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Questions tagged [conditional-random-field]

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1answer
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Introduction to Conditional random fields

I came across the application of a conditional random field (CRF) to the output from a convolutional neural network (CNN) for image segmentation. The additional CRF step seems to be a common ...
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0answers
276 views

loss function in CRF keras-contrib returns Nan in join mode

I use a BiLSTM-CRF architecture to assign some labels to a sequence of the sentences in a paper. We have 150 papers each of which contains 380 sentences and each sentence is represented by a double ...
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40 views

Difference between sentence log-likelihood objective in Collobert at al. 2011 and the CRF objective function?

I'm a bit confused while trying to understand what is the difference between the sentence log-likelihood objective described in "Natural Language Processing (Almost) from Scratch" (Collobert at al. ...
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0answers
33 views

Sequential Prediction: Data Modeling and Classical Algorithms

I have data that can be called demographic data. Raw data Person 0001 \begin{array}{|c|c|} \hline Feb\,1981- Apr\,85 & engaged\,\,in\,\,\underline{activity}\,\,\textit{A}\,\,of \,\,\underline{...
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1answer
26 views

Does Alphabet Case Matters while Training Stanford Open NLP NER classifier?

I'm working with Named Entity Recognition for non-English. I've some raw text file (all small letter) and trying to make NER classifier. I'm not sure If it'll be better using Small Capital mixed text ...
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26 views

Conditional Random Field for Hyperspectral Image Classification

How can I separate training and test sets in a hyperspectral image and apply Conditional Random Field (CRF) for pixel classification? If I choose pixels randomly, some of the neighboring pixels of a ...
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0answers
64 views

How to manually assist CRF prediction in sklearn?

I am using sklearn-crfsuite to parse sequences of tokens. Is there any way to assist the model with the predicted labels? Considering the labels: A, ...
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0answers
20 views

Message passing vs alpha expansion

I am studying graphs but do not understand of the difference between the message passing algorithm and the alpha expansion. I know that in graph problems you have a graph with some labels and you want ...
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0answers
77 views

LSTM with only one node in the hidden layers

I have a regression problem where I'd like to train an LSTM to tackle it. However, since I do not have too many samples for training (only 2000), I am thinking about using only one hidden node, since ...
1
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1answer
214 views

NER at sentence level or document level?

Should NER models (LSTM or CRF) take input training data at sentence level or paragraph level? Let's say we have this input text, and we would like to do Named Entity Extraction: GOP Sen. Rand ...
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0answers
27 views

Noisy conditional simulation

A conditional random field $Z_C(x)$ is a random field whose realisations $z_C(x)$ always take the same values $z_C(x_a)$ at locations $x_a$. Realisations of $Z_C(x)$ can be produced as follows (...
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0answers
371 views

HMM and CRF: the label bias problem and I-equivalence

I have a question about the label bias problem in HMM and CRF. I understand that HMM and MEMM suffer from the label bias problem, which has preference over states with fewer transitions. The problem ...
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20 views

Options for spatially correlated outputs (i.e. structured regression)?

I have a convolutional neural network which takes a 40x40 real valued input, and maps it to a real valued 40x40 output. I've optimized the number of convolution layers, filter size, hidden layer size, ...
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0answers
1k views

CRF (TensorFlow) log-likelihood becomes positive

I am currently doing a multiclass classification task on sequence data and am using tf.contrib.crf.crf_log_likelihood to compute sentence level log-likelihood values. In particular it implements a ...
6
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2answers
411 views

Python package that allows to train a CRF on two datasets

I am looking for a Python package that allows to train a conditional random field (CRF) on two datasets. For example: I have two datasets, dataset A and dataset B. I want to train a conditional ...
0
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1answer
623 views

derivation of partition function in conditional random fields

When reading the paper of Efficient piecewise training of deep structured models for semantic segmentation, I am confused about the derivation in CRF training (section 6). In specific, I do not know ...
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0answers
31 views

Including document level information in sequence tagging

Typically sequence taggers (specifically, linear-chain CRFs) in NLP use sentence level information, i.e., for each word, we define feature functions that only depend on the tokens in the sentence. I ...
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0answers
31 views

Why should the last tag be fixed in CRF?

In the tutorial, it is said that: Let v range over the tags. Define U(k, v) to be the score of the best sequence of tags from 1 to k, where tag k is required to be v. This is a maximization ...
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62 views

Taking forever to converge

I'm using the HCRF Library for a sequence prediction task . For learning the model parameters, I'm using LBFGS, although there are options to use CG and BFGS. I notice that it take an awfully long ...
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2answers
975 views

How can I implement a CRF feature function?

It is said that a feature function can represent anything, the first or the last word of a sentence, a capital character and etc. But how exactly can I represent them in such a form: $F_j(x, y)$ or $\...
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0answers
75 views

CRF message passing as convolution operation

I was reading this particular paper : https://www.robots.ox.ac.uk/~vgg/rg/papers/kraehenbuehl__nips2012__densecrf.pdf , and I didn't understand this equation (eq 5) in the paper: I understand the ...
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0answers
43 views

What is the connection between CRF and Logistics Regression? [duplicate]

Logistic regression seems to be a simple version of Conditional Random field, I am unable to figure out how. Perhaps my fundamentals are shaky.
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1answer
185 views

Fast Gaussian Filtering Using a Permutohedral Lattice

I'm trying to implement http://www.dabi.temple.edu/~zoran/papers/KostaAAAI13.pdf but am stack at understanding equations 10) and 11). They claim that the sum of the Gauss kernel multiplied with the ...
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0answers
460 views

Deep Learning vs Structured Learning

I am interested in the differences between using large, deep learning networks vs Probabilistic graphical models (PGMs), like Random Field models, for structured learning (e.g. on images, or labels of ...
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1answer
1k views

CRF equivalent in deep learning

Conditional Random Fields (CRFs) is a typical solution for a sequence labelling/segmentation problem. For example, a sequence is a string and CRFs are used to label each word as being a part of a ...
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0answers
21 views

Using Conditional random field for many valued labels

I want to use the CRF for labeling a corpus of annotated text. Each word in the corpus has its own set of labels. More specifically, the labels are the pronunciations of each word: some words like "...
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0answers
446 views

unsupervised methods for conditional random fields (CRF)

can any one explain that why CRFs are not applicable for unsupervised learning? thanks in advance
2
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1answer
527 views

A Question on forward-backward algorithm in CRF++

I’m a beginner of CRF++. A question have plagued me for many days. I’m exhausted~ Why the code in calcBeta() is so like ...
2
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1answer
243 views

Is there any advantage of using MEMM instead of CRF for named-entity recognition?

I wonder whether there is any advantage of using maximum-entropy Markov model (MEMM), a.k.a. conditional Markov model (CMM) instead of using conditional random fields (CRF) for named-entity ...
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0answers
329 views

Stacked Conditional Random Field (CRF) implementation

I am looking for a library that can train a stacked conditional random field (CRF). I plan to use it for natural language processing purposes. Ideally, Python interface, works on Linux, and multi-...
2
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1answer
422 views

CRF implementation with Python API that allows a CRF model to be trained multithreadedly

I am looking for a conditional random field (CRF) implementation with a Python API that allows a CRF model to be trained multithreadedly. I currently use pyCRFsuite, which works great except that CRF ...
6
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1answer
524 views

Sequence length when training a conditional random field (CRF)

I am training a conditional random field (CRF) to perform named entity recognition. I have 1000 documents, each containing from 100 to 500 sentences. During the training phase, is it better to train ...
3
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0answers
138 views

What means random field in “Conditional Random Field” [closed]

I have studied about CRF but the popular papers and tutorials have not describe the philosophy of CRF. I see the terms "Field", "Random Field" and "Conditional" from terminological aspect. But the ...
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0answers
30 views

Sequence tagging with additional structure

I am looking for pointers (papers, algorithms etc) for learning models for sequence tagging but which allow for additional structure. Consider Part of Speech Tagging, I could train a CRF which would ...
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0answers
72 views

Compute partition function

I'm given a distribution on 3 discrete variables $x, y, z$ which is defined as $$p(x, y, z) = \frac{1}{Z} \psi(x, y, z) = \frac{1}{Z} \phi_1(x, y) \phi_2(y, z)$$ where $x, y$ can take up value among ...
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1answer
43 views

How can I solve this graphical model?

I have a classification problem, with the following structure. There is a fully-connected graph, and each node needs be assigned a class label. Every pair of nodes in the graph has a probability ...
2
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1answer
624 views

What is the number of features in CRFsuite / python-crfsuite? [closed]

I wonder what the number of features is in python-crfsuite . I thought that the number of features was the number of attributes multiplied by the number of labels, e.g.: ...
2
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1answer
555 views

Generative-Discriminative pairs: Naive Bayes and Logistic Regression

I'm trying to understand the something written in this paper. At the bottom of page 7: This means that if the naive Bayes model $$ p(y,\mathbf{x}) = p(y) \prod_k p(x_k|y) $$ is trained to ...
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0answers
195 views

Is every Conditional Random Field simply a Markov Random Field with restricted structure?

If I have a graph $H$ with nodes $\mathbf{X} \cup \mathbf{Y}$, and a set of factors $\phi_1(D_1), \ldots, \phi_k(D_k)$, where for each $i$, $D_i \not\subset X$, then doesn't this define both a MRF and ...
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0answers
928 views

Difference between a generative MRF and discriminative CRF

I am having trouble developing the intuition behind the difference between a regular generative Markov random field (MRF) and its discriminative counterpart. So, as I think I have understood so far ...
1
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1answer
583 views

CRF Training: Max-margin vs max-likelihood

I'm trying to use PyStruct's CRF implementation. In its user guide, it says the following: I call these models Conditional Random Fields (CRFs), but this a slight abuse of notation, as PyStruct ...
2
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1answer
592 views

Maximum Likelihood Estimation for Conditional Random Field parameters

I have a custom potential function for a Conditional Random Field (CRF) very similar to Fei Fei Li's work. In this work, the parameter learning is done by Maximum Likelihood Estimation. I would like ...
3
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1answer
1k views

What's meaning of BOS and EOS in CRFSuite feature list and what is the role of them?

In NER(Named Entity Recognition) example in python-crf package website we see this function as feature generator: ...
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0answers
101 views

How is prior knowledge of letter/word patterns incorporated into handwriting (or speech) recognition?

Using handwriting recognition as an example, we can train various models to recognise individual characters but to actually be useful we must incorporate prior knowledge of common character sequences, ...
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1answer
2k views
3
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1answer
794 views

Interpreting crfsuite output model for numerical features

I am using crfsuite-python to implement a linear chain CRF in which I would like to use numerical features rather than strings as is the case with the standard CRF application parts of speech tagging. ...
2
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0answers
85 views

Confidence/credible intervals for parameter estimates from structured support vector machine

I am estimating parameters for a conditional random field using a structured support vector machine. The data consists of a flat graph of $i%$ city blocks, where $y_i$ is the assignment of the the $...
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3answers
17k views

Intuitive difference between hidden Markov models and conditional random fields

I understand that HMMs (Hidden Markov Models) are generative models, and CRF are discriminative models. I also understand how CRFs (Conditional Random Fields) are designed and used. What I do not ...
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4answers
28k views

Implementation of CRF in python

Is there a popular implementation of Conditional Random Fields in Python? I can't seem to find any that is widely used and popular!
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0answers
366 views

HMM ever better than CRF?

For classifying a sequence of instances, are there any specific circumstances that make Hidden Markov Models (HMMs) more accurate than Conditional Random Fields (CRFs)? I have seen several papers ...