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14 views

Learning to Rank: query-dependent vs. query-independent features

I've been doing some reading about learning to rank - specifically lambdaMART - and one thing I am confused about is the role of features. When training a model, should one only use query-dependent ...
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0answers
10 views

LexRank damping factor

I am looking into using LexRank to do some text summarization. I am looking at the original paper. One thing that puzzles me is whether a damping factor is used or not. The formulae are all using it, ...
0
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1answer
25 views

Choosing the best set of keywords

I have a dataset of tweets collected using twitter streaming API on a particular topic (say 'football') using around 40 keywords. Now if I'm going to track the same topic (football) in future how do I ...
0
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1answer
27 views

“Mean average precision” (MAP) evaluation statistic - understanding good/bad/chance values

I'm evaluating a multilabel classifier. I'm familiar with the Area Under the Curve statistic, which has some nice properties (e.g. chance level is always 50%). But for some applications, it's more ...
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0answers
8 views

relevance feedback - (Pseudo relevance feedback)

While studying relevance feedback(Pseudo relevance feedback), I have learn that the model can go horribly wrong for some queries. Can anyone give reasons why this is?
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0answers
17 views

Rocchio algorithm - relevance feedback

I am studying the Rocchio alorithm. I understand how it works. And typically we set Positive feedback is more valuable than negative feedback (so, set β < γ; e.g. γ = 0.25, β = 0.75). And many ...
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1answer
34 views

What is the difference between accuracy and agreement?

According to Manning et al. (p. 155) accuracy is the sum of the diagonal in the confusion matrix divided by the sum of all items. On the other hand, following Artstein and Poesio (p . 558) precisely ...
0
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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 ...
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2answers
70 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 ...
1
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1answer
22 views

“Exotic” text representations methods?

I'm looking to the different methods of representing a text into a machine-readable format. However, until now, I only found "Bag of Words" approachs with a lot of variations (boolean BoW, weighted ...
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0answers
46 views

Calculation of normalization constant

This is an equation from the paper "A Content-based Probabilistic Correction Model for OCR Document Retrieval" - Rong Jin, Alex G. Hauptmann , ChengXiang Zhai $$P(w|M_{\text{orig}})= ...
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2answers
84 views

Most important journals in data mining/ML, NLP and IR?

Can you please provide with me with the names of the most important journals in data mining, machine learning, natural language processing and information retrieval?
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0answers
25 views

User intent identification in web search queries using unlabeled data

Given a list of unlabeled search queries, is it possible to say which query is related to a certain topic? For example, if we want to select the queries where the intent of the user is to download or ...
2
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0answers
16 views

Evaluation of a semi-supervised ranking model

I learned ranking model with graph-based semi-supervised approach, while labeled (just positive) and unlabeled (positive and negative) data is both used in training. With the model, all of the data ...
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0answers
34 views

Algorithms for keyphrase clustering

Are there any standard algorithms for keyphrase clustering. There are several algorithms for keyphrase extraction from a corpus. For e.g. this publication reviews some of the popular keyphrase ...
2
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0answers
48 views

Why are ERR (Expected Reciprocal Ranking) scores not normalized?

It seems to me that normalized ERR (Expected Reciprocal Ranking) scores (ERR scores of your ranking algorithm divided by ERR score calculated for the ground truth ranking) are more useful than the ...
0
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1answer
149 views

K-Fold Cross validation and F1 Measure Score for Document Retrieval using TF-IDF weighting and some customised weighting schemes

I am developing a search engine system based on the vector space model, and I am confused on what approach I should take to evaluate the system. My case is this: I have a set of indexed documents ...
1
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1answer
98 views

Clustering of documents that are very different in number of words

I have a corpus of 643 documents with different sizes and my goal is to cluster them according their topics and label each cluster with semantic name for its main topic. I have tired different ...
1
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1answer
68 views

What is the standard procedure for evaluating a user-based CF algorithm with a dataset offline?

I have read some papers and other materials about the evaluation of recommender systems (RS). Most of them discuss the various properties of RS (e.g. accuracy, diversity, etc.), and metrics for ...
2
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2answers
154 views

Other documents features than tf-idf for clustering?

What are other feature representations for documents that are used for clustering textual documents? The only representation I'm aware of is tf-idf. Are there other ones?
0
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0answers
90 views

Document Query similarity for very short documents

I am working on a project which incorporates a basic implementation of the vector space model. A collection of documents d1...dn form the columns of the term document matrix, the rows represent the ...
1
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0answers
55 views

Mutual Information for clustering

I'm working on a document clustering application and decided to use Normalized Mutual Information as one of the measures of effectivenes. But I don't really understand how to implement this in that ...
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0answers
49 views

Unusual Precision-Recall Curve

Looking at precision-recall curves to evaluate an information retrieval system. Typically, these are done with a set of samples, with what users have picked out of a set of given items. I have an ...
1
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0answers
51 views

MAE/MSE with or without square root

I read some papers about recommender systems and information retrieval, where Mean Absolut Error and Mean Squared Error are mentioned. But I've found some differences between the formal definition of ...
0
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1answer
26 views

Discounted cumulated gain

I've a little question regarding the Discounted Cumulated Gain (DCG) (Sorry, I couldn't find the papers of Järvelin and Kekäläinen). Can this evaluation-metric be used when a information retrieval ...
4
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3answers
224 views

Summary statistics of the precision-recall curve

From what I understand, one can use the AUC of the ROC curve as a summary statistic of the full curve. Q1. Are there any similar summary statistics that one can use on a single precision-recall ...
1
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0answers
69 views

Is that overfitting?

Given a document-term matrix $X$, where $$X(d, t) = \textit{occurrences of 't' in 'd'}$$, it's possible to compute it's Truncated Singular Value Decomposition:$$X_k = U_k \Sigma_k V_k^T$$ Then, for a ...
0
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3answers
134 views

Is there any dataset or api that gives a list of infrequent words? [closed]

I'm actually working on an information retrieval project, and I want to extract words that are of significance from a sentence. This is somewhat opposite to stopwords. In a sentence like: He was a ...
3
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1answer
371 views

A parellel between LSA and pLSA

In the original paper of pLSA the author, Thomas Hoffman, draw a parallel between pLSA and LSA data structures that I would like to discuss with you. Background: Taking inspiration the Information ...
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0answers
80 views

Using sentiment lexicons or all words processing for sentiment analysis?

I am learning sentiment analysis to apply it to twitter real time data to predict user's mood. I ponder about using which alternative way to do that data mining job. Use all words to process and ...
1
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0answers
29 views

Evaluation and Testsets for NNMF

I am trying to evaluate my recommender system which uses Non-negative Matrix Factorization. Some things that I evaluate are How does the size of the feature matrix affect the recommendations How ...
3
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1answer
1k views

Euclidean Distance b/t unit vectors or cosine similarity where vectors are document vectors

I was reading Similarity Measures and suddenly my whole world was falling apart. I have implemented a search engine using clustering techniques. For clustering, I used k means which uses Euclidean ...
2
votes
2answers
200 views

How do you predict a continuous value from many booleans & a continuous value?

Hello: I am a computer science student working as a research assistant in an undergrad IR lab, feeling spectacularly out of my element. Given an input of a single continuous value and a vector of ...
2
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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 ...
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2answers
275 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 ...
3
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0answers
299 views

Similarity calculations for arrays

First of all, my apologies if I mess up the terminology. I've been out of math for several years, so I'm certain I'm going to use terms incorrectly. Also, though I concentrated mathematics in college, ...
5
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1answer
274 views

Choosing a measure of similarity to quantify similarity between individuals on a set of personality scales

I have a bunch of users. Each user has a number of personality attributes, such as "fitness level" or "eco-consciousness", rated on a scale from 1 to 5. I want to calculate how similar two users are, ...
2
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0answers
148 views

How to to calculate the topic distribution of a document

I have a simple (may be stupid) question. I want to calculate Kullback–Leibler divergence on two documents. It requires probability distribution of each document. I do not know how to calculate ...
3
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1answer
602 views

Using latent Dirichlet allocation for information retrieval

I am working on understanding various document ranking algorithms like (TF-IDF, LSI, language models, etc) by actually implementing them. I want to understand LDA and using various resources to ...
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2answers
367 views

How to compute term frequency and find clusters in a dataset composed of strings?

I am currently looking for some Information Retrieval techniques. I have a SQL database table containing strings. It has 1000 records, each being a random sentence I picked from random web sites. I ...
3
votes
2answers
995 views

Comparing cosine similarities for tf-idf vectors for documents with different length

I'm computing cosine similarities between 2 vectors. These vectors are information retrieval query and document representations respectively. They have been computed using tf-idf weights. Since my ...
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2answers
540 views

KL divergence calculation

I am wondering that how one can calculate KL-divergence on two probability distributions. For example, if we have ...
7
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2answers
1k views

Can one use Cohen's Kappa for two judgements only?

I am using Cohen's Kappa to calculate the inter-agreement between two judges. It is calculated as: $ \frac{P(A) - P(E)}{1 - P(E)} $ where $P(A)$ is the proportion of agreement and $P(E)$ the ...
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1answer
3k views

Measuring Document Similarity

To cluster (text) documents you need a way of measuring similarity between pairs of documents. Two alternatives are: Compare documents as term vectors using Cosine Similarity - and TF/IDF as the ...
22
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7answers
1k views

Statistical classification of text

I'm a programmer without statistical background, and I'm currently looking at different classification methods for a large number of different documents that I want to classify into pre-defined ...