Questions tagged [information-retrieval]

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11 views

False positive rate at K recall

I just stumbled upon a new metric I've never heard about called False Positive rate at K recall (FPR-K). Searching the internet I just managed to find more papers using the metric but none of them ...
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17 views

Metrics for Multilabel Classification

From what I've read, F1-score is a commonly used metric to assess the performance of a multilabel classification problem. However, I recently came across mAP@K and mAR@K as metrics used for ...
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1answer
18 views

Real vs True Positives

Wikipedia defines TPR (True Positive Rate) as $\frac{\text{TP}}{P}$ where: $\text{TP}$ = # of true positives $\text{P}$ = # of real positives This confuses me. I thought: $\text{TP}$ is supposed ...
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6 views

Ranking Evaluation Metric to be used in recommender when only one true positive is available

I have historic data of top k(ranked) items recommended to a user from which a user has bought only one item. What evaluation metric should be used in such cases where there is a single true positive ...
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0answers
7 views

Average Precision vs DCG: Requiring a binary ground truth?

As far as I understand, DCG and NCG can evaluate the performance of a given ranking result without requiring a binary truth. Such metrics only require a notion of (true) relevance per document in the ...
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0answers
14 views

mislabeled data Event Detection, neural language processing

I'm doing some experiment with sequence labeling problems, more specifically is Event detection problem. But I'm encountering with the mislabeled data issue. In my dataset, there are many tokens that ...
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20 views

Disadvantages of the Point-wise Approach in Learning to Rank

I'm studying approaches to learning-to-rank for Information Retrieval and I'm having trouble understanding one of the commonly listed disadvantage to using point-wise solutions like binary ...
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0answers
13 views

Retrospectively reviewing a scoring tool?

I was asked to try Cross Validated instead of Academia with this question so I hope you welcome it: I had a study that had a problem in which we retrospectively evaluated a risk score by looking back ...
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0answers
25 views

What is the standard metric used in recommendation systems to evaluate the rankings?

I was searching for a metric to do this for a while and still could not find. More specifically, my problem is as follows. I have a ranked golden corpus. For example, consider that it looks as ...
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13 views

Correlation between Tags and Topics

I would like to know how do we get $n$ tags (e.g. 3 tags) at most the will give the most number of questions related to a topic on Stack Overflow/Exchange. For example, suppose that the combinations ...
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0answers
19 views

AUC-like measure for multiple simultaneous classification tasks?

I know that given an ordered set of binary labels, and equally-sized ordered set of scalar predictions, we can quantify how cleanly the predictions separate the labels into clean buckets of 0's and 1'...
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29 views

Weighting for precision and recall

I want to integrate the notion of weighting into an evaluation. I am wondering if it is appropriate/correct to calculate precision and recall scores by adding a weighting on true positives, false ...
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1answer
18 views

Models for ranking possible classifications by confidence

What are good means of finding the various highest confidences or likelihoods for a (say) hundred possible outcomes of a classification problem? Inputs belong to only one class, unlike document ...
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44 views

Youden's J statistic can be negative but it is said to be between 0 and 1

J = (true_positives x true_negatives - false_positives x false_negatives) / (positives x negatives) where positives and negatives are the number of real positive and real negative samples. ...
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24 views

What are most recent research work on the problem of key phrases extraction from a text corpus?

I am interested in the problem of extracting key phrases from a text corpus. This is different from the keyword extraction problem, which is only for a particular document. This problem helps us, for ...
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1answer
63 views

How is Cross validation used with non-machine learning problems?

I am fairly new in the field of Information retrieval. I have basic knowledge about machine learning. I understand the purpose of CV in the context of Machine learning. However, I've become a bit ...
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0answers
64 views

What is an appropriate Evaluation Metric and corresponding Loss function which best optimize the metric for a classification based FAQ Chatbot?

I am developing a FAQ chatbot to display/return only one correct answer in a chat window for a given question from the user. I know MRR & MAP make sense as an evaluation metric for information ...
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20 views

Estimating a surprise of a word in context

What will be the best way to estimate the entropy/surprise of a word in a specific context? Let's say to compare the surprise of: context: "I watched the movie in my" word: Computer I ...
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2answers
68 views

What are commonly used methods to represent a document by a vector?

Methods that I know of Bag of words + weighting: tf-idf, bm25 Topic models: LSA, LDA Word/sentence/document embedding Are there other commonly used methods to represent a document by a vector?
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38 views

ML Approach for Optimizing Field Boosts for Search (Information Retrieval)

I have been experimenting with different methods for tuning our search engine's field boosts. In Solr or Elastic Search, you specify the importance of matches in each field when configuring the search ...
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1answer
2k views

Remove bias in ranking evaluation

In ranking, a commonly used method to evaluate the performance of a ranking algorithm is calculating ranking metrics such as MAP, NDCG. In use cases where there is no ground truth signal, some proxy ...
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2answers
169 views

inverting the binomial distribution: probability distribution for number of trials necessary to have a given number of successes

The binomial distribution gives me a distribution for the number of successes in several Bernoulli trials, k, given parameters N the total number of trials and q, the success probability for one ...
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0answers
107 views

Learning to rank and traditional information retrieval evaluation

I have some questions about best practices in information retrieval (unsupervised) vs learning to rank evaluation. How necessary in a train-validation-test or cross-validation scenario? is it ...
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1answer
51 views

What contests/datasets for expertise retrieval ?

Expertise retrieval is a difficult task to define. This article describes the task of expert finding as "finding the right person with the appropriate skills and knowledge". Applications exist in ...
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1answer
405 views

Extracting Part of Speech (Source and Destinations) using text mining/NLP?

I need to extract the source and destination terms from the text documents using text mining/NLP/Information Retrieval ? ex : i am travelling from New York to London. i am heading towards ...
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0answers
382 views

NDCG for recommender algorithm

I need to apply NDCG over the results of a recommender algorithm, but was not able to find any proper example that suits my use case in order to find out if my implementation is correct. Here is my ...
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3answers
1k views

What algorithm should I use to predict a continuous dependent variable from multiple continuous & categorical independent variables?

I'm software engineer of an E-commerce company, facing a problem like this: An e-commerce shop sells their products daily and wants to know what conditions that might improve their sales. I'm ...
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1answer
3k views

Both validation loss and accuracy goes up in neural network

I'm training a 2-layer CNN model on audio samples, represented as CQT. There are ≈160k samples, many that are very similar since they originate from the same instrument and/or audio file. 10% have ...
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0answers
73 views

How can I deal with the mismatch between the vocabularies of questions and answers in a closed domain QA system?

I am building a question answering system that given a legal document attempts to answer questions related to the document. For example a tenancy agreement is given to the system and the user asks ...
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0answers
137 views

How to adjust your dataset so it fits a power-law? [closed]

I have created a dataset of pictures taken at the museum of different paintings. The dataset is divided into 113 different categories (paintings) and contains around 4.8k images. Just to be clear: ...
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101 views

Representing Text for Section Labeling While Avoiding Bias

Problem Background: I have free form text data with more or less arbitrary formatting/structure, but semantically it can be broken down like this: ...
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1answer
1k views

How to find “similar documents” after a Latent Dirichlet Allocation model is built

Let's say I run an LDA model with 3 topics on 5 documents. After the model is learned (with Gibbs sampling presumably), I have topic distribution for each document, shown as the following: My ...
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2answers
5k views

Drawbacks with Cosine Similarity

I am assessing the similarity between documents represented as vectors of tf-idf values. I know that the cosine similarity is a well-defined and commonly used measure in information retrieval. ...
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0answers
153 views

Why F-1 is a better score than the harmonic mean between true negative rate and recall?

Beside of the easy interpretation, F1 measure is very sensible to the relative frequency between positives and negatives. ROC AUC metric don't, but it is difficult to optimize. Are there any special ...
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95 views

What is a rotation matrix and how to implemnt it?

In Revisiting the VLAD Image Representation the authors introduce Local Coordinate System, i.e. they: we learn off-line (for each visual word) a rotation matrix Qi from training descriptors mapped ...
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0answers
440 views

Union of good feature sets degrades accuracy

I am doing binary text classification and I have some feature sets (unigrams, bigrams, dependencies, etc.) and each one of these performs very good individually. For example unigrams alone achieve 89% ...
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0answers
135 views

Contextual Matching algorithm implementation in Python

I have been trying to implement an algorithm using Python in order to perform contextual matching in a set of documents. My ultimate goal would be to be able to perform queries using positive keywords ...
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0answers
25 views

Average Number of Documents Containing a Term

I am working on a research project in which I am using inverted index for terms and documents in one of the techniques. I am doing algorithmic analysis for all the methods so that I can compare and ...
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2answers
82 views

Is there a ranking metric based on percentages that favors larger magnitudes?

I have two groups, "in" and "out," and item categories that can be split up among the groups. For example, I can have item category A that is 99% "in" and 1% "out," and item B that is 98% "in" and 2% "...
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2answers
811 views

Can we use Bag of Visual Words to compute similarity between images directly?

I'm implementing a Content Based Image Retrieval application (CBIR). I've read about the Bag of Features model and it's considered an intermediate-step algorithm in some application. For example, ...
2
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0answers
91 views

F-measure and hypothesis testing

I would compare two classifiers (A and B), where B is obtained from A. I would exploit the F-measure computed on two samples (of two independent populations, p1 and p2, respectively): A -> F-...
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1answer
3k views

Difference between Log Entropy Model and TF-IDF Model?

I would like to understand what are the differences/advantages in using TF-IDF or the Log Entropy model for represeting documents and queries in an information retrieval system using diferent weights. ...
2
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1answer
2k views

Where did sublinear tf-idf originate?

I have often come across this weighting scheme for tf-idf (term frequency - inverse document frequency) in text mining. I am wondering where it came from (for citations). I've searched very rigorously,...
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1answer
33 views

Where do initial document values come from in K-means document clustering?

So the K-means algorithm seems simple enough as I understand it: given some documents, turn those documents into points, initialize some number of k (centroids), assign document-points to nearest ...
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0answers
51 views

How to decide what is the relevant group in a precision and recall computation?

One of the most famous measurements for an information retrieval system is to compute its precision and recall. For both cases, we need to compute the number of total relevant documents and compare it ...
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0answers
294 views

topic modeling for image retrieval

I'm interested in learning a topic model from a bag of visual words for image retrieval. I can compute V cluster centers (visual words) of SIFT descriptors at keypoints for each training image and fit,...
6
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1answer
3k views

The Effect of Stopword Filtering prior to Word Embedding Training

Recently I have played with the pretrained GLOVE word embedding model for Twitter http://nlp.stanford.edu/projects/glove/ I notice that common stopwords are existing in the model. That is, there is ...
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1answer
1k views

how to find the nearest neighbor of a sparse vector

I have about 500 vectors,each vector is a 1500-dimension vector, and almost every vector is very sparse-- I mean only about 30-70 dimension of the vector is not 0。 Now, the problom is that here is a ...
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0answers
593 views

Is there an algorithm for determining scoring function (Utility function) in ranking instances?

So while going through the topic of Preference learning, I came to know about "instance ranking". Since the problem which I'm working on requires me to rank the instances (data point), is there any ...
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0answers
229 views

Is this ordering of examples redundant in Rank SVM?

I was studying RankSVM[1] and I was observing the ranking pairs listed in the example here. Formulation can be seen on page 4 of this paper. One of the ordering is:...