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1
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2answers
567 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 ...
0
votes
0answers
17 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 ...
0
votes
0answers
14 views

Acronym Resolution Issues

I am trying to work out the resolution of acronyms. Till now I could find two good papers in http://cogprints.org/4399/1/NRC-48078.pdf and ...
4
votes
1answer
154 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 ...
3
votes
0answers
33 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 ...
0
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0answers
19 views

Computing average precision at depth n (AP@n)

I have a binary classification problem, for which I use some kernel SVM (e.g., with the RBF kernel). I apply the trained SVM model to a testing set and I get a list with predictions (labels) along ...
0
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0answers
40 views

How do you compare words or documents using LSA (latent semantic analysis)

As the title says, I am a bit cofunsed in how documents or words are compared using LSA (when I say compare, I am referring to calculate similarities, for instance, cosine similarity). In An ...
0
votes
1answer
31 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 ...
3
votes
1answer
277 views

Difference between Weighted Average Entropy and Adjusted Mutual Information (for evaluating Clustering)

I was advised by my team leader to use this weighted average entropy to evaluating the performance of my dbscan clustering algorithm, and its mathematical formulation is: Scikit provides what many ...
2
votes
0answers
49 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 ...
0
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0answers
8 views

Query Expansion: Combining relevant doc information given a base query

I am working on a search problem. I have a bunch of short queries such as "fuzzy bunny" as well as some short documents which I know to be related / contain these queries. For example, for "fuzzy ...
0
votes
0answers
16 views

Extracting document's keyword after dimensionality reduction

Let's say I have a word document matrix and I applied SVD to this matrix (LSA), and now I have the representation of this matrix in a reduced dimensional space. How could I use this for extracting ...
3
votes
2answers
177 views

Vector Space Model for Online News Clustering

I am trying to automatically cluster news articles based on their content. I need this algorithm to be online and simply group news articles related to the same story as they arrive. The common ...
1
vote
0answers
27 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 ...
1
vote
0answers
19 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 ...
2
votes
1answer
63 views

Explaining the steps of a visualization tool

this is my first post on CrossValidated. I've done, for academic purpose, a web tool doing this process: web scraping from various sites pre-process the responses (cleaning, error and redundance ...
0
votes
0answers
23 views

Measuring efficiency of hierarchical clustering alone

I have designed a model which uses hierarchical clustering to predict user habit, by either stating number (0) or number (1) in a specific context. It uses a three stage approach with 4d data (n ...
0
votes
0answers
34 views

What is the measurement scale of the Reciprocal Rank metric?

The reciprocal rank (RR) metric is often used to measure to what extent ranking approaches are able to return relevant items in the topmost positions of the list of results. It is computed as follows: ...
0
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0answers
179 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 ...
0
votes
0answers
89 views

Information retrieval from strings using neural network

I am dealing with input strings that have max length of 200 characters, they are the USSD popup msgs and the SMS text strings on android smart phones extracted as and when they appear via an android ...
1
vote
2answers
611 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 ...
0
votes
1answer
139 views

Clustering structured data: Assessing the similarity of documents that appear in tree structure

Usually when performing text document clustering, similarities across documents are assessed based on the lexical content of documents. But, in my problem, I wish to consider both the lexical content ...
6
votes
1answer
165 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 ...
1
vote
1answer
97 views

Average precision when not all the relevant documents are found

I can't find on the Internet a proper source that explains this. I have built a search engine that for a particular query retrieves 5 relevant document out of the 10 relevant documents. When I ...
3
votes
0answers
51 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 ...
0
votes
0answers
31 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
73 views

Test statistics in IR (t-test vs Wilcoxon vs signed test)

I have just read this paper about different test statistics one could use in Information Retrieval (IR), discussing whether Wilcoxon, Sign-test or Student t-test would be more reliable for comparing ...
0
votes
1answer
102 views

Calculation of Cosine Similarity using LSA

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?
17
votes
2answers
6k 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 ...
0
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0answers
145 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 ...
0
votes
1answer
29 views

What are distinctive terms?

Here $n$ is the number of distinctive terms in document $d$. What is the meaning of distinctive? My guess is that it's terms that remain after filtering document from terms that aren't necessary, ...
0
votes
0answers
154 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 ...
2
votes
0answers
100 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 ...
0
votes
1answer
52 views

Measure precision

This is used in information retrieval. I have an algorithm that uses a sample set to predict a yes / no. I think the correct term is binomial. Yes this document is about sports or no this document ...
0
votes
2answers
147 views

Correct Evaluation of Random Forest on fixed training/test set

I have to test the performance of Random Forest on the same dataset (text classification) with about 118.000 instances of which about 1/3 is used for training and 2/3 is used for testing. The division ...
2
votes
2answers
104 views

Weighting words based on position in text

I'm currently working on semantic analysis and had a question about text organization and structure. Are there any algorithms, or statistical / machine-learning models that weight the importance of a ...
5
votes
1answer
148 views

In inverse theory, how do I transform the averaging kernel matrix to a new grid?

Rodgers and Connor (2003) describe how measurements by remote sounders can be properly compared, taking into account differences in averaging kernels and error covariances. They make the assumption ...
2
votes
1answer
3k 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 ...
1
vote
0answers
17 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 ...
0
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0answers
20 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 ...
1
vote
1answer
48 views

How do I perform an IDF calculation?

How do I perform an IDF calculation? I am uncertain as to whether IDF should be calculated in per-class level or for the entire document set (that contains multiple classes).
0
votes
0answers
33 views

What are the mathematics I need to learn, before I start research in data mining [duplicate]

I usually use text mining, graph mining, Information retrieval, and natural lanuage processing. Also i will use the fundamental concepts of data mining like classification, association and clustering. ...
1
vote
1answer
2k views

Create a matrix of tf-idf values from documents

I have a set of documents like: D1 = "The sky is blue." D2 = "The sun is bright." D3 = "The sun in the sky is bright." and a ...
3
votes
0answers
288 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 ...
0
votes
1answer
51 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 ...
4
votes
3answers
663 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 ...
2
votes
1answer
510 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
votes
1answer
193 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 ...
1
vote
1answer
37 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 ...
3
votes
2answers
378 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 ...