I have like 200 bibitem entry in the environment
\begin{thebibliography}
\bibitem{Bermudez} Berm\'udez, J.D., J. V. Segura y E. Vercher (2010). \emph{Bayesian forecasting with the Holt-Winters model}. Journal of the Operational Research Society, 61, 164-171.
\begin{thebibliography}
I want the resulting .bib file format
#article{bermudez2010bayesian,
title={Bayesian forecasting with the Holt--Winters model},
author={Berm{\'u}dez, Jos{\'e} D and Segura, Jos{\'e} Vicente and Vercher, Enriqueta},
journal={Journal of the Operational Research Society},
volume={61},
number={1},
pages={164--171},
year={2010},
publisher={Taylor \& Francis}
}
Is there a way I can do it without converting one by one
Regards
One possibility is to use https://text2bib.economics.utoronto.ca/ to convert the \bibitem into bibtex format. Choosing Spanish as language, the output of the conversion is
#article{Bermudez,
author = {Berm\'udez, J. D. and J. V. Segura and E. Vercher},
journal = {Journal of the Operational Research Society},
pages = {164-171},
title = {{B}ayesian forecasting with the Holt-Winters model},
volume = {61},
year = {2010},
}
Some fields are missing, e.g. the publisher, because this information was not contained in your \bibitem
You can use tex2bib, a tool based on text2bib, but migrated to a newest PHP version (PHP 7).
See an example of use
Input of text transformation:
\bibitem{Bermudez} Berm\'udez, J.D., J. V. Segura y E. Vercher (2010). \emph{Bayesian forecasting with the Holt-Winters model}. Journal of the Operational Research Society, 61, 164-171.
Output:
#article{bv10,
author = {Berm\'udez, J. D. and J. V. Segura y E. Vercher},
title = {Bayesian forecasting with the Holt-Winters model},
journal = {Journal of the Operational Research Society},
year = {2010},
volume = {61},
pages = {164-171},
}
Related
I am using BERT model for context search in Italian language but it does not understand the contextual meaning of the sentence and returns wrong result.
in below example code when I compare "milk with chocolate flavour" with two other type of milk and one chocolate so it returns high similarity with chocolate. it should return high similarity with other milks.
can anyone suggest me any improvement on the below code so that it can return semantic results?
Code :
!python -m spacy download it_core_news_lg
!pip install sentence-transformers
import scipy
import numpy as np
from sentence_transformers import models, SentenceTransformer
model = SentenceTransformer('distiluse-base-multilingual-cased') # workes with Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish
corpus = [
"Alpro, Cioccolato bevanda a base di soia 1 ltr", #Alpro, Chocolate soy drink 1 ltr(soya milk)
"Milka cioccolato al latte 100 g", #Milka milk chocolate 100 g
"Danone, HiPRO 25g Proteine gusto cioccolato 330 ml", #Danone, HiPRO 25g Protein chocolate flavor 330 ml(milk with chocolate flabor)
]
corpus_embeddings = model.encode(corpus)
queries = [
'latte al cioccolato', #milk with chocolate flavor,
]
query_embeddings = model.encode(queries)
# Calculate Cosine similarity of query against each sentence i
closest_n = 10
for query, query_embedding in zip(queries, query_embeddings):
distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0]
results = zip(range(len(distances)), distances)
results = sorted(results, key=lambda x: x[1])
print("\n======================\n")
print("Query:", query)
print("\nTop 10 most similar sentences in corpus:")
for idx, distance in results[0:closest_n]:
print(corpus[idx].strip(), "(Score: %.4f)" % (1-distance))
Output :
======================
Query: latte al cioccolato
Top 10 most similar sentences in corpus:
Milka cioccolato al latte 100 g (Score: 0.7714)
Alpro, Cioccolato bevanda a base di soia 1 ltr (Score: 0.5586)
Danone, HiPRO 25g Proteine gusto cioccolato 330 ml (Score: 0.4569)
The problem is not with your code, it is just the insufficient model performance.
There are a few things you can do. First, you can try Universal Sentence Encoder (USE). From my experience their embeddings are a little bit better, at least in English.
Second, you can try a different model, for example sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1. It is based on ROBERTa and might give a better performance.
Now you can combine together embeddings from several models (just by concatenating the representations). In some cases it helps, on expense of much heavier compute.
And finally you can create your own model. It is well known that single language models perform significantly better than multilingual ones. You can follow the guide and train your own Italian model.
I'm new to Stata and have a question about its command language. I want to use my ARIMA model to forecast, ie use x[t], x[t-1]... to produce an estimate xhat[t+1], and then roll forward one time step, to make the next forecast, rebuilding the model every N time steps.
i can duplicate code, something like the following code for T, T+1, T+2, etc.:
arima x if t<=T, arima(2,0,2)
predict xhat
to produce a series of xhats to compare with in-sample x observations. There must be a more natural way to do this in the command language. any suggestions, pointers would be very much appreciated.
Posting a working solution provided by Stata tech support:
webuse dfex
tsset month
generate int id = _n
capture program drop forecarima
program forecarima, rclass
syntax [if]
tempvar yhat
arima unemp `if', arima(1,1,0)
local T = e(tmax)
local T1 = `T' + 1
summarize id if month == `T1'
local h = r(max)
predict `yhat', y dynamic(`T')
return scalar y = unemp[`h']
return scalar yhat = `yhat'[`h']
end
rolling unemp = r(y) unemp_hat = r(yhat), window(400) recursive ///
saving(results,replace): forecarima
use results,clear
browse
this provides output with the prediction and observed both available. the dates are off by one step, but easier left to post-processing.
I would like to predict time series values X using another time series Y and the past value of X.In detail, I would like to predict X at time t (Xt) using (Xt-p,...,Xt-1) and (Yt-p,...,Yt-1,Yt) with p the dimension of the "look back".
So, my problem is that I do not have the same length for my 2 predictors.
Let's use a exemple to be clearer.
If I use a timestep of 2, I would have for one observation :
[(Xt-p,Yt-p),...,(Xt-1,Yt-1),(??,Yt)] as input and Xt as output. I do not know what to use instead of the ??
I understand that mathematically speaking I need to have the same length for my predictors, so I am looking for a value to replace the missing value.
I really do not know if there is a good solution here and if I could to something so any help would be greatly appreciated.
Cheers !
PS : you could see my problem as if I wanted to predict the number of ice cream sell one day in advance in a city using the forcast of weather for the next day. X would be the number of ice cream and Y could be the temperature.
You could e.g. do the following:
input_x = Input(shape=input_shape_x)
input_y = Input(shape=input_shape_y)
lstm_for_x = LSTM(50, return_sequences=False)(input_x)
lstm_for_y = LSTM(50, return_sequences=False)(input_y)
merged = merge([lstm_for_x, lstm_for_y], mode="concat") # for keras < 2.0
merged = Concatenate([lstm_for_x, lstm_for_y])
output = Dense(1)(merged)
model = Model([x_input, y_input], output)
model.compile(..)
model.fit([X, Y], X_next)
Where X is an array of sequences, X_forward is X p-steps ahead and Y is an array of sequences of Ys.
I have a bunch of already human-classified documents in some groups.
Is there a modified version of lda which I can use to train a model and then later classify unknown documents with it?
For what it's worth, LDA as a classifier is going to be fairly weak because it's a generative model, and classification is a discriminative problem. There is a variant of LDA called supervised LDA which uses a more discriminative criterion to form the topics (you can get source for this in various places), and there's also a paper with a max margin formulation that I don't know the status of source-code-wise. I would avoid the Labelled LDA formulation unless you're sure that's what you want, because it makes a strong assumption about the correspondence between topics and categories in the classification problem.
However, it's worth pointing out that none of these methods use the topic model directly to do the classification. Instead, they take documents, and instead of using word-based features use the posterior over the topics (the vector that results from inference for the document) as its feature representation before feeding it to a classifier, usually a Linear SVM. This gets you a topic model based dimensionality reduction, followed by a strong discriminative classifier, which is probably what you're after. This pipeline is available
in most languages using popular toolkits.
You can implement supervised LDA with PyMC that uses Metropolis sampler to learn the latent variables in the following graphical model:
The training corpus consists of 10 movie reviews (5 positive and 5 negative) along with the associated star rating for each document. The star rating is known as a response variable which is a quantity of interest associated with each document. The documents and response variables are modeled jointly in order to find latent topics that will best predict the response variables for future unlabeled documents. For more information, check out the original paper.
Consider the following code:
import pymc as pm
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
train_corpus = ["exploitative and largely devoid of the depth or sophistication ",
"simplistic silly and tedious",
"it's so laddish and juvenile only teenage boys could possibly find it funny",
"it shows that some studios firmly believe that people have lost the ability to think",
"our culture is headed down the toilet with the ferocity of a frozen burrito",
"offers that rare combination of entertainment and education",
"the film provides some great insight",
"this is a film well worth seeing",
"a masterpiece four years in the making",
"offers a breath of the fresh air of true sophistication"]
test_corpus = ["this is a really positive review, great film"]
train_response = np.array([3, 1, 3, 2, 1, 5, 4, 4, 5, 5]) - 3
#LDA parameters
num_features = 1000 #vocabulary size
num_topics = 4 #fixed for LDA
tfidf = TfidfVectorizer(max_features = num_features, max_df=0.95, min_df=0, stop_words = 'english')
#generate tf-idf term-document matrix
A_tfidf_sp = tfidf.fit_transform(train_corpus) #size D x V
print "number of docs: %d" %A_tfidf_sp.shape[0]
print "dictionary size: %d" %A_tfidf_sp.shape[1]
#tf-idf dictionary
tfidf_dict = tfidf.get_feature_names()
K = num_topics # number of topics
V = A_tfidf_sp.shape[1] # number of words
D = A_tfidf_sp.shape[0] # number of documents
data = A_tfidf_sp.toarray()
#Supervised LDA Graphical Model
Wd = [len(doc) for doc in data]
alpha = np.ones(K)
beta = np.ones(V)
theta = pm.Container([pm.CompletedDirichlet("theta_%s" % i, pm.Dirichlet("ptheta_%s" % i, theta=alpha)) for i in range(D)])
phi = pm.Container([pm.CompletedDirichlet("phi_%s" % k, pm.Dirichlet("pphi_%s" % k, theta=beta)) for k in range(K)])
z = pm.Container([pm.Categorical('z_%s' % d, p = theta[d], size=Wd[d], value=np.random.randint(K, size=Wd[d])) for d in range(D)])
#pm.deterministic
def zbar(z=z):
zbar_list = []
for i in range(len(z)):
hist, bin_edges = np.histogram(z[i], bins=K)
zbar_list.append(hist / float(np.sum(hist)))
return pm.Container(zbar_list)
eta = pm.Container([pm.Normal("eta_%s" % k, mu=0, tau=1.0/10**2) for k in range(K)])
y_tau = pm.Gamma("tau", alpha=0.1, beta=0.1)
#pm.deterministic
def y_mu(eta=eta, zbar=zbar):
y_mu_list = []
for i in range(len(zbar)):
y_mu_list.append(np.dot(eta, zbar[i]))
return pm.Container(y_mu_list)
#response likelihood
y = pm.Container([pm.Normal("y_%s" % d, mu=y_mu[d], tau=y_tau, value=train_response[d], observed=True) for d in range(D)])
# cannot use p=phi[z[d][i]] here since phi is an ordinary list while z[d][i] is stochastic
w = pm.Container([pm.Categorical("w_%i_%i" % (d,i), p = pm.Lambda('phi_z_%i_%i' % (d,i), lambda z=z[d][i], phi=phi: phi[z]),
value=data[d][i], observed=True) for d in range(D) for i in range(Wd[d])])
model = pm.Model([theta, phi, z, eta, y, w])
mcmc = pm.MCMC(model)
mcmc.sample(iter=1000, burn=100, thin=2)
#visualize topics
phi0_samples = np.squeeze(mcmc.trace('phi_0')[:])
phi1_samples = np.squeeze(mcmc.trace('phi_1')[:])
phi2_samples = np.squeeze(mcmc.trace('phi_2')[:])
phi3_samples = np.squeeze(mcmc.trace('phi_3')[:])
ax = plt.subplot(221)
plt.bar(np.arange(V), phi0_samples[-1,:])
ax = plt.subplot(222)
plt.bar(np.arange(V), phi1_samples[-1,:])
ax = plt.subplot(223)
plt.bar(np.arange(V), phi2_samples[-1,:])
ax = plt.subplot(224)
plt.bar(np.arange(V), phi3_samples[-1,:])
plt.show()
Given the training data (observed words and response variables), we can learn the global topics (beta) and regression coefficients (eta) for predicting the response variable (Y) in addition to topic proportions for each document (theta).
In order to make predictions of Y given the learned beta and eta, we can define a new model where we do not observe Y and use the previously learned beta and eta to obtain the following result:
Here we predicted a positive review (approx 2 given review rating range of -2 to 2) for the test corpus consisting of one sentence: "this is a really positive review, great film" as shown by the mode of the posterior histogram on the right.
See ipython notebook for a complete implementation.
Yes you can try the Labelled LDA in the stanford parser at
http://nlp.stanford.edu/software/tmt/tmt-0.4/
Consider these references as EXAMPLES :
1)Cahn, R. S.; Ingold, C.; Prelog, V. Specification of Molecular Chirality. Angew. Chem. Int. Ed. 1966, 5, 385-415.
2)Christie, G. H.; Kenner, J. The Molecular Configurations of Polynuclear Aromatic Compounds. J. Chem. Soc., Trans. 1922, 121, 614-620.
3)Kuhn, R. Molekulare Asymmetrie in Stereochemie, 1933, 803.
4)Oki, M. Recent Advances in Atropisomerism. Topics in Stereochemistry 1983, 14, 1-81.
5)Miyashita, A.; Yasuda, A.; Takaya, H.; Toriumi, K.; Ito, T.; Souchi, T.; Noyori, R. Synthesis of 2,2'-bis(diphenylphosphino)-1,1'-binaphthyl (BINAP), an atropisomeric chiral bis(triaryl)phosphine, and its
use in the rhodium(I)-catalyzed asymmetric hydrogenation of α-(acylamino)acrylic acids. J. Am. Chem.
Soc. 1980, 102, 7932-7934.
I have a large number of references ( like mentioned above ), and I want to segregate data from each of those references ( five for this case ) into separate parts such as
1) Name of the author(s),
2) Title of the topic
3) Date/year of publication
4) Pages and
5) Any other information
I want to create a machine learning model which should be capable of learning the formats by itself and get the right meaning/data from the references in to separate parts.The algorithm should be as much efficient as possible.
Question :
What algorithm and what approach should I have to use to implement the above functionality?
Would I be required to use multiple algorithms to create a model for the above scenario?