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Questions tagged [information-retrieval]

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Looking to extract patterns from sequences of codes

I have the following problem: I have a registration of people who enter a building, I have the name, entry date and end date. I also have the times at which events occur inside the building. I want to ...
slow_learner's user avatar
1 vote
0 answers
40 views

Calculating co occurrence probabilities of search queries

Hi guys I want to calculate the pointwise mutual information for related search queries on an e-commerce website. In order to calculate that I need to fist calculate the co occurrence matrix for the ...
A.Bashar Eter's user avatar
2 votes
1 answer
81 views

Why does my bi-encoder converge to the mean square of the [0,1] label distribution? [duplicate]

I'm following the Bi-Encoder architecture (see here) in order to build a dense retrieval (search) system. Formally, my network encodes a query q and an item description d based on fixed ...
joko's user avatar
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2 votes
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20 views

Extracting information from bills, tax statements, etc: What ML model to use?

I have a bunch of documents such as bank statements, utilities bills, personal expenditure invoices, etc. The document types range is very broad. Some of these files are saved as pictures, others as ...
An old man in the sea.'s user avatar
0 votes
1 answer
825 views

(n)DCG without true relevance scores

I have a search engine that returns results with a normalized score on the scale 0.0 to 1.0. Higher score means higher relevancy to the input query. The ranked output scores look like this, e.g. [1.0, ...
Carsten's user avatar
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0 votes
1 answer
29 views

Extracting information from form document through supervised learning

I was searching for a while around the web and I couldn't find any solution that would give some ideas on how to solve my problem. I have a few hundreds of document with some permission forms filled ...
Primoz's user avatar
  • 101
3 votes
1 answer
3k 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 ...
Jjang's user avatar
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2 votes
0 answers
551 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 ...
Aishwarya A R's user avatar
1 vote
1 answer
132 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 ...
Josh's user avatar
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2 votes
0 answers
40 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 ...
ducPham's user avatar
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0 answers
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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 ...
EmJ's user avatar
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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'...
jon_simon's user avatar
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491 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 ...
ongenz's user avatar
  • 101
2 votes
1 answer
39 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 ...
Aaron Brick's user avatar
1 vote
0 answers
435 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. ...
Fibo Kowalsky's user avatar
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0 answers
31 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 ...
Arnold's user avatar
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0 votes
2 answers
162 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 ...
Richo N.'s user avatar
2 votes
0 answers
90 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 ...
GeorgeOfTheRF's user avatar
0 votes
0 answers
26 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 ...
Cranjis's user avatar
  • 57
2 votes
2 answers
118 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?
dontloo's user avatar
  • 16.5k
1 vote
0 answers
47 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 ...
Simon's user avatar
  • 288
4 votes
1 answer
3k 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 ...
Rainfield's user avatar
  • 151
3 votes
2 answers
783 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 ...
wirrbel's user avatar
  • 615
1 vote
0 answers
235 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 ...
hernan's user avatar
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1 vote
1 answer
84 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 ...
Robin's user avatar
  • 236
0 votes
1 answer
474 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 ...
PradhanKamal's user avatar
1 vote
0 answers
417 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 ...
tim's user avatar
  • 11
0 votes
3 answers
4k 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 ...
Binh Nguyen's user avatar
4 votes
1 answer
4k 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 ...
blueski's user avatar
  • 41
3 votes
0 answers
81 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 ...
Thomas's user avatar
  • 31
2 votes
0 answers
171 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: ...
user6321's user avatar
  • 399
1 vote
0 answers
102 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: ...
rtur's user avatar
  • 21
3 votes
1 answer
2k 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 ...
cwl's user avatar
  • 799
2 votes
2 answers
12k 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. ...
Ale's user avatar
  • 121
1 vote
0 answers
303 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 ...
emanuele's user avatar
  • 2,098
0 votes
0 answers
112 views

What is a rotation matrix and how to implement 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 ...
user6321's user avatar
  • 399
1 vote
0 answers
456 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% ...
Christos Baziotis's user avatar
1 vote
0 answers
340 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 ...
Swan87's user avatar
  • 111
1 vote
0 answers
28 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 ...
Coding Mash's user avatar
3 votes
2 answers
265 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% "...
neelshiv's user avatar
  • 314
2 votes
2 answers
1k 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, ...
user6321's user avatar
  • 399
2 votes
0 answers
99 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-...
Gabrer's user avatar
  • 156
6 votes
1 answer
5k 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. ...
yolanda_dlh's user avatar
3 votes
1 answer
3k 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,...
aces's user avatar
  • 131
0 votes
1 answer
49 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 ...
Codarus's user avatar
  • 101
1 vote
0 answers
52 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 ...
user3049183's user avatar
0 votes
0 answers
300 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,...
Vadim Smolyakov's user avatar
7 votes
1 answer
4k 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 ...
Patrick the Cat's user avatar
1 vote
1 answer
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 problem is that here is a ...
ruiruige1991's user avatar
2 votes
0 answers
759 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 ...
Manu's user avatar
  • 101