2
votes
1answer
32 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
47 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
25 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
81 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
16 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
39 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 ...
1
vote
2answers
71 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
30 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
37 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
37 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
31 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
101 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
92 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
35 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
83 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
60 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
64 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
163 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
66 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
51 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
122 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
133 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
132 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
52 views

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

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
132 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
107 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
98 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, ...
1
vote
1answer
726 views

understanding of libsvm output

I applied libsvm to build a text classifier. The output looks like as follows: ...
1
vote
1answer
125 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
72 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
259 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
51 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 ...
1
vote
1answer
80 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
240 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
379 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
155 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 ...
3
votes
2answers
308 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
101 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 ...
14
votes
3answers
778 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
142 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
153 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 ...
6
votes
3answers
792 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
1k 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
467 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
197 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? ...
4
votes
2answers
220 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 ...
5
votes
5answers
4k 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
71 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
610 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 ...
4
votes
1answer
534 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 ...