trigram

This is a table of type trigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.

trigram	frequency
the number of	108
in order to	77
in information retrieval	63
in this paper	61
we use the	59
as well as	56
based on the	54
a set of	50
learning to rank	47
in terms of	46
with respect to	46
information retrieval doi	43
advances in information	43
the set of	43
the performance of	42
in this work	38
one of the	36
on the other	34
in this section	33
overview of the	29
the effectiveness of	29
a number of	29
the other hand	28
for information retrieval	28
the results of	28
the importance of	27
we propose a	26
to predict the	25
due to the	25
shown in table	25
in addition to	24
used in the	23
to the query	23
the quality of	23
can be used	23
of a document	22
is used to	22
according to the	21
part of the	21
each of the	20
we do not	20
be used to	19
convolutional neural networks	19
a sequence of	19
of the query	19
respect to the	19
in the following	19
related to the	18
of a review	18
the value of	18
as shown in	18
in the document	18
in the same	18
similar to the	17
large number of	17
which is the	17
we focus on	17
the task of	17
the local context	17
to the best	17
of seed words	17
in our experiments	17
to evaluate the	17
query terms in	17
the use of	17
to capture the	17
of the proposed	17
in the training	17
state of the	17
the best of	16
in the case	16
best of our	16
propose a novel	16
the training set	16
the impact of	16
the context of	16
a collection of	16
most of the	16
the proposed model	16
to address the	16
the case of	16
in social media	16
of the text	16
a subset of	16
is defined as	16
the test set	16
of our knowledge	16
it can be	16
based on a	16
online learning to	16
distributed representations of	15
representation of the	15
the problem of	15
quality of the	15
of information retrieval	15
is the number	15
of query terms	15
neural networks for	15
query and document	15
the embedding of	15
for each of	15
is based on	15
text and image	15
that can be	15
to compute the	15
the clef ehealth	15
with the same	15
in the original	15
it does not	14
of the task	14
of the model	14
we want to	14
we introduce the	14
the effect of	14
performance of the	14
performance of our	14
the result list	14
score of a	14
our proposed model	14
in the context	14
product attention layer	14
we use a	14
results show that	14
to learn the	14
of the clef	14
along with the	13
of the documents	13
terms in the	13
well as the	13
the seed words	13
the concatenation of	13
table shows the	13
goal is to	13
sequential question answering	13
be able to	13
shown in fig	13
in a document	13
of the review	13
which can be	13
such as the	13
as a result	13
generated schema labels	13
user and item	13
of our model	13
of the document	13
aspects of the	13
there is a	13
have the same	13
a list of	12
a document is	12
parameters of the	12
the product attention	12
is able to	12
likely to be	12
of this paper	12
evaluate the performance	12
of the first	12
the evaluation of	12
show that the	12
relevant to the	12
similarity between the	12
a variety of	12
of schema labels	12
in natural language	12
of the art	12
have been proposed	12
a large number	12
refers to the	12
the embedding space	12
we assume that	12
cold start scenario	12
the model is	12
it is not	12
sessions and tasks	12
the proposed approach	12
this is a	12
the end of	12
are shown in	12
the fact that	12
queries and documents	12
of candidate rankers	12
we report the	11
the training data	11
the parameters of	11
fang et al	11
of the original	11
we evaluate the	11
of a domain	11
to this end	11
of a query	11
the support of	11
between query and	11
clef ehealth evaluation	11
the sd c	11
of words and	11
in social networks	11
results of the	11
to estimate the	11
words and phrases	11
in the first	11
for a given	11
this paper we	11
in the top	11
identification and verification	11
we observe that	11
the contact recommendation	11
local context of	11
more likely to	11
each query term	11
to the user	11
warm start scenario	11
some of the	11
of the same	11
show that our	11
at least one	11
we used the	11
total number of	11
the review embedding	11
information from the	11
can be seen	11
to a query	11
results of our	11
is similar to	11
documents in the	10
consists of a	10
of the paper	10
at the end	10
the attention weights	10
words in the	10
representations of words	10
the text and	10
of the evaluation	10
on social media	10
the role of	10
the form of	10
the representation of	10
in the network	10
during text preprocessing	10
we need to	10
we found that	10
schema label generation	10
based on their	10
the cold start	10
the probability that	10
to explore the	10
for future work	10
of the generators	10
the semantics of	10
the difficulty of	10
compared to the	10
of our proposed	10
we describe the	10
in the second	10
we present a	10
a query claim	10
in the non	10
number of documents	10
scores of the	10
focus on the	10
analysis of the	10
the lack of	10
we compare our	10
note that the	10
we propose to	10
as a single	10
verification of claims	10
described in sect	10
and verification of	10
provided by the	10
of the data	10
fan et al	10
we can see	10
dataless text classification	10
at test time	10
title and description	10
document network embedding	10
the total number	10
we refer to	10
are used to	10
we compute the	10
embedding of the	10
input to the	10
the original paper	10
the missing views	10
we aim to	10
in the form	10
importance of the	9
to make the	9
for the task	9
illustrated in fig	9
disjunctively written languages	9
supported by the	9
sentence s i	9
sentiment attention layer	9
in comparison to	9
the length of	9
the cosine similarity	9
number of candidate	9
are able to	9
the query and	9
within the same	9
can be considered	9
a method for	9
ehealth evaluation lab	9
introduced in sect	9
for keyphrase extraction	9
model is trained	9
corresponds to the	9
we consider the	9
the candidate user	9
a ranked list	9
can be found	9
word and topic	9
statistics of the	9
sum of the	9
based query sequences	9
in previous work	9
the relevance of	9
proceedings of the	9
posting lists in	9
additional supporting information	9
the same as	9
are used as	9
trained on the	9
one or more	9
the document is	9
stepwise recipe dataset	9
that our model	9
of documents in	9
referred to as	9
bantu language dataset	9
score of the	9
description of the	9
semantics of the	9
found to be	9
automatic identification and	9
the choice of	9
number of words	9
depend on the	9
early risk prediction	9
an overview of	9
and it is	9
of social media	9
in our case	9
for each query	9
a chemical reaction	9
in which the	9
each of these	9
in the embedding	9
is set to	8
are interested in	8
from the same	8
the remainder of	8
factoid question answering	8
contact recommendation task	8
of the two	8
of the image	8
better than the	8
early detection of	8
the probability of	8
a total of	8
in the next	8
of the individual	8
is used for	8
by the authors	8
method for stochastic	8
of the words	8
in this case	8
addition to the	8
weights of the	8
we present the	8
we also report	8
the semantic similarity	8
of the dataset	8
of the neural	8
is used as	8
used in our	8
are more likely	8
to provide a	8
and the number	8
the sentiment attention	8
the results show	8
we propose an	8
of query term	8
used as the	8
to the original	8
contribute to the	8
the neighbors of	8
there are many	8
the warm start	8
on the internet	8
importance of each	8
each posting list	8
of the word	8
the goal is	8
this work is	8
and their compositionality	8
the word embedding	8
novelty and diversity	8
in the graph	8
the idea of	8
this work was	8
is the set	8
this type of	8
irony detection in	8
terms of the	8
the first one	8
relevance score of	8
from the text	8
the scoring function	8
described in the	8
of recommender systems	8
there is no	8
dmp common standards	8
for this task	8
and phrases and	8
as an example	8
and document terms	8
to represent the	8
to address this	8
be found in	8
phrases and their	8
top k results	8
for all the	8
the relevance score	8
word embeddings and	8
named entity recognition	8
zhang et al	8
the combination of	8
our proposed approach	8
i plan to	8
to the following	8
the application of	8
context of query	8
the most relevant	8
of all the	8
the target user	8
the readability of	8
can also be	8
the knowledge graph	8
the presence of	8
of cond gans	8
the ability of	8
effectiveness of the	8
evaluation of the	8
divergence from randomness	8
latent dirichlet allocation	8
of the system	8
is associated with	8
the support scores	8
to identify the	8
we plan to	8
the weights of	8
the transfer layer	8
the results for	8
the top k	8
the definition of	8
the user and	8
may not be	8
the rest of	8
the current state	7
the objective function	7
in the field	7
are based on	7
respect to a	7
with the corresponding	7
from the training	7
this can be	7
to information retrieval	7
an attention mechanism	7
and yelp data	7
sentiment and product	7
a query term	7
using word embeddings	7
global vectors for	7
fed into a	7
relevance scores of	7
significantly better than	7
to be a	7
s and s	7
as opposed to	7
the differences in	7
it has been	7
to understand the	7
the need to	7
is different from	7
ranked list of	7
version of the	7
to the same	7
this means that	7
severity analysis module	7
length of the	7
because of the	7
on top of	7
be used in	7
of a claim	7
for a query	7
training of deep	7
of the bias	7
on the web	7
knowledge graph embedding	7
the field of	7
of deep bidirectional	7
our results show	7
be seen as	7
in the collection	7
to generate a	7
in other words	7
the paper and	7
of the attention	7
the average of	7
in web search	7
ding and suel	7
the jth sentence	7
verbs and adjectives	7
nodes in a	7
would like to	7
the scores of	7
is not available	7
which is a	7
hidden state of	7
for stochastic optimization	7
of posting lists	7
there has been	7
bidirectional transformers for	7
the location mentions	7
models have been	7
have access to	7
n is the	7
as described in	7
and a set	7
the amazon data	7
of the retrieved	7
for language understanding	7
cosine similarity between	7
deep neural networks	7
defined in eq	7
lists in the	7
not able to	7
representation of a	7
the system is	7
shown in the	7
that the user	7
we believe that	7
deep bidirectional transformers	7
we are interested	7
of signs of	7
the severity of	7
word embeddings of	7
as part of	7
we can use	7
of the reviews	7
in our model	7
the neural network	7
natural language processing	7
there are no	7
the level of	7
query term q	7
the learning rate	7
in the cold	7
between the query	7
level attention mechanism	7
to generate the	7
propose a new	7
small number of	7
attention mechanism to	7
of the training	7
the accuracy of	7
is denoted by	7
to assess the	7
of training data	7
between query terms	7
to be able	7
a dataset of	7
as input to	7
this is the	7
label mixed ranking	7
the standard pipeline	7
to learn a	7
schema labels and	7
information retrieval models	7
of a term	7
discussed in sect	7
is relevant to	7
for computing the	7
in the corpus	7
the training process	7
end of the	7
graph convolutional networks	7
a schema label	7
rest of the	7
a knowledge base	7
the target and	7
document language models	7
for a specific	7
for each task	7
from social media	7
convolutional neural network	7
the analysis of	7
schema label mixed	7
our model is	7
the bias goggles	7
embeddings of the	7
transformers for language	7
by the model	7
the negation morpheme	7
we hypothesize that	7
the data table	7
a series of	7
different from the	7
the same time	7
amazon data set	7
into account the	7
we use an	7
and topic vectors	7
of medical articles	7
which we use	7
and test sets	7
the full text	7
the dual approach	7
in the warm	7
such as bm	7
semantic similarity between	7
view of the	7
neural machine translation	7
of the target	7
neural information retrieval	7
models such as	7
the similarity between	7
the sum of	7
the size of	7
similarity of the	7
used for the	7
in all cases	7
domain suggestion mining	7
provided in the	7
we set the	7
of sentences and	7
in the previous	7
recurrent neural networks	7
the work of	7
of a word	6
and the second	6
document is relevant	6
performance on the	6
language modeling framework	6
of the embeddings	6
vectors for word	6
is not a	6
g and g	6
the severity analysis	6
by considering the	6
neighbors of the	6
do not consider	6
of each word	6
model based on	6
is important to	6
q and c	6
should abandon fossil	6
is computed as	6
abandon fossil fuels	6
to the overall	6
to the task	6
a gating mechanism	6
is the same	6
take advantage of	6
and sd c	6
review embedding is	6
a social network	6
improvements in the	6
we also use	6
i is the	6
for our experiments	6
depends on the	6
term q i	6
learning for image	6
advantage of the	6
for word representation	6
model with the	6
better results than	6
conjunctively written languages	6
cases where the	6
depending on the	6
the regression model	6
ij is the	6
community question answering	6
is treated as	6
labs of the	6
the requirement set	6
matching between query	6
we make the	6
the same document	6
in the dataset	6
of the results	6
the words in	6
the learned representations	6
for each word	6
to determine the	6
such that the	6
the hidden state	6
in the paper	6
the case for	6
included in the	6
sd c methods	6
to a single	6
the weight of	6
has been shown	6
that it is	6
in a graph	6
has led to	6
are described in	6
ability of the	6
medium and long	6
consider the following	6
sd c models	6
amount of data	6
the review score	6
as illustrated in	6
seq seq retrieval	6
word embeddings for	6
of schema label	6
we compare the	6
results on a	6
the existence of	6
to better understand	6
defined as follows	6
the word vectors	6
the addition of	6
retrieval based on	6
access to a	6
this paper is	6
the document representations	6
associated with a	6
in a single	6
the development of	6
have focused on	6
amazon and yelp	6
the case where	6
should not be	6
reported in table	6
of the network	6
in which we	6
and sentiment analysis	6
the outputs of	6
to the fact	6
a learning rate	6
the most important	6
we see that	6
computation of the	6
body of work	6
the query terms	6
a review of	6
the frequency of	6
mixed bantu language	6
we showed that	6
to have a	6
do not have	6
could be used	6
the schema label	6
inductive document network	6
impact on the	6
the difference between	6
is the first	6
together with the	6
from the query	6
semantic matching between	6
is that the	6
constraints in the	6
biomedical question answering	6
detection of signs	6
the word level	6
for each dataset	6
take into account	6
in most cases	6
the distribution of	6
distribution of the	6
of query and	6
model can be	6
as in the	6
any of the	6
of a chemical	6
the objective of	6
information retrieval systems	6
for bantu languages	6
stochastic gradient descent	6
the query term	6
integer linear programming	6
for text classification	6
difference between the	6
it is important	6
we have a	6
during the training	6
subset of the	6
yelp data sets	6
bias score of	6
conference and labs	6
for the evaluation	6
to the embedding	6
at the same	6
the design of	6
the system to	6
of the search	6
effectiveness of our	6
the structure of	6
we denote by	6
of the item	6
in our study	6
schema labels can	6
refer to the	6
the web graph	6
the process of	6
support scores of	6
the goal of	6
need to be	6
this is because	6
in the experiments	6
the output of	6
the amount of	6
from a single	6
that there are	6
we introduce a	6
we are able	6
to demonstrate the	6
social media posts	6
similarity between query	6
images and videos	6
indicates that the	6
influence of the	6
the embeddings of	6
the task is	6
of users and	6
the ir models	6
for the contact	6
in the text	6
large amount of	6
the type of	6
the dmp common	6
g r q	6
a document to	6
the word embeddings	6
representations of schema	6
diversity and novelty	6
target user u	6
average of the	6
vector space model	6
which has been	6
the results in	6
for contact recommendation	6
of the previous	6
in the past	6
the domain of	6
and labs of	6
results on the	6
in part by	6
li et al	6
those of the	6
the approximated review	6
outputs of the	6
we find that	6
close to the	6
is important for	6
in line with	6
use of the	6
of the sentence	6
for each node	6
at each interaction	5
location mentions in	5
we show the	5
the system can	5
the next section	5
convolutional networks for	5
a baseline system	5
to study the	5
the most recent	5
the same type	5
logged bandit feedback	5
the similarity of	5
this kind of	5
that are semantically	5
of our approach	5
for sentence classification	5
dataset retrieval task	5
is available at	5
of the checkthat	5
even though the	5
mean average precision	5
and warm start	5
the algorithm is	5
w is the	5
to classify the	5
semantically related to	5
it is common	5
set of candidate	5
on the full	5
p g and	5
corresponds to a	5
useful information from	5
value of the	5
in the performance	5
to the results	5
text retrieval conference	5
by providing a	5
the statistics of	5
standard insights extraction	5
from the web	5
of text passages	5
support score of	5
in contact recommendation	5
results in the	5
a part of	5
to learn how	5
the proposed system	5
at prediction time	5
mentions in tweets	5
in our work	5
on the quality	5
that the proposed	5
in the remainder	5
to tackle the	5
results are shown	5
we study the	5
by fang et	5
are reported in	5
we would like	5
represented by the	5
on automatic identification	5
from the corpus	5
remainder of the	5
use the same	5
in future work	5
multifield document ranking	5
semantic similarity of	5
to analyze the	5
been extensively used	5
graph neural networks	5
our work is	5
models based on	5
generalized language model	5
claims as well	5
value for k	5
in a way	5
from other sentences	5
terms of both	5
listed in table	5
there is an	5
resulting in a	5
theoretical analysis of	5
treated as a	5
content of the	5
contrary to the	5
a variant of	5
a training set	5
the same meaning	5
the features of	5
experimental results on	5
by using the	5
words per sentence	5
of our experiments	5
set of queries	5
the results obtained	5
is possible to	5
be interested in	5
in the result	5
information retrieval and	5
is equivalent to	5
representations of the	5
report the results	5
the mixed bantu	5
for sentiment analysis	5
to define the	5
a small number	5
specific sentiment lexicon	5
sentences in the	5
allows users to	5
wachsmuth et al	5
the subset of	5
to optimize the	5
from the trec	5
documents with respect	5
model for the	5
is a very	5
the higher the	5
to evaluate our	5
to extend the	5
the real world	5
is trained to	5
on the same	5
the explicit features	5
used to extract	5
this problem by	5
canonical correlation analysis	5
features such as	5
and can be	5
the vrss output	5
the input space	5
is applied to	5
the overall sentiment	5
the existing lexicon	5
using a set	5
will be used	5
present in the	5
visual representation of	5
of the test	5
of web domains	5
dimension of the	5
to answer rq	5
the counterfactual evaluation	5
commonly used in	5
a study of	5
latent semantic analysis	5
is to learn	5
we should abandon	5
q and q	5
lab on automatic	5
a result of	5
improve the performance	5
is the most	5
the usage of	5
able to reproduce	5
that we use	5
neural ranking models	5
the learning of	5
trotman et al	5
insights extraction pipeline	5
a way to	5
to investigate the	5
a deep relevance	5
by the user	5
used to predict	5
of each query	5
we have proposed	5
work was supported	5
the evaluation forum	5
generative adversarial networks	5
to obtain the	5
in the final	5
node in the	5
users and items	5
a sample of	5
to find the	5
negative matrix factorization	5
of text and	5
to improve the	5
in the image	5
of the models	5
the assessment of	5
it is a	5
and item embeddings	5
during topic modeling	5
the three languages	5
hierarchical attention network	5
partially supported by	5
it is possible	5
be used for	5
in a network	5
of graded disease	5
cruzado and castells	5
one of its	5
to the local	5
the generated schema	5
in the entire	5
we provide a	5
the latent topic	5
a large corpus	5
reviews in the	5
results from the	5
and a symptom	5
a mixture of	5
number of neighbors	5
for readability analysis	5
of von mises	5
prediction on the	5
q i in	5
that we can	5
and use the	5
train and test	5
to do so	5
the base model	5
the results are	5
c i and	5
a survey of	5
weighted sum of	5
they can be	5
recurrent neural network	5
social media platforms	5
review helpfulness prediction	5
to select a	5
a document as	5
the bias score	5
relevant to a	5
in the figure	5
corresponding to the	5
the degree of	5
context of a	5
of the product	5
from which we	5
results in table	5
history of writings	5
a review text	5
can see that	5
formulated as a	5
is shown in	5
to ensure that	5
in cases where	5
multimodal deep learning	5
for web search	5
approximated review embedding	5
posting lists are	5
risk prediction on	5
used in previous	5
the same cluster	5
the most similar	5
our model can	5
social media data	5
in bold font	5
the ir axioms	5
million pmc articles	5
by the system	5
are likely to	5
see that the	5
the stances of	5
information about the	5
length in the	5
explained in sect	5
the attention mechanism	5
the category ac	5
to combine the	5
i in the	5
followed by a	5
with the query	5
lingual information retrieval	5
a probability distribution	5
outperforms the other	5
from multiple modalities	5
information retrieval evaluation	5
there are several	5
we have presented	5
and product information	5
structure of the	5
chemical reactions from	5
concatenation of all	5
compare our model	5
and candidate users	5
used to train	5
the difference is	5
target and candidate	5
as explained in	5
table reports the	5
of dataset retrieval	5
from the original	5
when they are	5
obtained from the	5
part of a	5
query expansion using	5
alam et al	5
the focus of	5
result of the	5
the issue of	5
discounted cumulative gain	5
over chemical reactions	5
the standard insights	5
of the article	5
number of queries	5
of the authors	5
yelp data set	5
it as a	5
a preliminary evaluation	5
the selection of	5
to the other	5
capitalism and war	5
of this work	5
is to retrieve	5
it is also	5
the state of	5
a dataset for	5
of its views	5
set of seed	5
of retrieval models	5
assume that the	5
to score the	5
aspects of bias	5
nature of the	5
the fast retrieval	5
the result of	5
in an unsupervised	5
our model with	5
to the question	5
and how the	5
of the candidate	5
as compared to	5
supporting and attacking	5
large body of	5
information from different	5
accuracy of the	5
sentences and documents	5
seed words is	5
the relation between	5
with a new	5
the majority of	5
processed by the	5
of the other	5
for each user	5
the cooccur method	5
to a specific	5
the target product	5
the absence of	5
the score function	5
model on the	5
to each query	5
item and user	5
figure shows the	5
to be close	5
the influence of	5
text and images	5
in the local	5
by one of	5
to enhance the	5
can be interpreted	5
results in a	5
the next step	5
are used for	5
early risk detection	5
for early risk	5
for schema label	5
and does not	5
d is the	5
the original pagerank	5
and show that	5
the original query	5
more than one	5
in the set	5
the probability distribution	5
also report the	5
and they are	5
the readability analysis	5
k is the	5
task as a	5
comparison to the	5
networks for sentence	5
performance of retrieval	5
of each sentence	5
conditionally on the	5
in the input	5
are not available	5
fully connected layer	5
effectiveness and efficiency	5
the value function	5
the golden domains	5
word embedding models	5
of the user	5
counterfactual learning to	5
assigned by the	5
in of the	5
can lead to	5
based on word	5
our approach on	5
is derived from	5
given by the	5
have been developed	5
table retrieval task	5
g and p	5
they do not	5
and only if	5
documents to be	5
of the number	5
parts of the	5
to a new	5
the support score	5
have proposed a	5
to rank for	5
not need to	5
on irony detection	4
evaluation of information	4
to be relevant	4
by latent semantic	4
the paper is	4
does not require	4
terms in a	4
can be applied	4
approach can be	4
schema labels for	4
information needs that	4
is also a	4
in long tasks	4
associated to the	4
applied to a	4
of clef ehealth	4
is one of	4
the same set	4
we create a	4
and we use	4
a range of	4
a specific ab	4
with each other	4
neural networks with	4
in this way	4
used the same	4
meaning of the	4
words and topics	4
of neural re	4
set the number	4
over the baseline	4
networks for text	4
and the task	4
predict the review	4
editions of the	4
is carried out	4
of the pubmed	4
to bantu languages	4
bias goggles model	4
the complexity of	4
the neural models	4
t rooted at	4
a specific bc	4
training data set	4
and the baselines	4
inspired by the	4
we assume the	4
requirements for a	4
from a review	4
exact and semantic	4
learning of the	4
statistical classification algorithms	4
this ensures that	4
the implementation of	4
with the adam	4
filter out the	4
similarity search for	4
transfer learning is	4
models are trained	4
to approximate a	4
be the set	4
address the problem	4
have to be	4
sentiment of the	4
we consider two	4
removed from the	4
of labeled data	4
is obtained by	4
will focus on	4
in this category	4
with subword information	4
the story of	4
as can be	4
i are the	4
the spoken queries	4
a regression model	4
publicly available dataset	4
neural ir models	4
our goal is	4
as sd c	4
relevance matching model	4
representation learning with	4
according to its	4
the same stance	4
can be formulated	4
a new corpus	4
were able to	4
edge weight constraints	4
was found to	4
in different languages	4
number of topics	4
instead of the	4
be applied to	4
the dmp is	4
to seed words	4
based model for	4
validate our approach	4
for each restaurant	4
schema label representations	4
common standards model	4
for users to	4
from the product	4
differences between the	4
learning from logged	4
the same topic	4
premises from the	4
the noun class	4
semantic term matching	4
number of sentences	4
scale reproducibility study	4
all terms in	4
set of nodes	4
there are two	4
is illustrated in	4
a simple way	4
factoid qa datasets	4
to be useful	4
the trec robust	4
to the base	4
open domain suggestion	4
conditional on the	4
given a sequence	4
according to this	4
of search engines	4
we also include	4
for link prediction	4
where the support	4
visual saliency recall	4
an extension of	4
detailed in sect	4
to make it	4
be provided to	4
is the total	4
the stepwise recipe	4
to detect irony	4
of sentences in	4
attention is all	4
extensively used in	4
we have shown	4
shown to be	4
a consequence of	4
the results from	4
a theoretical analysis	4
efficiency of the	4
of the result	4
evaluation lab overview	4
with a large	4
can improve the	4
t is the	4
to the two	4
path length in	4
data management plan	4
are statistically significant	4
can be very	4
of information is	4
national research fund	4
function v d	4
norm of the	4
instead of a	4
a user may	4
for document clustering	4
the test data	4
is likely to	4
than the other	4
of the jth	4
to train a	4
and found that	4
term in the	4
also find that	4
set of features	4
are similar to	4
we argue that	4
mixed ranking model	4
that they would	4
the retrieved documents	4
this results in	4
avg sim of	4
the main contributions	4
each word w	4
the nature of	4
the inverted index	4
because of their	4
combination of the	4
their both views	4
to train the	4
to verify that	4
plan to explore	4
are semantically related	4
possibly correct answer	4
a stream of	4
word embedding techniques	4
the vector representation	4
to establish a	4
matrix factorization for	4
an ontology can	4
the proposed method	4
the current ranker	4
set of document	4
retrieved from the	4
a text query	4
point of view	4
the advantage of	4
set of users	4
task of the	4
approximate a review	4
since there are	4
based evaluation of	4
of document language	4
is the maximum	4
exactly the same	4
associated with the	4
the first to	4
been proposed for	4
the traditional dbgd	4
between the two	4
local contexts of	4
sentiment analysis methods	4
methods have been	4
model to predict	4
and the corresponding	4
query q k	4
than the original	4
the first step	4
objective is to	4
users tend to	4
for training the	4
seed words per	4
of the different	4
the ground truth	4
both text and	4
that exploit the	4
from the seeds	4
of a set	4
the language modeling	4
and structural information	4
information retrieval research	4
paper is organized	4
of text for	4
compare our proposed	4
proposed approach is	4
organized as follows	4
is relevant if	4
this is an	4
a review embedding	4
our vrss model	4
the global context	4
when using the	4
that have the	4
given a query	4
of the information	4
the review volume	4
of exploring the	4
the corresponding generator	4
dynamic pruning strategies	4
the labels of	4
followed by the	4
variational recurrent neural	4
and the query	4
to rank features	4
not available at	4
the training of	4
hit path set	4
results for the	4
submitted by the	4
the biased concept	4
on a dataset	4
the same domain	4
a lot of	4
proposed a model	4
a ranking problem	4
one way to	4
a user query	4
candidate rankers are	4
we did not	4
of the lab	4
on word embeddings	4
the generation of	4
to the participants	4
and show the	4
a latent space	4
premises for a	4
large corpus of	4
each document is	4
representations of a	4
the content of	4
keywords of medical	4
all you need	4
of the questions	4
search engines and	4
and product attention	4
the dimension of	4
of the cases	4
bm and beyond	4
chen et al	4
six bantu languages	4
topics and relevance	4
to get the	4
the same semantic	4
based approach to	4
be due to	4
on two datasets	4
for this purpose	4
term mismatch problem	4
share the same	4
the recommendation task	4
table summarizes the	4
the relevance filtering	4
the performances of	4
of the framework	4
precision and recall	4
edition of the	4
function of the	4
a method to	4
an ablation study	4
of each product	4
partial score upperbound	4
in the last	4
for that purpose	4
we calculate the	4
for a document	4
a target user	4
and used to	4
the probabilistic relevance	4
different media forms	4
the availability of	4
the need for	4
claims in political	4
representations of documents	4
it is essential	4
transfer learning for	4
the logging ranker	4
counterfactual risk minimization	4
word embedding as	4
of the three	4
for further details	4
average number of	4
features of the	4
the seq seq	4
a long history	4
the concept of	4
we also find	4
images that are	4
of each document	4
at a time	4
the text is	4
words that are	4
existing lexicon to	4
knowledge base completion	4
and neural models	4
to automate the	4
answer to the	4
in the area	4
of word representations	4
to both express	4
which is used	4
for query expansion	4
semantic matching in	4
the text input	4
language modeling approach	4
that the system	4
along the search	4
the data and	4
the best results	4
we can also	4
and image modalities	4
at the word	4
same set of	4
simple yet effective	4
corpus c i	4
from the qatar	4
the algorithms in	4
of the weights	4
goal of the	4
for the document	4
number of epochs	4
trained word embeddings	4
approaches based on	4
the original version	4
for a review	4
for dataset retrieval	4
between the target	4
so as to	4
of biased concepts	4
it is more	4
if and only	4
different types of	4
a document with	4
a framework for	4
the average number	4
the review text	4
manual seed words	4
word representations in	4
probabilistic relevance framework	4
have a higher	4
from different sources	4
used in this	4
and their corresponding	4
to each other	4
that relies on	4
reactions from patents	4
from the set	4
funded by the	4
ceur workshop proceedings	4
used for training	4
databases for rapid	4
field document ranking	4
have been made	4
to obtain a	4
is then fed	4
to show that	4
from review text	4
of the f	4
evaluation of graded	4
for recommender systems	4
the previous section	4
the same number	4
guided deep document	4
documents to the	4
task asks to	4
see for further	4
supported in part	4
in the future	4
simple english wikipedia	4
being able to	4
deep relevance matching	4
the same way	4
models and the	4
the computation graph	4
by cond gans	4
on the idea	4
training and validation	4
as input for	4
supporting information from	4
the loss of	4
to the one	4
available in the	4
ranking based on	4
and relevance judgments	4
extraction over chemical	4
the tf component	4
the values of	4
work can be	4
can be done	4
baselines on the	4
each step of	4
in vector space	4
deep learning models	4
and image models	4
of the association	4
we remove the	4
incremental approach for	4
will be provided	4
according to eq	4
query term in	4
sequence of images	4
that in the	4
and topic modeling	4
the offline performance	4
for a single	4
and natural language	4
as a sequence	4
is organized as	4
gating mechanism to	4
word embeddings to	4
indexing by latent	4
the search stages	4
as a multi	4
of the users	4
to the score	4
has been done	4
relevant information from	4
of document passages	4
deep document clustering	4
of annotated data	4
where the missing	4
number of parameters	4
that has been	4
by the participants	4
lexicon l i	4
and c have	4
of the underlying	4
we used a	4
our approach is	4
we show that	4
of the th	4
past few years	4
var times add	4
the clustering of	4
to the product	4
at query time	4
that are not	4
for the former	4
hoc information retrieval	4
focus only on	4
they have been	4
number of posting	4
all datasets and	4
of title and	4
note that we	4
of the top	4
to the sum	4
of the biased	4
and use it	4
other sentences in	4
an example of	4
prior work on	4
in knowledge graphs	4
same number of	4
our model outperforms	4
participants said that	4
of claims in	4
the dataset retrieval	4
we apply a	4
a similar approach	4
evaluated on the	4
the same data	4
node features are	4
schema label features	4
is necessary to	4
a single verb	4
to process the	4
the user embedding	4
english and arabic	4
the qatar national	4
exist in the	4
experiments were conducted	4
relevance judgments from	4
the usefulness of	4
find that the	4
the bias characteristics	4
the cosine distance	4
recent advances in	4
information of the	4
the most successful	4
from logged bandit	4
leads to the	4
the norm of	4
ari acc ari	4
implementation of the	4
acc ari acc	4
in long sessions	4
a description of	4
text in the	4
data collected from	4
images and text	4
of web pages	4
also use the	4
into a latent	4
we design a	4
we follow the	4
in the pipeline	4
rely on the	4
predicting the next	4
of the discriminator	4
embedding to the	4
where k is	4
was supported in	4
answer passage retrieval	4
automate the assessment	4
we construct the	4
zhang and balog	4
note that this	4
is the bias	4
contributions of this	4
as a graph	4
r is the	4
cambridge english exam	4
is predicted as	4
demonstrate the effectiveness	4
with a single	4
compute the semantic	4
next query prediction	4
basic retrieval models	4
rooted at n	4
the pivoted normalization	4
we train on	4
on the amazon	4
the best performance	4
of the support	4
in this study	4
ewc and ewc	4
terms in documents	4
of the cooccur	4
of the approach	4
this additional supporting	4
can be obtained	4
an existing lexicon	4
representations of sentences	4
the other methods	4
set of all	4
structural information of	4
in the computation	4
the sentence level	4
support vector machine	4
k and b	4
latent representations of	4
reuters rcv rcv	4
the sequence of	4
by adding a	4
are set to	4
a very large	4
all the words	4
of the nodes	4
the hierarchical structure	4
seen as a	4
neural network to	4
on early detection	4
is all you	4
the percentage of	4
to our knowledge	4
the document length	4
concatenation of title	4
the location mention	4
comprises of the	4
as future work	4
by leveraging the	4
between pairs of	4
of the methods	4
the term mismatch	4
average of all	4
on the accuracy	4
complex grammatical structure	4
can provide a	4
bias characteristics of	4
to get a	4
of the time	4
and morphological analysis	4
counterfactual online learning	4
the latent representations	4
set of documents	4
k k k	4
in the literature	4
the two previous	4
is generated by	4
keyphrase extraction from	4
a ranker from	4
the interactions between	4
extraction of chemical	4
of the most	4
that have been	4
and information retrieval	4
robertson et al	4
example of the	4
related to a	4
used to make	4
the distance between	4
than that of	4
the discovery of	4
other types of	4
seed words are	4
labels can be	4
are far from	4
matching in information	4
the efficiency of	4
participants stated that	4
of documents to	4
by comparing the	4
is essential for	4
address this problem	4
the extraction of	4
affected by the	4
for document classification	4
we have the	4
is considered as	4
case where the	4
from incomplete judgments	4
is composed of	4
socialism and war	4
of the queries	4
have been extensively	4
and precision at	4
we take the	4
a query q	4
the local relevance	4
weight of the	4
when applied to	4
trained on a	4
in times of	4
to differentiate between	4
refer to as	4
a linear combination	4
of sd c	4
for the first	4
the deep learning	4
the candidate users	4
to show to	4
on the dataset	4
amount of training	4
a given query	4
deep learning based	4
in political debates	4
to the data	4
the simplicity of	4
wu et al	4
hypothesize that the	4
focus on a	4
the difference in	4
out of the	4
the participants had	4
the neural model	4
the query reformulation	4
on the learned	4
we address the	4
variant of the	4
the item and	4
composed of a	4
as defined in	4
compatible with the	4
of the set	4
that for the	4
s k k	4
of the embedding	4
from the previous	4
of the conference	4
qatar national research	4
information from other	4
of each passage	4
in this model	4
to be more	4
table presents the	4
the datasets used	4
be close to	4
found in the	4
as an ontology	4
for the category	4
which does not	4
number of iterations	4
for which we	4
the relevance scores	4
some of them	4
query reformulation patterns	4
the union of	4
neural ranking model	4
appears to be	4
a disease and	4
and the other	4
in the language	4
model and the	4
concatenation of the	4
to the ones	4
learn the latent	4
is represented by	4
a large body	4
the review is	4
of an article	4
sequence of text	4
to label the	4
of the regression	4
for evaluation purposes	4
the purpose of	4
in this experiment	4
pairs of documents	4
which aims to	4
characteristics of web	4
of the web	4
detect irony in	4
interest in the	4
order to avoid	3
the first layer	3
in the corresponding	3
zamani and croft	3
approaches have been	3
estimation of word	3
number of tokens	3
a larger set	3
an effective way	3
sequential semantic structure	3
and the most	3
of each model	3
bipartite graph g	3
tables and show	3
from a large	3
learned by the	3
automatic keyphrase extraction	3
the idf component	3
our proposed method	3
of transrev is	3
with a missing	3
sentence representation is	3
is to be	3
more diverse and	3
that our approach	3
the most commonly	3
test with a	3
denotes the set	3
w and t	3
as an additional	3
in computer vision	3
in previous studies	3
we are not	3
for the training	3
we extract the	3
products and their	3
from top to	3
lab overview of	3
the retrieved image	3
reviews from the	3
techniques for recommender	3
a novel neural	3
and evaluated on	3
relevance filtering modules	3
be attributed to	3
was evaluated on	3
the relationship of	3
national science foundation	3
and schema labels	3
make use of	3
of these two	3
ranging from to	3
of damage present	3
of the collection	3
run times are	3
the k parameter	3
of the seed	3
expansion using word	3
identification of the	3
a review can	3
domain dom to	3
the target query	3
from our experiments	3
for stepwise illustration	3
encode the local	3
neural language models	3
similarly to the	3
the dataset for	3
recurrent convolutional neural	3
human generated machine	3
p rod and	3
of sentiment analysis	3
in previous works	3
asks to predict	3
a knowledge graph	3
task will be	3
the exception of	3
introduce a novel	3
standard image and	3
of the tasks	3
baselines in terms	3
information retrieval based	3
by machine translation	3
a partial score	3
information can be	3
of a single	3
of the algorithms	3
semantic meanings of	3
were asked to	3
word w ij	3
large datasets of	3
we test on	3
image does not	3
the model for	3
value of k	3
ecosystem for producing	3
and linear threshold	3
to reproduce the	3
for document and	3
of the last	3
the greek web	3
we were able	3
network and the	3
readability of articles	3
retrieval in the	3
bias scores of	3
the methods used	3
of deep learning	3
since the same	3
a single document	3
r k represents	3
features generated from	3
words and sentences	3
in the scoring	3
has also been	3
embedding of a	3
the previous queries	3
related to length	3
by gsra grant	3
the main components	3
the vector space	3
a document and	3
of the madmp	3
by the european	3
and their attributes	3
application of the	3
document retrieval using	3
we index the	3
prediction of online	3
of an ontology	3
to experiment with	3
be used as	3
deal with the	3
one can then	3
computed as the	3
in a story	3
building on the	3
premises in the	3
information from multiple	3
to increase the	3
and we have	3
task on early	3
regression model to	3
i t is	3
the cluster representatives	3
textual description of	3
we evaluate our	3
composed of text	3
the visual representation	3
are given by	3
have a positive	3
on machine translation	3
those related to	3
to reduce the	3
is contrary to	3
the online performance	3
in the metadata	3
assess the quality	3
we also provide	3
the relief group	3
results of this	3
concludes the paper	3
number of nodes	3
document figure classification	3
and we report	3
observations with their	3
along with a	3
of reuters rcv	3
the exploration of	3
most relevant information	3
of knowledge bases	3
average improvement in	3
of a given	3
embedding at test	3
generated by g	3
and c are	3
on all the	3
propose to use	3
the sentence s	3
two previous queries	3
the system needs	3
presented in table	3
model without the	3
can be represented	3
sentiment lexicon l	3
joint conference on	3
adam optimizer with	3
be solved using	3
and testing on	3
of claims as	3
on a subset	3
neural networks from	3
a publicly available	3
neural networks that	3
news and reuters	3
qa pairs with	3
of the current	3
the same query	3
is calculated as	3
position of each	3
the neighborhood of	3
number of common	3
social media users	3
was used to	3
validation and test	3
the matrix of	3
select a subset	3
techniques such as	3
if it is	3
representations and the	3
use early stopping	3
images and video	3
recurrent seq seq	3
the claim and	3
over all the	3
in a multilingual	3
between words and	3
to query terms	3
details of the	3
of this article	3
schema label generator	3
significance of the	3
set of web	3
damage in the	3
on the map	3
which leads to	3
both express and	3
candidate user length	3
our contributions are	3
is described by	3
dynamic tree cut	3
query evaluation using	3
allowing it to	3
explainable artificial intelligence	3
semantic structure of	3
readability analysis task	3
a single image	3
that bert sw	3
the latent representation	3
be related to	3
generated from a	3
there are only	3
a node to	3
models for web	3
users in a	3
multiview learning with	3
not included in	3
the variation of	3
a setting where	3
we have to	3
to produce recommendations	3
no effect on	3
proposed model for	3
address the term	3
k of the	3
network to learn	3
information retrieval an	3
which in turn	3
corresponding posting lists	3
knowledge graph completion	3
textual and visual	3
to reproduce prf	3
same as the	3
graph attention networks	3
than the previous	3
list of premises	3
noisy click settings	3
term u i	3
a small set	3
the following three	3
on political debates	3
to the network	3
as the baseline	3
is common to	3
similarity score is	3
summarised in table	3
pigd and pmgd	3
it is necessary	3
on how to	3
the vocabulary of	3
the raw co	3
related document classification	3
at this point	3
may be due	3
entity alignments in	3
random walks on	3
sources such as	3
a neural network	3
on extracting insights	3
and the product	3
with missing views	3
the links between	3
the top documents	3
in a similar	3
dataset search engines	3
using clickthrough data	3
the maximum value	3
words per cluster	3
speech tagging and	3
word vectors of	3
is a long	3
user searching for	3
research in the	3
to be the	3
more than tokens	3
when compared to	3
of each of	3
that this is	3
when we train	3
to retrieve the	3
we build upon	3
using attention fusion	3
k represents the	3
a space with	3
probabilistic ranking framework	3
hoc ranking with	3
for the two	3
has shown to	3
we considered the	3
is the average	3
are extracted from	3
task neural learning	3
the veracity of	3
precision at rank	3
of research papers	3
have shown that	3
up to three	3
reading comprehension dataset	3
ir axioms for	3
information in the	3
approaches that rely	3
dependencies among the	3
a model that	3
and compare it	3
previous queries in	3
and query terms	3
to the models	3
of the learned	3
all interdisciplinary actors	3
common standards working	3
with a user	3
bantu languages are	3
text and the	3
kong et al	3
randomly select of	3
their resources has	3
use them to	3
in our mapping	3
w is a	3
gsra grant gsra	3
want to update	3
which consists of	3
not publicly available	3
of user and	3
annotated with tasks	3
the text content	3
content of a	3
the former case	3
for a restaurant	3
to show the	3
an introduction to	3
the center for	3
to that effect	3
for the word	3
by using a	3
is due to	3
of them are	3
in one language	3
for parameter tuning	3
seems to be	3
story of how	3
with the exception	3
statistical language models	3
it might be	3
gradient descent algorithms	3
order to ensure	3
a member of	3
the image in	3
knowledge graph alignment	3
with posting lists	3
the dot product	3
based collaborative filtering	3
for the same	3
automatic verification of	3
of importance of	3
socialism and peace	3
by applying a	3
training for the	3
number of training	3
of the sentiment	3
this leads to	3
model consists of	3
word in the	3
and snippet retrieval	3
from table that	3
for the annotation	3
visual feature similarity	3
the evaluation results	3
of the ecosystem	3
query run times	3
correlated with the	3
is essential to	3
machine learning models	3
automatic generation of	3
number of reviews	3
the helpfulness of	3
based recommender systems	3
adjusted rand index	3
tokenized path t	3
a measure of	3
semantic matching model	3
a better overview	3
and long sessions	3
of the products	3
on amazon and	3
the images retrieved	3
the local contextual	3
with the number	3
term memory networks	3
standard information retrieval	3
and p real	3
applied to the	3
with the claim	3
difference of the	3
the top results	3
all the other	3
sparsity in the	3
out of registered	3
community interest in	3
for reviews that	3
the perspective of	3
to retrieve reviews	3
a comparative study	3
as the ground	3
is given in	3
by three annotators	3
the past few	3
information retrieval heuristics	3
top of the	3
best results are	3
as we are	3
is in line	3
capitalism and peace	3
we define the	3
graph that is	3
majority of the	3
for disaster management	3
for each premise	3
computed using the	3
from both the	3
models that we	3
level attention for	3
and fed into	3
makes relevance decisions	3
and for each	3
difficulty of the	3
to evaluate how	3
for the corresponding	3
in the test	3
outperforms the baseline	3
guided topic model	3
these results are	3
images from the	3
the individual modules	3
are selected from	3
for table retrieval	3
by computing the	3
takes advantage of	3
to the seed	3
maxscore to block	3
i aim to	3
on the context	3