sample is not high (~ 200-500 characters) I use TF-IDF to get important words as TfidfVectorizer(sublinear_tf=False, ...
0
votes
0answers
39 views

the relationship between training set size and precision/recall

With respect to a classification problem, I once heard the following comment: Adding more positive training cases can increase the recall; and adding more negative training cases can increase the ...
0
votes
2answers
119 views

A multi-label classification for tagging short text

I am fairly new in the area of text mining and want to practice my skills a little. I have the following task at hand which I want to work on. I have a large list of short texts (~100.000) and every ...
1
vote
0answers
69 views

How to explain difference of importance between feature selection and model quality?

I have a data collection with a mixed feature set consisting of both numerical features and text features. The number of numerical features is quite small, i.e., 6, comparing to the number of text ...
1
vote
1answer
75 views

Classification of data with incomplete label sets

There is such a problem: we have to process multi-label classification (assignmet of tags) of text articles, using some pre-labeled training set. But for many texts in the training set, should be ...
0
votes
1answer
271 views

Search in TF-IDF

I want to find the similarity between a document with documents coded as TF-IDF in a pickle file (Python). TF-IDF is done as offline so there is no problem, but when I send a new document for ...
2
votes
1answer
88 views

How to determine if short strings of text are closely related to a larger text?

I have 1 short string of text (let's say it's a tweet, max 140 characters): "A review of my beloved Roku 3 media player" I also have a larger body of text (like a ...
0
votes
1answer
57 views

How to convert numerical values to ML feature in the range [0;1]?

I am supposed to extract a bunch of "generally useful" features from a piece of text. Use cases vary, but one could be text categorization. One thing that springs to mind here of course is the length ...
1
vote
1answer
146 views

Machine Learning System Design: a practical advice

Recently, I stared working on a machine learning competition hosted on Kagge.com. As the first step, a quick and dirty system was developed using Logistic Regression (LR). After running the system ...
3
votes
0answers
206 views

How to think of features in NLP problems

I am working on a Named Entity Recognition (NER) project. Instead of using an existing library, I decided to implement one from scratch because I wanna learn the basics of how PGMs work under the ...
3
votes
3answers
138 views

Finding multiple topics in short texts

I am building a natural language understanding component for a dialog system. The input is a natural language sentence (usually a short one), and the output should be a set of zero or more classes ...
1
vote
0answers
60 views

Difference between Taxonomy and Classification with respect to data/text mining [closed]

I am trying to understand the exact difference between Taxonomy and Classification with respect to data/text mining. I would appreciate it if you could explain with a simple example.
5
votes
1answer
150 views

Using text mining/natural language processing tools for econometrics

I am not sure whether this question is fully appropriate here, if not, please delete. I am a grad student in economics. For a project which investigates issues in social insurances, I have access to ...
0
votes
1answer
131 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
111 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, ...
2
votes
1answer
904 views

understanding of libsvm output

I applied libsvm to build a text classifier. The output looks like as follows: ...
1
vote
1answer
135 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
78 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
294 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 ...
2
votes
0answers
53 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 ...