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Questions tagged [information-retrieval]

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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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0answers
7 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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3answers
397 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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2answers
91 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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2answers
4k views

Is it ok to get negative Cosine Similarity using LSA?

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?
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1answer
1k 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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0answers
11 views

What values should the Interpolated 11-point precision curve, measured at k, have when P@k = 0?

I'm evaluating a few retrieval models using the 11-point Interpolated precision x recall curve (here is a description of the protocol I've been using: https://nlp.stanford.edu/IR-book/html/htmledition/...
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81 views

LAMBDAMART model gives low training score for queries having large number of candidate documents

I am using LAMBDAMART as the learning to rank algorithm to rank relevant documents for a given query.The model performs well for the queries having lower number(300-400) of candidate documents.However ...
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21 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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7answers
2k 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 ...
2
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2answers
1k 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 ...
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15 views

Tackle inaccurate query returns of search

I have a large commodity dataset consisting of millions of commodites, where each commodity entity consists of commodity_name, ...
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1answer
40 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
29 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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0answers
17 views

Using clickthrough data for training a ranking function

Suppose that you have clickthrough data in the following form -- (query, clicked url, frequency). I wonder if there is any way of using the data to train a ranking function. Naively, you can treat ...
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0answers
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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4answers
6k views

What is the best algorithm to find similar text documents?

I have many text documents and I would like to find similar documents to each document within my data set. Is Latent Dirichlet Allocation (LDA) the best way to do that, or are there other algorithms ...
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1answer
126 views
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1answer
857 views

Normalised score for BM25

BM25 provides a function that assigns a score that is a function of a query and a document. The score is computed for each document in the collection and can be used to rank documents, but this score ...
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2answers
57 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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23 views

Positional similarity

What is a good positional similarity measure? Bottom line - words, which are next to each other should be ranked higher (no semantic or fuzzy matching needed). I want to use this in addition to TF/IDF-...
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0answers
19 views

Which curve comparison should I use to evaluate the performance of a recommender?

I am building a recommender system on the Last.FM dataset (link here) (1,892 users and 17,632 artists and the number of times a particular artist was listened to by a user). Next, the raw dataset was ...
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0answers
32 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

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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1answer
33 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
1k 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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0answers
410 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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2answers
622 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, ...
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0answers
69 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 ...
2
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1answer
640 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
3k 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
24 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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1answer
386 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 ...
5
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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. ...
9
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1answer
2k 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
341 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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0answers
70 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 ...
2
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0answers
112 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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0answers
99 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: ...
9
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1answer
10k views

Mean Average Precision vs Mean Reciprocal Rank

I am trying to understand when it is appropriate to use the MAP and when MRR should be used. I found this presentation that states that MRR is best utilised when the number of relevant results is less ...
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0answers
115 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 ...
8
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1answer
445 views

Why does Lucene IDF have a seemingly additional +1?

From the Lucene docs $\text{IDF} = 1 + \log\left(\frac{\text{numDocs}}{\text{docFreq}+1}\right)$ In other references (i.e. wikipedia), IDF is typically calculated as $\log\left(\frac{\text{numDocs}}{...
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1answer
2k 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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0answers
90 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
104 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 ...
0
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1answer
720 views

Are there differences between Delta TF-IDF and TF-IDF?

Are there differences between both algorithm or not ? i mean if i implement for ex Delta TF-IDF in a project instead of TF-IDF...
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2answers
72 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% "...
2
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0answers
84 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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0answers
49 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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1answer
31 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 ...