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Questions tagged [information-retrieval]

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6
votes
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?
4
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0answers
841 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 ...
3
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0answers
72 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 ...
3
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0answers
102 views

Heuristics streaming data matching

I have an index composed by thousands of documents. Slightly modified copies of those documents are sent to my application in small chunks, and I need to check, from those chunks, which document has ...
3
votes
0answers
77 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 ...
3
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0answers
1k 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 ...
3
votes
0answers
432 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, ...
2
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0answers
11 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 ...
2
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0answers
86 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-...
2
votes
0answers
542 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 ...
2
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0answers
2k views

Typical range of values for TFIDF

I am working on a text corpus. Each line contains between 10 and 50 words. There are around 25 000 words in the whole text and 1 000 000 lines. I turned this corpus into its tf-idf representation. I ...
2
votes
0answers
140 views

Search in a graph of medical records

I have a hierarchical order of a disease list. Just for example: Respiratory system disease 1.1. Asphyxia neonatorum 1.2. Croup 1.3. Lower respiratory tract disease 1.4. Bronchial disease 1....
2
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0answers
120 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 ...
1
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0answers
18 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'...
1
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0answers
47 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 ...
1
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0answers
37 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 ...
1
vote
0answers
80 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 ...
1
vote
0answers
359 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 ...
1
vote
0answers
100 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: ...
1
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0answers
125 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 ...
1
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0answers
422 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% ...
1
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0answers
111 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 ...
1
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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 ...
1
vote
0answers
50 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 ...
1
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0answers
225 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:...
1
vote
0answers
30 views

Information retrieval performance measures for unknown test collection?

I am evaluating a web search relevance feedback algorithm. The algorithm uses Bing API as source for its result sets. To evaluate the algorithm I will be conducting a user study. In the end I will ...
1
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0answers
129 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 ...
1
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0answers
126 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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0answers
3 views

Evalution Metric for Recommender with one Relevant Document

Suppose I have a bunch of user session data. For each user session, 5 rows are created. Each row contains the user id, item id and whether or not they selected that item. For example : ...
0
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0answers
22 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 ...
0
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0answers
11 views

Metric for ranked keyword identification

I am trying to determine which metric(s) to use to evaluate the "coverage" of several lexicons (lists of words) with respect to a ranked list of significant keywords I have extracted from two ...
0
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0answers
10 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. ...
0
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0answers
18 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/...
0
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0answers
150 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 ...
0
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0answers
22 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 ...
0
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0answers
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, ...
0
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0answers
21 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 ...
0
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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 ...
0
votes
2answers
432 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 ...
0
votes
0answers
94 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 ...
0
votes
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 ...
0
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0answers
273 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,...
0
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0answers
62 views

An IR evalualuation metric that only measures the rank of results?

I am working on a little text clustering problem, and trying to figure out how to evaluate the results. I came up with the following idea that I though fits pretty well with the specifics of the ...
0
votes
0answers
282 views

Is precision in recommender system related to mean average error (MAE)?

A recommender system is being evaluated while increasing the neighborhood size. The highest precision was achieved between 10-15 neighbors(users) while the lowest MAE was in the range from 30-40 users....
0
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0answers
25 views

Evaluation of IR approach without test collections

I am trying to evaluate an information retrieval approach. More specifically, it's a query expansion algorithm, based on topic distribution in retrieved documents. I want to evaluate my approach ...
0
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0answers
96 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 ...
0
votes
1answer
145 views

How to compute k-means on provided inverted index using tf-idf

I built an inverted index to represent the following sample documents: ...