prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are method improvements of F1 for paraphrase identification?
Context 1: Introduction
Data imbalance is a common issue in a variety of NLP tasks such as tagging and machine reading comprehension  Table TABREF3 gives concrete examples: for the Named Entity Recognition (NER) task BIBREF2, BIBREF3, most tokens are backgrounds with tagging class $O$  Specifically, the number of tokens tagging class $O$ is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5 0 dataset; Data-imbalanced issue is more severe for MRC tasks BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 with the value of negative-positive ratio being 50-200 
Data imbalance results in the following two issues: (1) the training-test discrepancy: Without balancing the labels, the learning process tends to converge to a point that strongly biases towards class with the majority label  This actually creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function while at test time, F1 score concerns more about positive examples; (2) the overwhelming effect of easy-negative examples  As pointed out by meng2019dsreg, significantly large number of negative examples also means that the number of easy-negative example is large  The huge number of easy examples tends to overwhelm the training, making the model not sufficiently learned to distinguish between positive examples and hard-negative examples  The cross-entropy objective (CE for short) or maximum likelihood (MLE) objective, which is widely adopted as the training objective for data-imbalanced NLP tasks BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, handles neither of the issues 
To handle the first issue, we propose to replace CE or MLE with losses based on the Sørensen–Dice coefficient BIBREF0 or Tversky index BIBREF1  The Sørensen–Dice coefficient, dice loss for short, is the harmonic mean of precision and recall  It attaches equal importance to false positives (FPs) and false negatives (FNs) and is thus more immune to data-imbalanced datasets  Tversky index extends dice loss by using a weight that trades precision and recall, which can be thought as the approximation of the $F_{\beta }$ score, and thus comes with more flexibility  Therefore, We use dice loss or Tversky index to replace CE loss to address the first issue 
Only using dice loss or Tversky index is not enough since they are unable to address the dominating influence of easy-negative examples  This is intrinsically because dice loss is actually a hard version of the F1 score  Taking the binary classification task as an example, at test time, an example will be classified as negative as long as its probability is smaller than 0 5, but training will push the value to 0 as much as possible  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones  Inspired by the idea of focal loss BIBREF16 in computer vision, we propose a dynamic weight adjusting strategy, which associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds  This strategy helps to deemphasize confident examples during training as their $p$ approaches the value of 1, makes the model attentive to hard-negative examples, and thus alleviates the dominating effect of easy-negative examples 
Combing both strategies, we observe significant performance boosts on a wide range of data imbalanced NLP tasks  Notably, we are able to achieve SOTA results on CTB5 (97 92, +1 86), CTB6 (96 57, +1 80) and UD1 4 (96 98, +2 19) for the POS task; SOTA results on CoNLL03 (93 33, +0 29), OntoNotes5 0 (92 07, +0 96)), MSRA 96 72(+0 97) and OntoNotes4 0 (84 47,+2 36) for the NER task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification 
The rest of this paper is organized as follows: related work is presented in Section 2  We describe different training objectives in Section 3  Experimental results are presented in Section 4  We perform ablation studies in Section 5, followed by a brief conclusion in Section 6 
Related Work ::: Data Resample
The idea of weighting training examples has a long history  Importance sampling BIBREF17 assigns weights to different samples and changes the data distribution  Boosting algorithms such as AdaBoost BIBREF18 select harder examples to train subsequent classifiers  Similarly, hard example mining BIBREF19 downsamples the majority class and exploits the most difficult examples  Oversampling BIBREF20, BIBREF21 is used to balance the data distribution  Another line of data resampling is to dynamically control the weights of examples as training proceeds  For example, focal loss BIBREF16 used a soft weighting scheme that emphasizes harder examples during training  In self-paced learning BIBREF22, example weights are obtained through optimizing the weighted training loss which encourages learning easier examples first  At each training step, self-paced learning algorithm optimizes model parameters and example weights jointly  Other works BIBREF23, BIBREF24 adjusted the weights of different training examples based on training loss  Besides, recent work BIBREF25, BIBREF26 proposed to learn a separate network to predict sample weights 
Related Work ::: Data Imbalance Issue in Object Detection
The background-object label imbalance issue is severe and thus well studied in the field of object detection BIBREF27, BIBREF28, BIBREF29, BIBREF30, BIBREF31  The idea of hard negative mining (HNM) BIBREF30 has gained much attention recently  shrivastava2016ohem proposed the online hard example mining (OHEM) algorithm in an iterative manner that makes training progressively more difficult, and pushes the model to learn better  ssd2016liu sorted all of the negative samples based on the confidence loss and picking the training examples with the negative-positive ratio at 3:1  pang2019rcnn proposed a novel method called IoU-balanced sampling and aploss2019chen designed a ranking model to replace the conventional classification task with a average-precision loss to alleviate the class imbalance issue  The efforts made on object detection have greatly inspired us to solve the data imbalance issue in NLP 
Losses ::: Notation
For illustration purposes, we use the binary classification task to demonstrate how different losses work  The mechanism can be easily extended to multi-class classification 
Let $\lbrace x_i\rbrace $ denote a set of instances  Each $x_i$ is associated with a golden label vector $y_i = [y_{i0},y_{i1} ]$, where $y_{i1}\in \lbrace 0,1\rbrace $ and $y_{i0}\in \lbrace 0,1\rbrace $ respectively denote the positive and negative classes, and thus $y_i$ can be either $[0,1]$ or $[0,1]$  Let $p_i = [p_{i0},p_{i1} ]$ denote the probability vector, and $p_{i1}$ and $p_{i0}$ respectively denote the probability that a model assigns the positive and negative label to $x_i$ 
Losses ::: Cross Entropy Loss
The vanilla cross entropy (CE) loss is given by:
As can be seen from Eq DISPLAY_FORM8, each $x_i$ contributes equally to the final objective  Two strategies are normally used to address the the case where we wish that not all $x_i$ are treated equal: associating different classes with different weighting factor $\alpha $ or resampling the datasets  For the former, Eq DISPLAY_FORM8 is adjusted as follows:
where $\alpha _i\in [0,1]$ may be set by the inverse class frequency or treated as a hyperparameter to set by cross validation  In this work, we use $\lg (\frac{n-n_t}{n_t}+K)$ to calculate the coefficient $\alpha $, where $n_t$ is the number of samples with class $t$ and $n$ is the total number of samples in the training set  $K$ is a hyperparameter to tune  The data resampling strategy constructs a new dataset by sampling training examples from the original dataset based on human-designed criteria, e g , extract equal training samples from each class  Both strategies are equivalent to changing the data distribution and thus are of the same nature  Empirically, these two methods are not widely used due to the trickiness of selecting $\alpha $ especially for multi-class classification tasks and that inappropriate selection can easily bias towards rare classes BIBREF32 
Losses ::: Dice coefficient and Tversky index
Sørensen–Dice coefficient BIBREF0, BIBREF33, dice coefficient (DSC) for short, is a F1-oriented statistic used to gauge the similarity of two sets  Given two sets $A$ and $B$, the dice coefficient between them is given as follows:
In our case, $A$ is the set that contains of all positive examples predicted by a specific model, and $B$ is the set of all golden positive examples in the dataset  When applied to boolean data with the definition of true positive (TP), false positive (FP), and false negative (FN), it can be then written as follows:
For an individual example $x_i$, its corresponding DSC loss is given as follows:
As can be seen, for a negative example with $y_{i1}=0$, it does not contribute to the objective  For smoothing purposes, it is common to add a $\gamma $ factor to both the nominator and the denominator, making the form to be as follows:
As can be seen, negative examples, with $y_{i1}$ being 0 and DSC being $\frac{\gamma }{ p_{i1}+\gamma }$, also contribute to the training  Additionally, milletari2016v proposed to change the denominator to the square form for faster convergence, which leads to the following dice loss (DL):
Another version of DL is to directly compute set-level dice coefficient instead of the sum of individual dice coefficient  We choose the latter due to ease of optimization 
Tversky index (TI), which can be thought as the approximation of the $F_{\beta }$ score, extends dice coefficient to a more general case  Given two sets $A$ and $B$, tversky index is computed as follows:
Tversky index offers the flexibility in controlling the tradeoff between false-negatives and false-positives  It degenerates to DSC if $\alpha =\beta =0 5$  The Tversky loss (TL) for the training set $\lbrace x_i,y_i\rbrace $ is thus as follows:
Losses ::: Self-adusting Dice Loss
Consider a simple case where the dataset consists of only one example $x_i$, which is classified as positive as long as $p_{i1}$ is larger than 0 5  The computation of $F1$ score is actually as follows:
Comparing Eq DISPLAY_FORM14 with Eq DISPLAY_FORM22, we can see that Eq DISPLAY_FORM14 is actually a soft form of $F1$, using a continuous $p$ rather than the binary $\mathbb {I}( p_{i1}>0 5)$  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones, which has a huge negative effect on the final F1 performance 
To address this issue, we propose to multiply the soft probability $p$ with a decaying factor $(1-p)$, changing Eq DISPLAY_FORM22 to the following form:
One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds  The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples  For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them  Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified 
A close look at Eq DISPLAY_FORM14 reveals that it actually mimics the idea of focal loss (FL for short) BIBREF16 for object detection in vision  Focal loss was proposed for one-stage object detector to handle foreground-background tradeoff encountered during training  It down-weights the loss assigned to well-classified examples by adding a $(1-p)^{\beta }$ factor, leading the final loss to be $(1-p)^{\beta }\log p$ 
In Table TABREF18, we show the losses used in our experiments, which is described in the next section 
Experiments
We evaluate the proposed method on four NLP tasks: part-of-speech tagging, named entity recognition, machine reading comprehension and paraphrase identification  Baselines in our experiments are optimized by using the standard cross-entropy training objective 
Experiments ::: Part-of-Speech Tagging
Part-of-speech tagging (POS) is the task of assigning a label (e g , noun, verb, adjective) to each word in a given text  In this paper, we choose BERT as the backbone and conduct experiments on three Chinese POS datasets  We report the span-level micro-averaged precision, recall and F1 for evaluation  Hyperparameters are tuned on the corresponding development set of each dataset 
Experiments ::: Part-of-Speech Tagging ::: Datasets
We conduct experiments on the widely used Chinese Treebank 5 0, 6 0 as well as UD1 4 
CTB5 is a Chinese dataset for tagging and parsing, which contains 507,222 words, 824,983 characters and 18,782 sentences extracted from newswire sources 
CTB6 is an extension of CTB5, containing 781,351 words, 1,285,149 characters and 28,295 sentences 
UD is the abbreviation of Universal Dependencies, which is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages  In this work, we use UD1 4 for Chinese POS tagging 
Experiments ::: Part-of-Speech Tagging ::: Baselines
We use the following baselines:
Joint-POS: shao2017character jointly learns Chinese word segmentation and POS 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice 
Bert-Tagger: devlin2018bert treats part-of-speech as a tagging task 
Experiments ::: Part-of-Speech Tagging ::: Results
Table presents the experimental results on the POS task  As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i e , outperforming BERT-tagger by +1 86 in terms of F1 score on CTB5, +1 80 on CTB6 and +2 19 on UD1 4  As far as we are concerned, we are achieving SOTA performances on the three datasets  Weighted cross entropy and focal loss only gain a little performance improvement on CTB5 and CTB6, and the dice loss obtains huge gain on CTB5 but not on CTB6, which indicates the three losses are not consistently robust in resolving the data imbalance issue  The proposed DSC loss performs robustly on all the three datasets 
Experiments ::: Named Entity Recognition
Named entity recognition (NER) refers to the task of detecting the span and semantic category of entities from a chunk of text  Our implementation uses the current state-of-the-art BERT-MRC model proposed by xiaoya2019ner as a backbone  For English datasets, we use BERT$_\text{Large}$ English checkpoints, while for Chinese we use the official Chinese checkpoints  We report span-level micro-averaged precision, recall and F1-score  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Named Entity Recognition ::: Datasets
For the NER task, we consider both Chinese datasets, i e , OntoNotes4 0 BIBREF34 and MSRA BIBREF35, and English datasets, i e , CoNLL2003 BIBREF36 and OntoNotes5 0 BIBREF37 
CoNLL2003 is an English dataset with 4 entity types: Location, Organization, Person and Miscellaneous  We followed data processing protocols in BIBREF14 
English OntoNotes5 0 consists of texts from a wide variety of sources and contains 18 entity types  We use the standard train/dev/test split of CoNLL2012 shared task 
Chinese MSRA performs as a Chinese benchmark dataset containing 3 entity types  Data in MSRA is collected from news domain  Since the development set is not provided in the original MSRA dataset, we randomly split the training set into training and development splits by 9:1  We use the official test set for evaluation 
Chinese OntoNotes4 0 is a Chinese dataset and consists of texts from news domain, which has 18 entity types  In this paper, we take the same data split as wu2019glyce did 
Experiments ::: Named Entity Recognition ::: Baselines
We use the following baselines:
ELMo: a tagging model from peters2018deep 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice, only used in Chinese datasets 
CVT: from kevin2018cross, which uses Cross-View Training(CVT) to improve the representations of a Bi-LSTM encoder 
Bert-Tagger: devlin2018bert treats NER as a tagging task 
Glyce-BERT: wu2019glyce combines glyph information with BERT pretraining 
BERT-MRC: The current SOTA model for both Chinese and English NER datasets proposed by xiaoya2019ner, which formulate NER as machine reading comprehension task 
Experiments ::: Named Entity Recognition ::: Results
Table shows experimental results on NER datasets  For English datasets including CoNLL2003 and OntoNotes5 0, our proposed method outperforms BERT-MRCBIBREF38 by +0 29 and +0 96 respectively  We observe huge performance boosts on Chinese datasets, achieving F1 improvements by +0 97 and +2 36 on MSRA and OntoNotes4 0, respectively  As far as we are concerned, we are setting new SOTA performances on all of the four NER datasets 
Experiments ::: Machine Reading Comprehension
Machine reading comprehension (MRC) BIBREF39, BIBREF40, BIBREF41, BIBREF40, BIBREF42, BIBREF15 has become a central task in natural language understanding  MRC in the SQuAD-style is to predict the answer span in the passage given a question and the passage  In this paper, we choose the SQuAD-style MRC task and report Extract Match (EM) in addition to F1 score on validation set  All hyperparameters are tuned on the development set of each dataset 
Experiments ::: Machine Reading Comprehension ::: Datasets
The following five datasets are used for MRC task: SQuAD v1 1, SQuAD v2 0 BIBREF4, BIBREF6 and Quoref BIBREF8 
SQuAD v1 1 and SQuAD v2 0 are the most widely used QA benchmarks  SQuAD1 1 is a collection of 100K crowdsourced question-answer pairs, and SQuAD2 0 extends SQuAD1 1 allowing no short answer exists in the provided passage 
Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems, containing 24K questions over 4 7K paragraphs from Wikipedia 
Experiments ::: Machine Reading Comprehension ::: Baselines
We use the following baselines:
QANet: qanet2018 builds a model based on convolutions and self-attention  Convolution to model local interactions and self-attention to model global interactions 
BERT: devlin2018bert treats NER as a tagging task 
XLNet: xlnet2019 proposes a generalized autoregressive pretraining method that enables learning bidirectional contexts 
Experiments ::: Machine Reading Comprehension ::: Results
Table shows the experimental results for MRC tasks  With either BERT or XLNet, our proposed DSC loss obtains significant performance boost on both EM and F1  For SQuADv1 1, our proposed method outperforms XLNet by +1 25 in terms of F1 score and +0 84 in terms of EM and achieves 87 65 on EM and 89 51 on F1 for SQuAD v2 0  Moreover, on QuoRef, the proposed method surpasses XLNet results by +1 46 on EM and +1 41 on F1  Another observation is that, XLNet outperforms BERT by a huge margin, and the proposed DSC loss can obtain further performance improvement by an average score above 1 0 in terms of both EM and F1, which indicates the DSC loss is complementary to the model structures 
Experiments ::: Paraphrase Identification
Paraphrases are textual expressions that have the same semantic meaning using different surface words  Paraphrase identification (PI) is the task of identifying whether two sentences have the same meaning or not  We use BERT BIBREF11 and XLNet BIBREF43 as backbones and report F1 score for comparison  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Paraphrase Identification ::: Datasets
We conduct experiments on two widely used datasets for PI task: MRPC BIBREF44 and QQP 
MRPC is a corpus of sentence pairs automatically extracted from online news sources, with human annotations of whether the sentence pairs are semantically equivalent  The MRPC dataset has imbalanced classes (68% positive, 32% for negative) 
QQP is a collection of question pairs from the community question-answering website Quora  The class distribution in QQP is also unbalanced (37% positive, 63% negative) 
Experiments ::: Paraphrase Identification ::: Results
Table shows the results for PI task  We find that replacing the training objective with DSC introduces performance boost for both BERT and XLNet  Using DSC loss improves the F1 score by +0 58 for MRPC and +0 73 for QQP 
Ablation Studies ::: The Effect of Dice Loss on Accuracy-oriented Tasks
We argue that the most commonly used cross-entropy objective is actually accuracy-oriented, whereas the proposed dice loss (DL) performs as a hard version of F1-score  To explore the effect of the dice loss on accuracy-oriented tasks such as text classification, we conduct experiments on the Stanford Sentiment Treebank sentiment classification datasets including SST-2 and SST-5  We fine-tune BERT$_\text{Large}$ with different training objectives  Experiment results for SST are shown in   For SST-5, BERT with CE achieves 55 57 in terms of accuracy, with DL and DSC losses slightly degrade the accuracy performance and achieve 54 63 and 55 19, respectively  For SST-2, BERT with CE achieves 94 9 in terms of accuracy  The same as SST-5, we observe a slight performance drop with DL and DSC, which means that the dice loss actually works well for F1 but not for accuracy 
Ablation Studies ::: The Effect of Hyperparameters in Tversky index
As mentioned in Section SECREF10, Tversky index (TI) offers the flexibility in controlling the tradeoff between false-negatives and false-positives  In this subsection, we explore the effect of hyperparameters (i e , $\alpha $ and $\beta $) in TI to test how they manipulate the tradeoff  We conduct experiments on the Chinese OntoNotes4 0 NER dataset and English QuoRef MRC dataset to examine the influence of tradeoff between precision and recall  Experiment results are shown in Table   The highest F1 for Chinese OntoNotes4 0 is 84 67 when $\alpha $ is set to 0 6 while for QuoRef, the highest F1 is 68 44 when $\alpha $ is set to 0 4  In addition, we can observe that the performance varies a lot as $\alpha $ changes in distinct datasets, which shows that the hyperparameters $\alpha ,\beta $ play an important role in the proposed method 
Conclusion
In this paper, we alleviate the severe data imbalance issue in NLP tasks  We propose to use dice loss in replacement of the standard cross-entropy loss, which performs as a soft version of F1 score  Using dice loss can help narrow the gap between training objectives and evaluation metrics  Empirically, we show that the proposed training objective leads to significant performance boost for part-of-speech, named entity recognition, machine reading comprehension and paraphrase identification tasks 
Context 2: Introduction
We posses a wealth of prior knowledge about many natural language processing tasks  For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification 
A key problem arisen here, is how to leverage such knowledge to guide the learning process, an interesting problem for both NLP and machine learning communities  Previous studies addressing the problem fall into several lines  First, to leverage prior knowledge to label data BIBREF1 , BIBREF2   Second, to encode prior knowledge with a prior on parameters, which can be commonly seen in many Bayesian approaches BIBREF3 , BIBREF4   Third, to formalise prior knowledge with additional variables and dependencies BIBREF5   Last, to use prior knowledge to control the distributions over latent output variables BIBREF6 , BIBREF7 , BIBREF8 , which makes the output variables easily interpretable 
However, a crucial problem, which has rarely been addressed, is the bias in the prior knowledge that we supply to the learning model  Would the model be robust or sensitive to the prior knowledge? Or, which kind of knowledge is appropriate for the task? Let's see an example: we may be a baseball fan but unfamiliar with hockey so that we can provide a few number of feature words of baseball, but much less of hockey for a baseball-hockey classification task  Such prior knowledge may mislead the model with heavy bias to baseball  If the model cannot handle this situation appropriately, the performance may be undesirable 
In this paper, we investigate into the problem in the framework of Generalized Expectation Criteria BIBREF7   The study aims to reveal the factors of reducing the sensibility of the prior knowledge and therefore to make the model more robust and practical  To this end, we introduce auxiliary regularization terms in which our prior knowledge is formalized as distribution over output variables  Recall the example just mentioned, though we do not have enough knowledge to provide features for class hockey, it is easy for us to provide some neutral words, namely words that are not strong indicators of any class, like player here  As one of the factors revealed in this paper, supplying neutral feature words can boost the performance remarkably, making the model more robust 
More attractively, we do not need manual annotation to label these neutral feature words in our proposed approach 
More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution  For the first manner, we simply use the most common features as neutral features and assume the neutral features are distributed uniformly over class labels  For the second and third one, we assume we have some knowledge about the class distribution which will be detailed soon later 
To summarize, the main contributions of this work are as follows:
The rest of the paper is structured as follows: In Section 2, we briefly describe the generalized expectation criteria and present the proposed regularization terms  In Section 3, we conduct extensive experiments to justify the proposed methods  We survey related work in Section 4, and summarize our work in Section 5 
Method
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge  A labeled feature is a strong indicator of a specific class and is manually provided to the classifier  For example, words like amazing, exciting can be labeled features for class positive in sentiment classification 
Generalized Expectation Criteria
Generalized expectation (GE) criteria BIBREF7 provides us a natural way to directly constrain the model in the preferred direction  For example, when we know the proportion of each class of the dataset in a classification task, we can guide the model to predict out a pre-specified class distribution 
Formally, in a parameter estimation objective function, a GE term expresses preferences on the value of some constraint functions about the model's expectation  Given a constraint function $G({\rm x}, y)$ , a conditional model distribution $p_\theta (y|\rm x)$ , an empirical distribution $\tilde{p}({\rm x})$ over input samples and a score function $S$ , a GE term can be expressed as follows:
$$S(E_{\tilde{p}({\rm x})}[E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]])$$   (Eq  4)
Learning from Labeled Features
Druck et al  ge-fl proposed GE-FL to learn from labeled features using generalized expectation criteria  When given a set of labeled features $K$ , the reference distribution over classes of these features is denoted by $\hat{p}(y| x_k), k \in K$   GE-FL introduces the divergence between this reference distribution and the model predicted distribution $p_\theta (y | x_k)$ , as a term of the objective function:
$$\mathcal {O} = \sum _{k \in K} KL(\hat{p}(y|x_k) || p_\theta (y | x_k)) + \sum _{y,i} \frac{\theta _{yi}^2}{2 \sigma ^2}$$   (Eq  6)
where $\theta _{yi}$ is the model parameter which indicates the importance of word $i$ to class $y$   The predicted distribution $p_\theta (y | x_k)$ can be expressed as follows: $ p_\theta (y | x_k) = \frac{1}{C_k} \sum _{\rm x} p_\theta (y|{\rm x})I(x_k) $
in which $I(x_k)$ is 1 if feature $k$ occurs in instance ${\rm x}$ and 0 otherwise, $C_k = \sum _{\rm x} I(x_k)$ is the number of instances with a non-zero value of feature $k$ , and $p_\theta (y|{\rm x})$ takes a softmax form as follows: $ p_\theta (y|{\rm x}) = \frac{1}{Z(\rm x)}\exp (\sum _i \theta _{yi}x_i)  $
To solve the optimization problem, L-BFGS can be used for parameter estimation 
In the framework of GE, this term can be obtained by setting the constraint function $G({\rm x}, y) = \frac{1}{C_k} \vec{I} (y)I(x_k)$ , where $\vec{I}(y)$ is an indicator vector with 1 at the index corresponding to label $y$ and 0 elsewhere 
Regularization Terms
GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias  In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied  But as mentioned before, it is often the case that we are not able to provide enough knowledge for some of the classes  For the baseball-hockey classification task, as shown before, GE-FL will predict most of the instances as baseball  In this section, we will show three terms to make the model more robust 
Neutral features are features that are not informative indicator of any classes, for instance, word player to the baseball-hockey classification task  Such features are usually frequent words across all categories  When we set the preference distribution of the neutral features to be uniform distributed, these neutral features will prevent the model from biasing to the class that has a dominate number of labeled features 
Formally, given a set of neutral features $K^{^{\prime }}$ , the uniform distribution is $\hat{p}_u(y|x_k) = \frac{1}{|C|}, k \in K^{^{\prime }}$ , where $|C|$ is the number of classes  The objective function with the new term becomes
$$\mathcal {O}_{NE} = \mathcal {O} + \sum _{k \in K^{^{\prime }}} KL(\hat{p}_u(y|x_k) || p_\theta (y | x_k)) $$   (Eq  9)
Note that we do not need manual annotation to provide neutral features  One simple way is to take the most common features as neutral features  Experimental results show that this strategy works successfully 
Another way to prevent the model from drifting from the desired direction is to constrain the predicted class distribution on unlabeled data  When lacking knowledge about the class distribution of the data, one feasible way is to take maximum entropy principle, as below:
$$\mathcal {O}_{ME} = \mathcal {O} + \lambda \sum _{y} p(y) \log p(y)$$   (Eq  11)
where $p(y)$ is the predicted class distribution, given by $ p(y) = \frac{1}{|X|} \sum _{\rm x} p_\theta (y | \rm x)  $ To control the influence of this term on the overall objective function, we can tune $\lambda $ according to the difference in the number of labeled features of each class  In this paper, we simply set $\lambda $ to be proportional to the total number of labeled features, say $\lambda = \beta |K|$  
This maximum entropy term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$   Therefore, $E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]$ is just the model distribution $p_\theta (y|{\rm x})$ and its expectation with the empirical distribution $\tilde{p}(\rm x)$ is simply the average over input samples, namely $p(y)$   When $S$ takes the maximum entropy form, we can derive the objective function as above 
Sometimes, we have already had much knowledge about the corpus, and can estimate the class distribution roughly without labeling instances  Therefore, we introduce the KL divergence between the predicted and reference class distributions into the objective function 
Given the preference class distribution $\hat{p}(y)$ , we modify the objective function as follows:
$$\mathcal {O}_{KL} &= \mathcal {O} + \lambda KL(\hat{p}(y) || p(y))$$   (Eq  13)
Similarly, we set $\lambda = \beta |K|$  
This divergence term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$ and setting the score function to $S(\hat{p}, p) = \sum _i \hat{p}_i \log \frac{\hat{p}_i}{p_i}$ , where $p$ and $\hat{p}$ are distributions  Note that this regularization term involves the reference class distribution which will be discussed later 
Experiments
In this section, we first justify the approach when there exists unbalance in the number of labeled features or in class distribution  Then, to test the influence of $\lambda $ , we conduct some experiments with the method which incorporates the KL divergence of class distribution  Last, we evaluate our approaches in 9 commonly used text classification datasets  We set $\lambda = 5|K|$ by default in all experiments unless there is explicit declaration  The baseline we choose here is GE-FL BIBREF0 , a method based on generalization expectation criteria 
Data Preparation
We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare  We use bag-of-words feature and remove stopwords in the preprocess stage  Though we have labels of all documents, we do not use them during the learning process, instead, we use the label of features 
The movie dataset, in which the task is to classify the movie reviews as positive or negtive, is used for testing the proposed approaches with unbalanced labeled features, unbalanced datasets or different $\lambda $ parameters  All unbalanced datasets are constructed based on the movie dataset by randomly removing documents of the positive class  For each experiment, we conduct 10-fold cross validation 
As described in BIBREF0 , there are two ways to obtain labeled features  The first way is to use information gain  We first calculate the mutual information of all features according to the labels of the documents and select the top 20 as labeled features for each class as a feature pool  Note that using information gain requires the document label, but this is only to simulate how we human provide prior knowledge to the model  The second way is to use LDA BIBREF9 to select features  We use the same selection process as BIBREF0 , where they first train a LDA on the dataset, and then select the most probable features of each topic (sorted by $P(w_i|t_j)$ , the probability of word $w_i$ given topic $t_j$ ) 
Similar to BIBREF10 , BIBREF0 , we estimate the reference distribution of the labeled features using a heuristic strategy  If there are $|C|$ classes in total, and $n$ classes are associated with a feature $k$ , the probability that feature $k$ is related with any one of the $n$ classes is $\frac{0 9}{n}$ and with any other class is $\frac{0 1}{|C| - n}$  
Neutral features are the most frequent words after removing stop words, and their reference distributions are uniformly distributed  We use the top 10 frequent words as neutral features in all experiments 
With Unbalanced Labeled Features
In this section, we evaluate our approach when there is unbalanced knowledge on the categories to be classified  The labeled features are obtained through information gain  Two settings are chosen:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
As shown in Figure 1 , Maximum entropy principle shows improvement only on the balanced case  An obvious reason is that maximum entropy only favors uniform distribution 
Incorporating Neutral features performs similarly to maximum entropy since we assume that neutral words are uniformly distributed  Its accuracy decreases slowly when the number of labeled features becomes larger ( $t>4$ ) (Figure 1 (a)), suggesting that the model gradually biases to the class with more labeled features, just like GE-FL 
Incorporating the KL divergence of class distribution performs much better than GE-FL on both balanced and unbalanced datasets  This shows that it is effective to control the unbalance in labeled features and in the dataset 
With Balanced Labeled Features
We also compare with the baseline when the labeled features are balanced  Similar to the experiment above, the labeled features are obtained by information gain  Two settings are experimented with:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for each class, and conduct comparisons on the original balanced movie dataset (positive:negtive=1:1) 
(b) Similar to (a), but the class distribution is unbalanced, by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 2   When the dataset is balanced (Figure 2 (a)), there is little difference between GE-FL and our methods  The reason is that the proposed regularization terms provide no additional knowledge to the model and there is no bias in the labeled features  On the unbalanced dataset (Figure 2 (b)), incorporating KL divergence is much better than GE-FL since we provide additional knowledge(the true class distribution), but maximum entropy and neutral features are much worse because forcing the model to approach the uniform distribution misleads it 
With Unbalanced Class Distributions
Our methods are also evaluated on datasets with different unbalanced class distributions  We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents  The original balanced movie dataset is used as a control group  We test with both balanced and unbalanced labeled features  For the balanced case, we randomly select 10 features from the feature pool for each class, and for the unbalanced case, we select 10 features for one class, and 1 feature for the other  Results are shown in Figure 3  
Figure 3 (a) shows that when the dataset and the labeled features are both balanced, there is little difference between our methods and GE-FL(also see Figure 2 (a))  But when the class distribution becomes more unbalanced, the difference becomes more remarkable  Performance of neutral features and maximum entropy decrease significantly but incorporating KL divergence increases remarkably  This suggests if we have more accurate knowledge about class distribution, KL divergence can guide the model to the right direction 
Figure 3 (b) shows that when the labeled features are unbalanced, our methods significantly outperforms GE-FL  Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive 
The Influence of λ\lambda 
We present the influence of $\lambda $ on the method that incorporates KL divergence in this section  Since we simply set $\lambda = \beta |K|$ , we just tune $\beta $ here  Note that when $\beta = 0$ , the newly introduced regularization term is disappeared, and thus the model is actually GE-FL  Again, we test the method with different $\lambda $ in two settings:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other class  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 4   As expected, $\lambda $ reflects how strong the regularization is  The model tends to be closer to our preferences with the increasing of $\lambda $ on both cases 
Using LDA Selected Features
We compare our methods with GE-FL on all the 9 datasets in this section  Instead of using features obtained by information gain, we use LDA to select labeled features  Unlike information gain, LDA does not employ any instance labels to find labeled features  In this setting, we can build classification models without any instance annotation, but just with labeled features 
Table 1 shows that our three methods significantly outperform GE-FL  Incorporating neutral features performs better than GE-FL on 7 of the 9 datasets, maximum entropy is better on 8 datasets, and KL divergence better on 7 datasets 
LDA selects out the most predictive features as labeled features without considering the balance among classes  GE-FL does not exert any control on such an issue, so the performance is severely suffered  Our methods introduce auxiliary regularization terms to control such a bias problem and thus promote the model significantly 
Related Work
There have been much work that incorporate prior knowledge into learning, and two related lines are surveyed here  One is to use prior knowledge to label unlabeled instances and then apply a standard learning algorithm  The other is to constrain the model directly with prior knowledge 
Liu et al text manually labeled features which are highly predictive to unsupervised clustering assignments and use them to label unlabeled data  Chang et al guiding proposed constraint driven learning  They first used constraints and the learned model to annotate unlabeled instances, and then updated the model with the newly labeled data  Daumé daume2008cross proposed a self training method in which several models are trained on the same dataset, and only unlabeled instances that satisfy the cross task knowledge constraints are used in the self training process 
MaCallum et al gec proposed generalized expectation(GE) criteria which formalised the knowledge as constraint terms about the expectation of the model into the objective function Graça et al pr proposed posterior regularization(PR) framework which projects the model's posterior onto a set of distributions that satisfy the auxiliary constraints  Druck et al ge-fl explored constraints of labeled features in the framework of GE by forcing the model's predicted feature distribution to approach the reference distribution  Andrzejewski et al andrzejewski2011framework proposed a framework in which general domain knowledge can be easily incorporated into LDA  Altendorf et al altendorf2012learning explored monotonicity constraints to improve the accuracy while learning from sparse data  Chen et al chen2013leveraging tried to learn comprehensible topic models by leveraging multi-domain knowledge 
Mann and McCallum simple,generalized incorporated not only labeled features but also other knowledge like class distribution into the objective function of GE-FL  But they discussed only from the semi-supervised perspective and did not investigate into the robustness problem, unlike what we addressed in this paper 
There are also some active learning methods trying to use prior knowledge  Raghavan et al feedback proposed to use feedback on instances and features interlacedly, and demonstrated that feedback on features boosts the model much  Druck et al active proposed an active learning method which solicits labels on features rather than on instances and then used GE-FL to train the model 
Conclusion and Discussions
This paper investigates into the problem of how to leverage prior knowledge robustly in learning models  We propose three regularization terms on top of generalized expectation criteria  As demonstrated by the experimental results, the performance can be considerably improved when taking into account these factors  Comparative results show that our proposed methods is more effective and works more robustly against baselines  To the best of our knowledge, this is the first work to address the robustness problem of leveraging knowledge, and may inspire other research 
We then present more detailed discussions about the three regularization methods  Incorporating neutral features is the simplest way of regularization, which doesn't require any modification of GE-FL but just finding out some common features  But as Figure 1 (a) shows, only using neutral features are not strong enough to handle extremely unbalanced labeled features 
The maximum entropy regularization term shows the strong ability of controlling unbalance 
This method doesn't need any extra knowledge, and is thus suitable when we know nothing about the corpus  But this method assumes that the categories are uniformly distributed, which may not be the case in practice, and it will have a degraded performance if the assumption is violated (see Figure 1 (b), Figure 2 (b), Figure 3 (a)) 
The KL divergence performs much better on unbalanced corpora than other methods  The reason is that KL divergence utilizes the reference class distribution and doesn't make any assumptions  The fact suggests that additional knowledge does benefit the model 
However, the KL divergence term requires providing the true class distribution  Sometimes, we may have the exact knowledge about the true distribution, but sometimes we may not  Fortunately, the model is insensitive to the true distribution and therefore a rough estimation of the true distribution is sufficient  In our experiments, when the true class distribution is 1:2, where the reference class distribution is set to 1:1 5/1:2/1:2 5, the accuracy is 0 755/0 756/0 760 respectively  This provides us the possibility to perform simple computing on the corpus to obtain the distribution in reality  Or, we can set the distribution roughly with domain expertise 

ground_truth: Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP

+0.58

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How do they define robustness of a model?
Context 3: Introduction
Stance detection (also called stance identification or stance classification) is one of the considerably recent research topics in natural language processing (NLP)  It is usually defined as a classification problem where for a text and target pair, the stance of the author of the text for that target is expected as a classification output from the set: {Favor, Against, Neither} BIBREF0  
Stance detection is usually considered as a subtask of sentiment analysis (opinion mining) BIBREF1 topic in NLP  Both are mostly performed on social media texts, particularly on tweets, hence both are important components of social media analysis  Nevertheless, in sentiment analysis, the sentiment of the author of a piece of text usually as Positive, Negative, and Neutral is explored while in stance detection, the stance of the author of the text for a particular target (an entity, event, etc ) either explicitly or implicitly referred to in the text is considered  Like sentiment analysis, stance detection systems can be valuable components of information retrieval and other text analysis systems BIBREF0  
Previous work on stance detection include BIBREF2 where a stance classifier based on sentiment and arguing features is proposed in addition to an arguing lexicon automatically compiled  The ultimate approach performs better than distribution-based and uni-gram-based baseline systems BIBREF2   In BIBREF3 , the authors show that the use of dialogue structure improves stance detection in on-line debates  In BIBREF4 , Hasan and Ng carry out stance detection experiments using different machine learning algorithms, training data sets, features, and inter-post constraints in on-line debates, and draw insightful conclusions based on these experiments  For instance, they find that sequence models like HMMs perform better at stance detection when compared with non-sequence models like Naive Bayes (NB) BIBREF4   In another related study BIBREF5 , the authors conclude that topic-independent features can be exploited for disagreement detection in on-line dialogues  The employed features include agreement, cue words, denial, hedges, duration, polarity, and punctuation BIBREF5   Stance detection on a corpus of student essays is considered in BIBREF6   After using linguistically-motivated feature sets together with multivalued NB and SVM as the learning models, the authors conclude that they outperform two baseline approaches BIBREF6   In BIBREF7 , the author claims that Wikipedia can be used to determine stances about controversial topics based on their previous work regarding controversy extraction on the Web 
Among more recent related work, in BIBREF8 stance detection for unseen targets is studied and bidirectional conditional encoding is employed  The authors state that their approach achieves state-of-the art performance rates BIBREF8 on SemEval 2016 Twitter Stance Detection corpus BIBREF0   In BIBREF9 , a stance-community detection approach called SCIFNET is proposed  SCIFNET creates networks of people who are stance targets, automatically from the related document collections BIBREF9 using stance expansion and refinement techniques to arrive at stance-coherent networks  A tweet data set annotated with stance information regarding six predefined targets is proposed in BIBREF10 where this data set is annotated through crowdsourcing  The authors indicate that the data set is also annotated with sentiment information in addition to stance, so it can help reveal associations between stance and sentiment BIBREF10   Lastly, in BIBREF0 , SemEval 2016's aforementioned shared task on Twitter Stance Detection is described  Also provided are the results of the evaluations of 19 systems participating in two subtasks (one with training data set provided and the other without an annotated data set) of the shared task BIBREF0  
In this paper, we present a tweet data set in Turkish annotated with stance information, where the corresponding annotations are made publicly available  The domain of the tweets comprises two popular football clubs which constitute the targets of the tweets included  We also provide the evaluation results of SVM classifiers (for each target) on this data set using unigram, bigram, and hashtag features 
To the best of our knowledge, the current study is the first one to target at stance detection in Turkish tweets  Together with the provided annotated data set and the corresponding evaluations with the aforementioned SVM classifiers which can be used as baseline systems, our study will hopefully help increase social media analysis studies on Turkish content 
The rest of the paper is organized as follows: In Section SECREF2 , we describe our tweet data set annotated with the target and stance information  Section SECREF3 includes the details of our SVM-based stance classifiers and their evaluation results with discussions  Section SECREF4 includes future research topics based on the current study, and finally Section SECREF5 concludes the paper with a summary 
A Stance Detection Data Set
We have decided to consider tweets about popular sports clubs as our domain for stance detection  Considerable amounts of tweets are being published for sports-related events at every instant  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey  As is the case for the sentiment analysis tools, the outputs of the stance detection systems on a stream of tweets about these clubs can facilitate the use of the opinions of the football followers by these clubs 
In a previous study on the identification of public health-related tweets, two tweet data sets in Turkish (each set containing 1 million random tweets) have been compiled where these sets belong to two different periods of 20 consecutive days BIBREF11   We have decided to use one of these sets (corresponding to the period between August 18 and September 6, 2015) and firstly filtered the tweets using the possible names used to refer to the target clubs  Then, we have annotated the stance information in the tweets for these targets as Favor or Against  Within the course of this study, we have not considered those tweets in which the target is not explicitly mentioned, as our initial filtering process reveals 
For the purposes of the current study, we have not annotated any tweets with the Neither class  This stance class and even finer-grained classes can be considered in further annotation studies  We should also note that in a few tweets, the target of the stance was the management of the club while in some others a particular footballer of the club is praised or criticised  Still, we have considered the club as the target of the stance in all of the cases and carried out our annotations accordingly 
At the end of the annotation process, we have annotated 700 tweets, where 175 tweets are in favor of and 175 tweets are against Target-1, and similarly 175 tweets are in favor of and 175 are against Target-2  Hence, our data set is a balanced one although it is currently limited in size  The corresponding stance annotations are made publicly available at http://ceng

ground_truth: ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced

Low sensitivity to bias in prior knowledge

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Which sports clubs are the targets?
Context 3: metu edu tr/ INLINEFORM0 e120329/ Turkish_Stance_Detection_Tweet_Dataset csv in Comma Separated Values (CSV) format  The file contains three columns with the corresponding headers  The first column is the tweet id of the corresponding tweet, the second column contains the name of the stance target, and the last column includes the stance of the tweet for the target as Favor or Against 
To the best of our knowledge, this is the first publicly-available stance-annotated data set for Turkish  Hence, it is a significant resource as there is a scarcity of annotated data sets, linguistic resources, and NLP tools available for Turkish  Additionally, to the best of our knowledge, it is also significant for being the first stance-annotated data set including sports-related tweets, as previous stance detection data sets mostly include on-line texts on political/ethical issues 
Stance Detection Experiments Using SVM Classifiers
It is emphasized in the related literature that unigram-based methods are reliable for the stance detection task BIBREF2 and similarly unigram-based models have been used as baseline models in studies such as BIBREF0   In order to be used as a baseline and reference system for further studies on stance detection in Turkish tweets, we have trained two SVM classifiers (one for each target) using unigrams as features  Before the extraction of unigrams, we have employed automated preprocessing to filter out the stopwords in our annotated data set of 700 tweets  The stopword list used is the list presented in BIBREF12 which, in turn, is the slightly extended version of the stopword list provided in BIBREF13  
We have used the SVM implementation available in the Weka data mining application BIBREF14 where this particular implementation employs the SMO algorithm BIBREF15 to train a classifier with a linear kernel  The 10-fold cross-validation results of the two classifiers are provided in Table TABREF1 using the metrics of precision, recall, and F-Measure 
The evaluation results are quite favorable for both targets and particularly higher for Target-1, considering the fact that they are the initial experiments on the data set  The performance of the classifiers is better for the Favor class for both targets when compared with the performance results for the Against class  This outcome may be due to the common use of some terms when expressing positive stance towards sports clubs in Turkish tweets  The same percentage of common terms may not have been observed in tweets during the expression of negative stances towards the targets  Yet, completely the opposite pattern is observed in stance detection results of baseline systems given in BIBREF0 , i e , better F-Measure rates have been obtained for the Against class when compared with the Favor class BIBREF0   Some of the baseline systems reported in BIBREF0 are SVM-based systems using unigrams and ngrams as features similar to our study, but their data sets include all three stance classes of Favor, Against, and Neither, while our data set comprises only tweets classified as belonging to Favor or Against classes  Another difference is that the data sets in BIBREF0 have been divided into training and test sets, while in our study we provide 10-fold cross-validation results on the whole data set  On the other hand, we should also note that SVM-based sentiment analysis systems (such as those given in BIBREF16 ) have been reported to achieve better F-Measure rates for the Positive sentiment class when compared with the results obtained for the Negative class  Therefore, our evaluation results for each stance class seem to be in line with such sentiment analysis systems  Yet, further experiments on the extended versions of our data set should be conducted and the results should again be compared with the stance detection results given in the literature 
We have also evaluated SVM classifiers which use only bigrams as features, as ngram-based classifiers have been reported to perform better for the stance detection problem BIBREF0   However, we have observed that using bigrams as the sole features of the SVM classifiers leads to quite poor results  This observation may be due to the relatively limited size of the tweet data set employed  Still, we can conclude that unigram-based features lead to superior results compared to the results obtained using bigrams as features, based on our experiments on our data set  Yet, ngram-based features may be employed on the extended versions of the data set to verify this conclusion within the course of future work 
With an intention to exploit the contribution of hashtag use to stance detection, we have also used the existence of hashtags in tweets as an additional feature to unigrams  The corresponding evaluation results of the SVM classifiers using unigrams together the existence of hashtags as features are provided in Table TABREF2  
When the results given in Table TABREF2 are compared with the results in Table TABREF1 , a slight decrease in F-Measure (0 5%) for Target-1 is observed, while the overall F-Measure value for Target-2 has increased by 1 8%  Although we could not derive sound conclusions mainly due to the relatively small size of our data set, the increase in the performance of the SVM classifier Target-2 is an encouraging evidence for the exploitation of hashtags in a stance detection system  We leave other ways of exploiting hashtags for stance detection as a future work 
To sum up, our evaluation results are significant as reference results to be used for comparison purposes and provides evidence for the utility of unigram-based and hashtag-related features in SVM classifiers for the stance detection problem in Turkish tweets 
Future Prospects
Future work based on the current study includes the following:
Conclusion
Stance detection is a considerably new research area in natural language processing and is considered within the scope of the well-studied topic of sentiment analysis  It is the detection of stance within text towards a target which may be explicitly specified in the text or not  In this study, we present a stance-annotated tweet data set in Turkish where the targets of the annotated stances are two popular sports clubs in Turkey  The corresponding annotations are made publicly-available for research purposes  To the best of our knowledge, this is the first stance detection data set for the Turkish language and also the first sports-related stance-annotated data set  Also presented in this study are SVM classifiers (one for each target) utilizing unigram and bigram features in addition to using the existence of hashtags as another feature  10-fold cross validation results of these classifiers are presented which can be used as reference results by prospective systems  Both the annotated data set and the classifiers with evaluations are significant since they are the initial contributions to stance detection problem in Turkish tweets 

ground_truth: Galatasaray, Fenerbahçe

Galatasaray , Fenerbahçe