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 type of classifiers are used?
Context 1: Introduction
Event detection on microblogging platforms such as Twitter aims to detect events preemptively  A main task in event detection is detecting events of predetermined types BIBREF0, such as concerts or controversial events based on microposts matching specific event descriptions  This task has extensive applications ranging from cyber security BIBREF1, BIBREF2 to political elections BIBREF3 or public health BIBREF4, BIBREF5  Due to the high ambiguity and inconsistency of the terms used in microposts, event detection is generally performed though statistical machine learning models, which require a labeled dataset for model training  Data labeling is, however, a long, laborious, and usually costly process  For the case of micropost classification, though positive labels can be collected (e g , using specific hashtags, or event-related date-time information), there is no straightforward way to generate negative labels useful for model training  To tackle this lack of negative labels and the significant manual efforts in data labeling, BIBREF1 (BIBREF1, BIBREF3) introduced a weak supervision based learning approach, which uses only positively labeled data, accompanied by unlabeled examples by filtering microposts that contain a certain keyword indicative of the event type under consideration (e g , `hack' for cyber security)  Another key technique in this context is expectation regularization BIBREF6, BIBREF7, BIBREF1  Here, the estimated proportion of relevant microposts in an unlabeled dataset containing a keyword is given as a keyword-specific expectation  This expectation is used in the regularization term of the model's objective function to constrain the posterior distribution of the model predictions  By doing so, the model is trained with an expectation on its prediction for microposts that contain the keyword  Such a method, however, suffers from two key problems:
Due to the unpredictability of event occurrences and the constantly changing dynamics of users' posting frequency BIBREF8, estimating the expectation associated with a keyword is a challenging task, even for domain experts;
The performance of the event detection model is constrained by the informativeness of the keyword used for model training  As of now, we lack a principled method for discovering new keywords and improve the model performance 
To address the above issues, we advocate a human-AI loop approach for discovering informative keywords and estimating their expectations reliably  Our approach iteratively leverages 1) crowd workers for estimating keyword-specific expectations, and 2) the disagreement between the model and the crowd for discovering new informative keywords  More specifically, at each iteration after we obtain a keyword-specific expectation from the crowd, we train the model using expectation regularization and select those keyword-related microposts for which the model's prediction disagrees the most with the crowd's expectation; such microposts are then presented to the crowd to identify new keywords that best explain the disagreement  By doing so, our approach identifies new keywords which convey more relevant information with respect to existing ones, thus effectively boosting model performance  By exploiting the disagreement between the model and the crowd, our approach can make efficient use of the crowd, which is of critical importance in a human-in-the-loop context BIBREF9, BIBREF10  An additional advantage of our approach is that by obtaining new keywords that improve model performance over time, we are able to gain insight into how the model learns for specific event detection tasks  Such an advantage is particularly useful for event detection using complex models, e g , deep neural networks, which are intrinsically hard to understand BIBREF11, BIBREF12  An additional challenge in involving crowd workers is that their contributions are not fully reliable BIBREF13  In the crowdsourcing literature, this problem is usually tackled with probabilistic latent variable models BIBREF14, BIBREF15, BIBREF16, which are used to perform truth inference by aggregating a redundant set of crowd contributions  Our human-AI loop approach improves the inference of keyword expectation by aggregating contributions not only from the crowd but also from the model  This, however, comes with its own challenge as the model's predictions are further dependent on the results of expectation inference, which is used for model training  To address this problem, we introduce a unified probabilistic model that seamlessly integrates expectation inference and model training, thereby allowing the former to benefit from the latter while resolving the inter-dependency between the two 
To the best of our knowledge, we are the first to propose a human-AI loop approach that iteratively improves machine learning models for event detection  In summary, our work makes the following key contributions:
A novel human-AI loop approach for micropost event detection that jointly discovers informative keywords and estimates their expectation;
A unified probabilistic model that infers keyword expectation and simultaneously performs model training;
An extensive empirical evaluation of our approach on multiple real-world datasets demonstrating that our approach significantly improves the state of the art by an average of 24 3% AUC 
The rest of this paper is organized as follows  First, we present our human-AI loop approach in Section SECREF2  Subsequently, we introduce our proposed probabilistic model in Section SECREF3  The experimental setup and results are presented in Section SECREF4  Finally, we briefly cover related work in Section SECREF5 before concluding our work in Section SECREF6 
The Human-AI Loop Approach
Given a set of labeled and unlabeled microposts, our goal is to extract informative keywords and estimate their expectations in order to train a machine learning model  To achieve this goal, our proposed human-AI loop approach comprises two crowdsourcing tasks, i e , micropost classification followed by keyword discovery, and a unified probabilistic model for expectation inference and model training  Figure FIGREF6 presents an overview of our approach  Next, we describe our approach from a process-centric perspective 
Following previous studies BIBREF1, BIBREF17, BIBREF2, we collect a set of unlabeled microposts $\mathcal {U}$ from a microblogging platform and post-filter, using an initial (set of) keyword(s), those microposts that are potentially relevant to an event category  Then, we collect a set of event-related microposts (i e , positively labeled microposts) $\mathcal {L}$, post-filtering with a list of seed events  $\mathcal {U}$ and $\mathcal {L}$ are used together to train a discriminative model (e g , a deep neural network) for classifying the relevance of microposts to an event  We denote the target model as $p_\theta (y|x)$, where $\theta $ is the model parameter to be learned and $y$ is the label of an arbitrary micropost, represented by a bag-of-words vector $x$  Our approach iterates several times $t=\lbrace 1, 2, \ldots \rbrace $ until the performance of the target model converges  Each iteration starts from the initial keyword(s) or the new keyword(s) discovered in the previous iteration  Given such a keyword, denoted by $w^{(t)}$, the iteration starts by sampling microposts containing the keyword from $\mathcal {U}$, followed by dynamically creating micropost classification tasks and publishing them on a crowdsourcing platform 
Micropost Classification  The micropost classification task requires crowd workers to label the selected microposts into two classes: event-related and non event-related  In particular, workers are given instructions and examples to differentiate event-instance related microposts and general event-category related microposts  Consider, for example, the following microposts in the context of Cyber attack events, both containing the keyword `hack':
Credit firm Equifax says 143m Americans' social security numbers exposed in hack
This micropost describes an instance of a cyber attack event that the target model should identify  This is, therefore, an event-instance related micropost and should be considered as a positive example  Contrast this with the following example:
Companies need to step their cyber security up
This micropost, though related to cyber security in general, does not mention an instance of a cyber attack event, and is of no interest to us for event detection  This is an example of a general event-category related micropost and should be considered as a negative example 
In this task, each selected micropost is labeled by multiple crowd workers  The annotations are passed to our probabilistic model for expectation inference and model training 
Expectation Inference & Model Training  Our probabilistic model takes crowd-contributed labels and the model trained in the previous iteration as input  As output, it generates a keyword-specific expectation, denoted as $e^{(t)}$, and an improved version of the micropost classification model, denoted as $p_{\theta ^{(t)}}(y|x)$  The details of our probabilistic model are given in Section SECREF3 
Keyword Discovery  The keyword discovery task aims at discovering a new keyword (or a set of keywords) that is most informative for model training with respect to existing keywords  To this end, we first apply the current model $p_{\theta ^{(t)}}(y|x)$ on the unlabeled microposts $\mathcal {U}$  For those that contain the keyword $w^{(t)}$, we calculate the disagreement between the model predictions and the keyword-specific expectation $e^{(t)}$:
and select the ones with the highest disagreement for keyword discovery  These selected microposts are supposed to contain information that can explain the disagreement between the model prediction and keyword-specific expectation, and can thus provide information that is most different from the existing set of keywords for model training 
For instance, our study shows that the expectation for the keyword `hack' is 0 20, which means only 20% of the initial set of microposts retrieved with the keyword are event-related  A micropost selected with the highest disagreement (Eq  DISPLAY_FORM7), whose likelihood of being event-related as predicted by the model is $99 9\%$, is shown as an example below:
RT @xxx: Hong Kong securities brokers hit by cyber attacks, may face more: regulator #cyber #security #hacking https://t co/rC1s9CB
This micropost contains keywords that can better indicate the relevance to a cyber security event than the initial keyword `hack', e g , `securities', `hit', and `attack' 
Note that when the keyword-specific expectation $e^{(t)}$ in Equation DISPLAY_FORM7 is high, the selected microposts will be the ones that contain keywords indicating the irrelevance of the microposts to an event category  Such keywords are also useful for model training as they help improve the model's ability to identify irrelevant microposts 
To identify new keywords in the selected microposts, we again leverage crowdsourcing, as humans are typically better than machines at providing specific explanations BIBREF18, BIBREF19  In the crowdsourcing task, workers are first asked to find those microposts where the model predictions are deemed correct  Then, from those microposts, workers are asked to find the keyword that best indicates the class of the microposts as predicted by the model  The keyword most frequently identified by the workers is then used as the initial keyword for the following iteration  In case multiple keywords are selected, e g , the top-$N$ frequent ones, workers will be asked to perform $N$ micropost classification tasks for each keyword in the next iteration, and the model training will be performed on multiple keyword-specific expectations 
Unified Probabilistic Model
This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously  We start by formalizing the problem and introducing our model, before describing the model learning method 
Problem Formalization  We consider the problem at iteration $t$ where the corresponding keyword is $w^{(t)}$  In the current iteration, let $\mathcal {U}^{(t)} \subset \mathcal {U}$ denote the set of all microposts containing the keyword and $\mathcal {M}^{(t)}= \lbrace x_{m}\rbrace _{m=1}^M\subset \mathcal {U}^{(t)}$ be the randomly selected subset of $M$ microposts labeled by $N$ crowd workers $\mathcal {C} = \lbrace c_n\rbrace _{n=1}^N$  The annotations form a matrix $\mathbf {A}\in \mathbb {R}^{M\times N}$ where $\mathbf {A}_{mn}$ is the label for the micropost $x_m$ contributed by crowd worker $c_n$  Our goal is to infer the keyword-specific expectation $e^{(t)}$ and train the target model by learning the model parameter $\theta ^{(t)}$  An additional parameter of our probabilistic model is the reliability of crowd workers, which is essential when involving crowdsourcing  Following Dawid and Skene BIBREF14, BIBREF16, we represent the annotation reliability of worker $c_n$ by a latent confusion matrix $\pi ^{(n)}$, where the $rs$-th element $\pi _{rs}^{(n)}$ denotes the probability of $c_n$ labeling a micropost as class $r$ given the true class $s$ 
Unified Probabilistic Model ::: Expectation as Model Posterior
First, we introduce an expectation regularization technique for the weakly supervised learning of the target model $p_{\theta ^{(t)}}(y|x)$  In this setting, the objective function of the target model is composed of two parts, corresponding to the labeled microposts $\mathcal {L}$ and the unlabeled ones $\mathcal {U}$ 
The former part aims at maximizing the likelihood of the labeled microposts:
where we assume that $\theta $ is generated from a prior distribution (e g , Laplacian or Gaussian) parameterized by $\sigma $ 
To leverage unlabeled data for model training, we make use of the expectations of existing keywords, i e , {($w^{(1)}$, $e^{(1)}$),    , ($w^{(t-1)}$, $e^{(t-1)}$), ($w^{(t)}$, $e^{(t)}$)} (Note that $e^{(t)}$ is inferred), as a regularization term to constrain model training  To do so, we first give the model's expectation for each keyword $w^{(k)}$ ($1\le k\le t$) as follows:
which denotes the empirical expectation of the model’s posterior predictions on the unlabeled microposts $\mathcal {U}^{(k)}$ containing keyword $w^{(k)}$  Expectation regularization can then be formulated as the regularization of the distance between the Bernoulli distribution parameterized by the model's expectation and the expectation of the existing keyword:
where $D_{KL}[\cdot \Vert \cdot ]$ denotes the KL-divergence between the Bernoulli distributions $Ber(e^{(k)})$ and $Ber(\mathbb {E}_{x\sim \mathcal {U}^{(k)}}(y))$, and $\lambda $ controls the strength of expectation regularization 
Unified Probabilistic Model ::: Expectation as Class Prior
To learn the keyword-specific expectation $e^{(t)}$ and the crowd worker reliability $\pi ^{(n)}$ ($1\le n\le N$), we model the likelihood of the crowd-contributed labels $\mathbf {A}$ as a function of these parameters  In this context, we view the expectation as the class prior, thus performing expectation inference as the learning of the class prior  By doing so, we connect expectation inference with model training 
Specifically, we model the likelihood of an arbitrary crowd-contributed label $\mathbf {A}_{mn}$ as a mixture of multinomials where the prior is the keyword-specific expectation $e^{(t)}$:
where $e_s^{(t)}$ is the probability of the ground truth label being $s$ given the keyword-specific expectation as the class prior; $K$ is the set of possible ground truth labels (binary in our context); and $r=\mathbf {A}_{mn}$ is the crowd-contributed label  Then, for an individual micropost $x_m$, the likelihood of crowd-contributed labels $\mathbf {A}_{m:}$ is given by:
Therefore, the objective function for maximizing the likelihood of the entire annotation matrix $\mathbf {A}$ can be described as:
Unified Probabilistic Model ::: Unified Probabilistic Model
Integrating model training with expectation inference, the overall objective function of our proposed model is given by:
Figure FIGREF18 depicts a graphical representation of our model, which combines the target model for training (on the left) with the generative model for crowd-contributed labels (on the right) through a keyword-specific expectation 
Model Learning  Due to the unknown ground truth labels of crowd-annotated microposts ($y_m$ in Figure FIGREF18), we resort to expectation maximization for model learning  The learning algorithm iteratively takes two steps: the E-step and the M-step  The E-step infers the ground truth labels given the current model parameters  The M-step updates the model parameters, including the crowd reliability parameters $\pi ^{(n)}$ ($1\le n\le N$), the keyword-specific expectation $e^{(t)}$, and the parameter of the target model $\theta ^{(t)}$  The E-step and the crowd parameter update in the M-step are similar to the Dawid-Skene model BIBREF14  The keyword expectation is inferred by taking into account both the crowd-contributed labels and the model prediction:
The parameter of the target model is updated by gradient descent  For example, when the target model to be trained is a deep neural network, we use back-propagation with gradient descent to update the weight matrices 
Experiments and Results
This section presents our experimental setup and results for evaluating our approach  We aim at answering the following questions:
[noitemsep,leftmargin=*]
Q1: How effectively does our proposed human-AI loop approach enhance the state-of-the-art machine learning models for event detection?
Q2: How well does our keyword discovery method work compare to existing keyword expansion methods?
Q3: How effective is our approach using crowdsourcing at obtaining new keywords compared with an approach labelling microposts for model training under the same cost?
Q4: How much benefit does our unified probabilistic model bring compared to methods that do not take crowd reliability into account?
Experiments and Results ::: Experimental Setup
Datasets  We perform our experiments with two predetermined event categories: cyber security (CyberAttack) and death of politicians (PoliticianDeath)  These event categories are chosen as they are representative of important event types that are of interest to many governments and companies  The need to create our own dataset was motivated by the lack of public datasets for event detection on microposts  The few available datasets do not suit our requirements  For example, the publicly available Events-2012 Twitter dataset BIBREF20 contains generic event descriptions such as Politics, Sports, Culture etc  Our work targets more specific event categories BIBREF21  Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively  Unlabeled microposts are collected by using the keyword `hack' for CyberAttack, while for PoliticianDeath, we use a set of keywords related to `politician' and `death' (such as `bureaucrat', `dead' etc ) For each dataset, we randomly select 500 tweets from the unlabeled subset and manually label them for evaluation  Table TABREF25 shows key statistics from our two datasets 
Comparison Methods  To demonstrate the generality of our approach on different event detection models, we consider Logistic Regression (LR) BIBREF1 and Multilayer Perceptron (MLP) BIBREF2 as the target models  As the goal of our experiments is to demonstrate the effectiveness of our approach as a new model training technique, we use these widely used models  Also, we note that in our case other neural network models with more complex network architectures for event detection, such as the bi-directional LSTM BIBREF17, turn out to be less effective than a simple feedforward network  For both LR and MLP, we evaluate our proposed human-AI loop approach for keyword discovery and expectation estimation by comparing against the weakly supervised learning method proposed by BIBREF1 (BIBREF1) and BIBREF17 (BIBREF17) where only one initial keyword is used with an expectation estimated by an individual expert 
Parameter Settings  We empirically set optimal parameters based on a held-out validation set that contains 20% of the test data  These include the hyperparamters of the target model, those of our proposed probabilistic model, and the parameters used for training the target model  We explore MLP with 1, 2 and 3 hidden layers and apply a grid search in 32, 64, 128, 256, 512 for the dimension of the embeddings and that of the hidden layers  For the coefficient of expectation regularization, we follow BIBREF6 (BIBREF6) and set it to $\lambda =10 \times $ #labeled examples  For model training, we use the Adam BIBREF22 optimization algorithm for both models 
Evaluation  Following BIBREF1 (BIBREF1) and BIBREF3 (BIBREF3), we use accuracy and area under the precision-recall curve (AUC) metrics to measure the performance of our proposed approach  We note that due to the imbalance in our datasets (20% positive microposts in CyberAttack and 27% in PoliticianDeath), accuracy is dominated by negative examples; AUC, in comparison, better characterizes the discriminative power of the model 
Crowdsourcing  We chose Level 3 workers on the Figure-Eight crowdsourcing platform for our experiments  The inter-annotator agreement in micropost classification is taken into account through the EM algorithm  For keyword discovery, we filter keywords based on the frequency of the keyword being selected by the crowd  In terms of cost-effectiveness, our approach is motivated from the fact that crowdsourced data annotation can be expensive, and is thus designed with minimal crowd involvement  For each iteration, we selected 50 tweets for keyword discovery and 50 tweets for micropost classification per keyword  For a dataset with 80k tweets (e g , CyberAttack), our approach only requires to manually inspect 800 tweets (for 8 keywords), which is only 1% of the entire dataset 
Experiments and Results ::: Results of our Human-AI Loop (Q1)
Table TABREF26 reports the evaluation of our approach on both the CyberAttack and PoliticianDeath event categories  Our approach is configured such that each iteration starts with 1 new keyword discovered in the previous iteration 
Our approach improves LR by 5 17% (Accuracy) and 18 38% (AUC), and MLP by 10 71% (Accuracy) and 30 27% (AUC) on average  Such significant improvements clearly demonstrate that our approach is effective at improving model performance  We observe that the target models generally converge between the 7th and 9th iteration on both datasets when performance is measured by AUC  The performance can slightly degrade when the models are further trained for more iterations on both datasets  This is likely due to the fact that over time, the newly discovered keywords entail lower novel information for model training  For instance, for the CyberAttack dataset the new keyword in the 9th iteration `election' frequently co-occurs with the keyword `russia' in the 5th iteration (in microposts that connect Russian hackers with US elections), thus bringing limited new information for improving the model performance  As a side remark, we note that the models converge faster when performance is measured by accuracy  Such a comparison result confirms the difference between the metrics and shows the necessity for more keywords to discriminate event-related microposts from non event-related ones 
Experiments and Results ::: Comparative Results on Keyword Discovery (Q2)
Figure FIGREF31 shows the evaluation of our approach when discovering new informative keywords for model training (see Section SECREF2: Keyword Discovery)  We compare our human-AI collaborative way of discovering new keywords against a query expansion (QE) approach BIBREF23, BIBREF24 that leverages word embeddings to find similar words in the latent semantic space  Specifically, we use pre-trained word embeddings based on a large Google News dataset for query expansion  For instance, the top keywords resulting from QE for `politician' are, `deputy',`ministry',`secretary', and `minister'  For each of these keywords, we use the crowd to label a set of tweets and obtain a corresponding expectation 
We observe that our approach consistently outperforms QE by an average of $4 62\%$ and $52 58\%$ AUC on CyberAttack and PoliticianDeath, respectively  The large gap between the performance improvements for the two datasets is mainly due to the fact that microposts that are relevant for PoliticianDeath are semantically more complex than those for CyberAttack, as they encode noun-verb relationship (e g , “the king of     died    ”) rather than a simple verb (e g , “    hacked ”) for the CyberAttack microposts  QE only finds synonyms of existing keywords related to either `politician' or `death', however cannot find a meaningful keyword that fully characterizes the death of a politician  For instance, QE finds the keywords `kill' and `murder', which are semantically close to `death' but are not specifically relevant to the death of a politician  Unlike QE, our approach identifies keywords that go beyond mere synonyms and that are more directly related to the end task, i e , discriminating event-related microposts from non related ones  Examples are `demise' and `condolence'  As a remark, we note that in Figure FIGREF31(b), the increase in QE performance on PoliticianDeath is due to the keywords `deputy' and `minister', which happen to be highly indicative of the death of a politician in our dataset; these keywords are also identified by our approach 
Experiments and Results ::: Cost-Effectiveness Results (Q3)
To demonstrate the cost-effectiveness of using crowdsourcing for obtaining new keywords and consequently, their expectations, we compare the performance of our approach with an approach using crowdsourcing to only label microposts for model training at the same cost  Specifically, we conducted an additional crowdsourcing experiment where the same cost used for keyword discovery in our approach is used to label additional microposts for model training  These newly labeled microposts are used with the microposts labeled in the micropost classification task of our approach (see Section SECREF2: Micropost Classification) and the expectation of the initial keyword to train the model for comparison  The model trained in this way increases AUC by 0 87% for CyberAttack, and by 1 06% for PoliticianDeath; in comparison, our proposed approach increases AUC by 33 42% for PoliticianDeath and by 15 23% for CyberAttack over the baseline presented by BIBREF1)  These results show that using crowdsourcing for keyword discovery is significantly more cost-effective than simply using crowdsourcing to get additional labels when training the model 
Experiments and Results ::: Expectation Inference Results (Q4)
To investigate the effectiveness of our expectation inference method, we compare it against a majority voting approach, a strong baseline in truth inference BIBREF16  Figure FIGREF36 shows the result of this evaluation  We observe that our approach results in better models for both CyberAttack and PoliticianDeath  Our manual investigation reveals that workers' annotations are of high reliability, which explains the relatively good performance of majority voting  Despite limited margin for improvement, our method of expectation inference improves the performance of majority voting by $0 4\%$ and $1 19\%$ AUC on CyberAttack and PoliticianDeath, respectively 
Related Work
Event Detection  The techniques for event extraction from microblogging platforms can be classified according to their domain specificity and their detection method BIBREF0  Early works mainly focus on open domain event detection BIBREF25, BIBREF26, BIBREF27  Our work falls into the category of domain-specific event detection BIBREF21, which has drawn increasing attention due to its relevance for various applications such as cyber security BIBREF1, BIBREF2 and public health BIBREF4, BIBREF5  In terms of technique, our proposed detection method is related to the recently proposed weakly supervised learning methods BIBREF1, BIBREF17, BIBREF3  This comes in contrast with fully-supervised learning methods, which are often limited by the size of the training data (e g , a few hundred examples) BIBREF28, BIBREF29 
Human-in-the-Loop Approaches  Our work extends weakly supervised learning methods by involving humans in the loop BIBREF13  Existing human-in-the-loop approaches mainly leverage crowds to label individual data instances BIBREF9, BIBREF10 or to debug the training data BIBREF30, BIBREF31 or components BIBREF32, BIBREF33, BIBREF34 of a machine learning system  Unlike these works, we leverage crowd workers to label sampled microposts in order to obtain keyword-specific expectations, which can then be generalized to help classify microposts containing the same keyword, thus amplifying the utility of the crowd  Our work is further connected to the topic of interpretability and transparency of machine learning models BIBREF11, BIBREF35, BIBREF12, for which humans are increasingly involved, for instance for post-hoc evaluations of the model's interpretability  In contrast, our approach directly solicits informative keywords from the crowd for model training, thereby providing human-understandable explanations for the improved model 
Conclusion
In this paper, we presented a new human-AI loop approach for keyword discovery and expectation estimation to better train event detection models  Our approach takes advantage of the disagreement between the crowd and the model to discover informative keywords and leverages the joint power of the crowd and the model in expectation inference  We evaluated our approach on real-world datasets and showed that it significantly outperforms the state of the art and that it is particularly useful for detecting events where relevant microposts are semantically complex, e g , the death of a politician  As future work, we plan to parallelize the crowdsourcing tasks and optimize our pipeline in order to use our event detection approach in real-time 
Acknowledgements
This project has received funding from the Swiss National Science Foundation (grant #407540_167320 Tighten-it-All) and from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 683253/GraphInt) 
Context 2: Introduction
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2   As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotated by experts  In real applications, we often need to consider new types of entities in new domains where we do not have existing annotated data  For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money 
In order to quickly obtain new training data, we can use crowdsourcing as one alternative way at lower cost in a short time  But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts  It is one biggest challenge to build a powerful NER system on such a low quality annotated data  Although we can obtain high quality annotations for each input sentence by majority voting, it can be a waste of human labors to achieve such a goal, especially for some ambiguous sentences which may require a number of annotations to reach an agreement  Thus majority work directly build models on crowd annotations, trying to model the differences among annotators, for example, some of the annotators may be more trustful BIBREF3 , BIBREF4  
Here we focus mainly on the Chinese NER, which is more difficult than NER for other languages such as English for the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation  The Chinese NE taggers trained on news domain often perform poor in other domains  Although we can alleviate the problem by using character-level tagging to resolve the problem of poor word segmentation performances BIBREF5 , still there exists a large gap when the target domain changes, especially for the texts of social media  Thus, in order to get a good tagger for new domains and also for the conditions of new entity types, we require large amounts of labeled data  Therefore, crowdsourcing is a reasonable solution for these situations 
In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data  Our goal is to extract additional annotator independent features by adversarial training, alleviating the annotation noises of non-experts  The idea of adversarial training in neural networks has been used successfully in several NLP tasks, such as cross-lingual POS tagging BIBREF6 and cross-domain POS tagging BIBREF7   They use it to reduce the negative influences of the input divergences among different domains or languages, while we use adversarial training to reduce the negative influences brought by different crowd annotators  To our best knowledge, we are the first to apply adversarial training for crowd annotation learning 
In the learning framework, we perform adversarial training between the basic NER and an additional worker discriminator  We have a common Bi-LSTM for representing annotator-generic information and a private Bi-LSTM for representing annotator-specific information  We build another label Bi-LSTM by the crowd-annotated NE label sequence which reflects the mind of the crowd annotators who learn entity definitions by reading the annotation guidebook  The common and private Bi-LSTMs are used for NER, while the common and label Bi-LSTMs are used as inputs for the worker discriminator  The parameters of the common Bi-LSTM are learned by adversarial training, maximizing the worker discriminator loss and meanwhile minimizing the NER loss  Thus the resulting features of the common Bi-LSTM are worker invariant and NER sensitive 
For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce  We require the crowd annotators to label the types of entities, including person, song, brand, product, and so on  Identifying these entities is useful for chatbot and e-commerce platforms BIBREF8   Then we conduct experiments on the newly created datasets to verify the effectiveness of the proposed adversarial neural network model  The results show that our system outperforms very strong baseline systems  In summary, we make the following contributions:
Related Work
Our work is related to three lines of research: Sequence labeling, Adversarial training, and Crowdsourcing 
Sequence labeling  NER is widely treated as a sequence labeling problem, by assigning a unique label over each sentential word BIBREF9   Early studies on sequence labeling often use the models of HMM, MEMM, and CRF BIBREF10 based on manually-crafted discrete features, which can suffer the feature sparsity problem and require heavy feature engineering  Recently, neural network models have been successfully applied to sequence labeling BIBREF1 , BIBREF11 , BIBREF2   Among these work, the model which uses Bi-LSTM for feature extraction and CRF for decoding has achieved state-of-the-art performances BIBREF11 , BIBREF2 , which is exploited as the baseline model in our work 
Adversarial Training  Adversarial Networks have achieved great success in computer vision such as image generation BIBREF12 , BIBREF13   In the NLP community, the method is mainly exploited under the settings of domain adaption BIBREF14 , BIBREF7 , cross-lingual BIBREF15 , BIBREF6 and multi-task learning BIBREF16 , BIBREF17   All these settings involve the feature divergences between the training and test examples, and aim to learn invariant features across the divergences by an additional adversarial discriminator, such as domain discriminator  Our work is similar to these work but is applies on crowdsourcing learning, aiming to find invariant features among different crowdsourcing workers 
Crowdsourcing  Most NLP tasks require a massive amount of labeled training data which are annotated by experts  However, hiring experts is costly and non-scalable, both in terms of time and money  Instead, crowdsourcing is another solution to obtain labeled data at a lower cost but with relative lower quality than those from experts  BIBREF18 snow2008cheap collected labeled results for several NLP tasks from Amazon Mechanical Turk and demonstrated that non-experts annotations were quite useful for training new systems  In recent years, a series of work have focused on how to use crowdsourcing data efficiently in tasks such as classification BIBREF19 , BIBREF20 , and compare quality of crowd and expert labels BIBREF21  
In sequence labeling tasks, BIBREF22 dredze2009sequence viewed this task as a multi-label problem while BIBREF3 rodrigues2014sequence took workers identities into account by assuming that each sentential word was tagged correctly by one of the crowdsourcing workers and proposed a CRF-based model with multiple annotators  BIBREF4 nguyen2017aggregating introduced a crowd representation in which the crowd vectors were added into the LSTM-CRF model at train time, but ignored them at test time  In this paper, we apply adversarial training on crowd annotations on Chinese NER in new domains, and achieve better performances than previous studies on crowdsourcing learning 
Baseline: LSTM-CRF
We use a neural CRF model as the baseline system BIBREF9 , treating NER as a sequence labeling problem over Chinese characters, which has achieved state-of-the-art performances BIBREF5   To this end, we explore the BIEO schema to convert NER into sequence labeling, following BIBREF2 lample-EtAl:2016:N16-1, where sentential character is assigned with one unique tag  Concretely, we tag the non-entity character by label “O”, the beginning character of an entity by “B-XX”, the ending character of an entity by “E-XX” and the other character of an entity by “I-XX”, where “XX” denotes the entity type 
We build high-level neural features from the input character sequence by a bi-directional LSTM BIBREF2   The resulting features are combined and then are fed into an output CRF layer for decoding  In summary, the baseline model has three main components  First, we make vector representations for sentential characters $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ , transforming the discrete inputs into low-dimensional neural inputs  Second, feature extraction is performed to obtain high-level features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ , by using a bi-directional LSTM (Bi-LSTM) structure together with a linear transformation over $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$   Third, we apply a CRF tagging module over $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ , obtaining the final output NE labels  The overall framework of the baseline model is shown by the right part of Figure 1  
Vector Representation of Characters
To represent Chinese characters, we simply exploit a neural embedding layer to map discrete characters into the low-dimensional vector representations  The goal is achieved by a looking-up table $\mathbf {E}^W$ , which is a model parameter and will be fine-tuned during training  The looking-up table can be initialized either by random or by using a pretrained embeddings from large scale raw corpus  For a given Chinese character sequence $c_1c_2\cdots c_n$ , we obtain the vector representation of each sentential character by: $ \mathbf {x}_t = \text{look-up}(c_t, \mathbf {E}^W), \text{~~~} t \in [1, n]$  
Feature Extraction
Based on the vector sequence $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ , we extract higher-level features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ by using a bidirectional LSTM module and a simple feed-forward neural layer, which are then used for CRF tagging at the next step 
LSTM is a type of recurrent neural network (RNN), which is designed for solving the exploding and diminishing gradients of basic RNNs BIBREF23   It has been widely used in a number of NLP tasks, including POS-tagging BIBREF11 , BIBREF24 , parsing BIBREF25 and machine translation BIBREF26 , because of its strong capabilities of modeling natural language sentences 
By traversing $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ by order and reversely, we obtain the output features $\mathbf {h}_1^{\text{private}}\mathbf {h}_2^{\text{private}}\cdots \mathbf {h}_n^{\text{private}}$ of the bi-LSTM, where $\mathbf {h}_t^{\text{private}} = \overrightarrow{\mathbf {h}}_t \oplus \overleftarrow{\mathbf {h}}_t $   Here we refer this Bi-LSTM as private in order to differentiate it with the common Bi-LSTM over the same character inputs which will be introduced in the next section 
Further we make an integration of the output vectors of bi-directional LSTM by a linear feed-forward neural layer, resulting in the features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ by equation:
$$\mathbf {h}_t^{\text{ner}} = \mathbf {W} \mathbf {h}_t^{\text{private}} + \mathbf {b},$$   (Eq  6)
where $\mathbf {W}$ and $\mathbf {b}$ are both model parameters 
CRF Tagging
Finally we feed the resulting features $\mathbf {h}_t^{\text{ner}}, t\in [1, n]$ into a CRF layer directly for NER decoding  CRF tagging is one globally normalized model, aiming to find the best output sequence considering the dependencies between successive labels  In the sequence labeling setting for NER, the output label of one position has a strong dependency on the label of the previous position  For example, the label before “I-XX” must be either “B-XX” or “I-XX”, where “XX” should be exactly the same 
CRF involves two parts for prediction  First we should compute the scores for each label based $\mathbf {h}_t^{\text{ner}}$ , resulting in $\mathbf {o}_t^{\text{ner}}$ , whose dimension is the number of output labels  The other part is a transition matrix $\mathbf {T}$ which defines the scores of two successive labels  $\mathbf {T}$ is also a model parameter  Based on $\mathbf {o}_t^{\text{ner}}$ and $\mathbf {T}$ , we use the Viterbi algorithm to find the best-scoring label sequence 
We can formalize the CRF tagging process as follows:
$$\begin{split} & \mathbf {o}_t^{\text{ner}} = \mathbf {W}^{\text{ner}} \mathbf {h}_t^{\text{ner}}, \text{~~~~} t \in [1,n] \\ & \text{score}(\mathbf {X}, \mathbf {y}) = \sum _{t = 1}^{n}(\mathbf {o}_{t,y_t} + T_{y_{t-1},y_t}) \\ & \mathbf {y}^{\text{ner}} = \mathop {arg~max}_{\mathbf {y}}\big (\text{score}(\mathbf {X}, \mathbf {y}))\big ), \\ \end{split}$$   (Eq  8)
where $\text{score}(\cdot )$ is the scoring function for a given output label sequence $\mathbf {y} = y_1y_2 \cdots y_n$ based on input $\mathbf {X}$ , $\mathbf {y}^{\text{ner}}$ is the resulting label sequence, $\mathbf {W}^{\text{ner}}$ is a model parameter 
Training
To train model parameters, we exploit a negative log-likelihood objective as the loss function  We apply softmax over all candidate output label sequences, thus the probability of the crowd-annotated label sequence is computed by:
$$p(\mathbf {\bar{y}}|\mathbf {X}) = \frac{\exp \big (\text{score}(\mathbf {X}, \mathbf {\bar{y}})\big )}{\sum _{\mathbf {y} \in \mathbf {Y}_{\mathbf {X}}} \exp \big (\text{score}(\mathbf {X}, \mathbf {y})\big )},$$   (Eq  10)
where $\mathbf {\bar{y}}$ is the crowd-annotated label sequences and $\mathbf {Y}_{\mathbf {X}}$ is all candidate label sequence of input $\mathbf {X}$  
Based on the above formula, the loss function of our baseline model is:
$$\text{loss}(\Theta , \mathbf {X}, \mathbf {\bar{y}}) = -\log p(\mathbf {\bar{y}}|\mathbf {X}),$$   (Eq  11)
where $\Theta $ is the set of all model parameters  We use standard back-propagation method to minimize the loss function of the baseline CRF model 
Worker Adversarial
Adversarial learning has been an effective mechanism to resolve the problem of the input features between the training and test examples having large divergences BIBREF27 , BIBREF13   It has been successfully applied on domain adaption BIBREF7 , cross-lingual learning BIBREF15 and multi-task learning BIBREF17   All settings involve feature shifting between the training and testing 
In this paper, our setting is different  We are using the annotations from non-experts, which are noise and can influence the final performances if they are not properly processed  Directly learning based on the resulting corpus may adapt the neural feature extraction into the biased annotations  In this work, we assume that individual workers have their own guidelines in mind after short training  For example, a perfect worker can annotate highly consistently with an expert, while common crowdsourcing workers may be confused and have different understandings on certain contexts  Based on the assumption, we make an adaption for the original adversarial neural network to our setting 
Our adaption is very simple  Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs, for example, the domain category in the domain adaption setting, while we add a discriminator to classify the annotation workers  Solely the features from the input sentence is not enough for worker classification  The annotation result of the worker is also required  Thus the inputs of our discriminator are different  Here we exploit both the source sentences and the crowd-annotated NE labels as basic inputs for the worker discrimination 
In the following, we describe the proposed adversarial learning module, including both the submodels and the training method  As shown by the left part of Figure 1 , the submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction 
Common Bi-LSTM over Characters
To build the adversarial part, first we create a new bi-directional LSTM, named by the common Bi-LSTM:
$$\mathbf {h}_1^{\text{\tiny common}} \mathbf {h}_2^{\text{\tiny common}} \cdots \mathbf {h}_n^{\text{\tiny common}} = \text{Bi-LSTM}(\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n) $$   (Eq  13)
As shown in Figure 1 , this Bi-LSTM is constructed over the same input character representations of the private Bi-LSTM, in order to extract worker independent features 
The resulting features of the common Bi-LSTM are used for both NER and the worker discriminator, different with the features of private Bi-LSTM which are used for NER only  As shown in Figure 1 , we concatenate the outputs of the common and private Bi-LSTMs together, and then feed the results into the feed-forward combination layer of the NER part  Thus Formula 6 can be rewritten as:
$$\mathbf {h}_t^{\text{ner}} = \mathbf {W} (\mathbf {h}_t^{\text{common}} \oplus \mathbf {h}_t^{\text{private}}) + \mathbf {b},$$   (Eq  14)
where $\mathbf {W}$ is wider than the original combination because the newly-added $\mathbf {h}_t^{\text{common}}$  
Noticeably, although the resulting common features are used for the worker discriminator, they actually have no capability to distinguish the workers  Because this part is exploited to maximize the loss of the worker discriminator, it will be interpreted in the later training subsection  These features are invariant among different workers, thus they can have less noises for NER  This is the goal of adversarial learning, and we hope the NER being able to find useful features from these worker independent features 
Additional Bi-LSTM over Annotated NER Labels
In order to incorporate the annotated NE labels to predict the exact worker, we build another bi-directional LSTM (named by label Bi-LSTM) based on the crowd-annotated NE label sequence  This Bi-LSTM is used for worker discriminator only  During the decoding of the testing phase, we will never have this Bi-LSTM, because the worker discriminator is no longer required 
Assuming the crowd-annotated NE label sequence annotated by one worker is $\mathbf {\bar{y}} = \bar{y}_1\bar{y}_2 \cdots \bar{y}_n$ , we exploit a looking-up table $\mathbf {E}^{L}$ to obtain the corresponding sequence of their vector representations $\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n$ , similar to the method that maps characters into their neural representations  Concretely, for one NE label $\bar{y}_t$ ( $t \in [1, n]$ ), we obtain its neural vector by: $\mathbf {x^{\prime }}_t = \text{look-up}(\bar{y}_t, \mathbf {E}^L)$  
Next step we apply bi-directional LSTM over the sequence $\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n$ , which can be formalized as:
$$\mathbf {h}_1^{\text{label}} \mathbf {h}_2^{\text{label}} \cdots \mathbf {h}_n^{\text{label}} = \text{Bi-LSTM}(\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n) $$   (Eq  16)
The resulting feature sequence is concatenated with the outputs of the common Bi-LSTM, and further be used for worker classification 
CNN
Following, we add a convolutional neural network (CNN) module based on the concatenated outputs of the common Bi-LSTM and the label Bi-LSTM, to produce the final features for worker discriminator  A convolutional operator with window size 5 is used, and then max pooling strategy is applied over the convolution sequence to obtain the final fixed-dimensional feature vector  The whole process can be described by the following equations:
$$\begin{split} &\mathbf {h}_t^{\text{worker}} = \mathbf {h}_t^{\text{common}} \oplus \mathbf {h}_t^{\text{label}} \\ &\mathbf {\tilde{h}}_t^{\text{worker}} = \tanh (\mathbf {W}^{\text{cnn}}[\mathbf {h}_{t-2}^{\text{worker}}, \mathbf {h}_{t-1}^{\text{worker}}, \cdots , \mathbf {h}_{t+2}^{\text{worker}}]) \\ &\mathbf {h}^{\text{worker}} = \text{max-pooling}(\mathbf {\tilde{h}}_1^{\text{worker}}\mathbf {\tilde{h}}_2^{\text{worker}} \cdots \mathbf {\tilde{h}}_n^{\text{worker}}) \\ \end{split}$$   (Eq  18)
where $t \in [1,n]$ and $\mathbf {W}^{\text{cnn}}$ is one model parameter  We exploit zero vector to paddle the out-of-index vectors 
Output and Prediction
After obtaining the final feature vector for the worker discriminator, we use it to compute the output vector, which scores all the annotation workers  The score function is defined by:
$$\mathbf {o}^{\text{worker}} = \mathbf {W}^{\text{worker}} \mathbf {h}^{\text{worker}},$$   (Eq  20)
where $\mathbf {W}^{\text{worker}}$ is one model parameter and the output dimension equals the number of total non-expert annotators  The prediction is to find the worker which is responsible for this annotation 
Adversarial Training
The training objective with adversarial neural network is different from the baseline model, as it includes the extra worker discriminator  Thus the new objective includes two parts, one being the negative log-likelihood from NER which is the same as the baseline, and the other being the negative the log-likelihood from the worker discriminator 
In order to obtain the negative log-likelihood of the worker discriminator, we use softmax to compute the probability of the actual worker $\bar{z}$ as well, which is defined by:
$$p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}) = \frac{\exp (\mathbf {o}^{\text{worker}}_{\bar{z}})}{\sum _{z} \exp (\mathbf {o}^{\text{worker}}_z)},$$   (Eq  22)
where $z$ should enumerate all workers 
Based on the above definition of probability, our new objective is defined as follows:
$$\begin{split} \text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) &= \text{loss}(\Theta , \mathbf {X}, \mathbf {\bar{y}}) - \text{loss}(\Theta , \Theta ^{\prime }, \mathbf {X}) \\ \text{~~~~~~} &= -\log p(\mathbf {\bar{y}}|\mathbf {X}) + \log p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}), \end{split}$$   (Eq  23)
where $\Theta $ is the set of all model parameters related to NER, and $\Theta ^{\prime }$ is the set of the remaining parameters which are only related to the worker discriminator, $\mathbf {X}$ , $\mathbf {\bar{y}}$ and $\bar{z}$ are the input sentence, the crowd-annotated NE labels and the corresponding annotator for this annotation, respectively  It is worth noting that the parameters of the common Bi-LSTM are included in the set of $\Theta $ by definition 
In particular, our goal is not to simply minimize the new objective  Actually, we aim for a saddle point, finding the parameters $\Theta $ and $\Theta ^{\prime }$ satisfying the following conditions:
$$\begin{split} \hat{\Theta } &= \mathop {arg~min}_{\Theta }\text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \hat{\Theta }^{\prime } &= \mathop {arg~max}_{\Theta ^{\prime }}\text{R}(\hat{\Theta }, \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \end{split}$$   (Eq  24)
where the first equation aims to find one $\Theta $ that minimizes our new objective $\text{R}(\cdot )$ , and the second equation aims to find one $\Theta ^{\prime }$ maximizing the same objective 
Intuitively, the first equation of Formula 24 tries to minimize the NER loss, but at the same time maximize the worker discriminator loss by the shared parameters of the common Bi-LSTM  Thus the resulting features of common Bi-LSTM actually attempt to hurt the worker discriminator, which makes these features worker independent since they are unable to distinguish different workers  The second equation tries to minimize the worker discriminator loss by its own parameter $\Theta ^{\prime }$  
We use the standard back-propagation method to train the model parameters, the same as the baseline model  In order to incorporate the term of the argmax part of Formula 24 , we follow the previous work of adversarial training BIBREF13 , BIBREF15 , BIBREF17 , by introducing a gradient reverse layer between the common Bi-LSTM and the CNN module, whose forward does nothing but the backward simply negates the gradients 
Data Sets
With the purpose of obtaining evaluation datasets from crowd annotators, we collect the sentences from two domains: Dialog and E-commerce domain  We hire undergraduate students to annotate the sentences  They are required to identify the predefined types of entities in the sentences  Together with the guideline document, the annotators are educated some tips in fifteen minutes and also provided with 20 exemplifying sentences 
Labeled Data: DL-PS  In Dialog domain (DL), we collect raw sentences from a chatbot application  And then we randomly select 20K sentences as our pool and hire 43 students to annotate the sentences  We ask the annotators to label two types of entities: Person-Name and Song-Name  The annotators label the sentences independently  In particular, each sentence is assigned to three annotators for this data  Although the setting can be wasteful of labor, we can use the resulting dataset to test several well-known baselines such as majority voting 
After annotation, we remove some illegal sentences reported by the annotators  Finally, we have 16,948 sentences annotated by the students  Table 1 shows the information of annotated data  The average Kappa value among the annotators is 0 6033, indicating that the crowd annotators have moderate agreement on identifying entities on this data 
In order to evaluate the system performances, we create a set of corpus with gold annotations  Concretely, we randomly select 1,000 sentences from the final dataset and let two experts generate the gold annotations  Among them, we use 300 sentences as the development set and the remaining 700 as the test set  The rest sentences with only student annotations are used as the training set 
Labeled data: EC-MT and EC-UQ  In E-commerce domain (EC), we collect raw sentences from two types of texts: one is titles of merchandise entries (EC-MT) and another is user queries (EC-UQ)  The annotators label five types of entities: Brand, Product, Model, Material, and Specification  These five types of entities are very important for E-commerce platform, for example building knowledge graph of merchandises  Five students participate the annotations for this domain since the number of sentences is small  We use the similar strategy as DL-PS to annotate the sentences, except that only two annotators are assigned for each sentence, because we aim to test the system performances under very small duplicated annotations 
Finally, we obtain 2,337 sentences for EC-MT and 2,300 for EC-UQ  Table 1 shows the information of annotated results  Similarly, we produce the development and test datasets for system evaluation, by randomly selecting 400 sentences and letting two experts to generate the groundtruth annotations  Among them, we use 100 sentences as the development set and the remaining 300 as the test set  The rest sentences with only crowdsourcing annotations are used as the training set 
Unlabeled data  The vector representations of characters are basic inputs of our baseline and proposed models, which are obtained by the looking-up table $\mathbf {E}^W$   As introduced before, we can use pretrained embeddings from large-scale raw corpus to initialize the table  In order to pretrain the character embeddings, we use one large-scale unlabeled data from the user-generated content in Internet  Totally, we obtain a number of 5M sentences  Finally, we use the tool word2vec to pretrain the character embeddings based on the unlabeled dataset in our experiments 
Settings
For evaluation, we use the entity-level metrics of Precision (P), Recall (R), and their F1 value in our experiments, treating one tagged entity as correct only when it matches the gold entity exactly 
There are several hyper-parameters in the baseline LSTM-CRF and our final models  We set them empirically by the development performances  Concretely, we set the dimension size of the character embeddings by 100, the dimension size of the NE label embeddings by 50, and the dimension sizes of all the other hidden features by 200 
We exploit online training with a mini-batch size 128 to learn model parameters  The max-epoch iteration is set by 200, and the best-epoch model is chosen according to the development performances  We use RMSprop BIBREF28 with a learning rate $10^{-3}$ to update model parameters, and use $l_2$ -regularization by a parameter $10^{-5}$   We adopt the dropout technique to avoid overfitting by a drop value of $0 2$  
Comparison Systems
The proposed approach (henceforward referred to as “ALCrowd”) is compared with the following systems:
CRF: We use the Crfsuite tool to train a model on the crowdsourcing labeled data  As for the feature settings, we use the supervised version of BIBREF0 zhao2008unsupervised 
CRF-VT: We use the same settings of the CRF system, except that the training data is the voted version, whose groundtruths are produced by majority voting at the character level for each annotated sentence 
CRF-MA: The CRF model proposed by BIBREF3 rodrigues2014sequence, which uses a prior distributation to model multiple crowdsourcing annotators  We use the source code provided by the authors 
LSTM-CRF: Our baseline system trained on the crowdsourcing labeled data 
LSTM-CRF-VT: Our baseline system trained on the voted corpus, which is the same as CRF-VT 
LSTM-Crowd: The LSTM-CRF model with crowd annotation learning proposed by BIBREF4 nguyen2017aggregating  We use the source code provided by the authors 
The first three systems are based on the CRF model using traditional handcrafted features, and the last three systems are based on the neural LSTM-CRF model  Among them, CRF-MA, LSTM-Crowd and our system with adversarial learning (ALCrowd) are based on crowd annotation learning that directly trains the model on the crowd-annotations  Five systems, including CRF, CRF-MA, LSTM-CRF, LSTM-Crowd, and ALCrowd, are trained on the original version of labeled data, while CRF-VT and LSTM-CRF-VT are trained on the voted version  Since CRF-VT, CRF-MA and LSTM-CRF-VT all require ground-truth answers for each training sentence, which are difficult to be produced with only two annotations, we do not apply the three models on the two EC datasets 
Main Results
In this section, we show the model performances of our proposed crowdsourcing learning system (ALCrowd), and meanwhile compare it with the other systems mentioned above  Table 2 shows the experimental results on the DL-PS datasets and Table 3 shows the experiment results on the EC-MT and EC-UQ datasets, respectively 
The results of CRF and LSTM-CRF mean that the crowd annotation is an alternative solution with low cost for labeling data that could be used for training a NER system even there are some inconsistencies  Compared with CRF, LSTM-CRF achieves much better performances on all the three data, showing +6 12 F1 improvement on DL-PS, +4 51 on EC-MT, and +9 19 on EC-UQ  This indicates that LSTM-CRF is a very strong baseline system, demonstrating the effectiveness of neural network 
Interestingly, when compared with CRF and LSTM-CRF, CRF-VT and LSTM-CRF-VT trained on the voted version perform worse in the DL-PS dataset  This trend is also mentioned in BIBREF4 nguyen2017aggregating  This fact shows that the majority voting method might be unsuitable for our task  There are two possible reasons accounting for the observation  On the one hand, simple character-level voting based on three annotations for each sentence may be still not enough  In the DL-PS dataset, even with only two predefined entity types, one character can have nine NE labels  Thus the majority-voting may be incapable of handling some cases  While the cost by adding more annotations for each sentence would be greatly increased  On the other hand, the lost information produced by majority-voting may be important, at least the ambiguous annotations denote that the input sentence is difficult for NER  The normal CRF and LSTM-CRF models without discard any annotations can differentiate these difficult contexts through learning 
Three crowd-annotation learning systems provide better performances than their counterpart systems, (CRF-MA VS CRF) and (LSTM-Crowd/ALCrowd VS LSTM-CRF)  Compared with the strong baseline LSTM-CRF, ALCrowd shows its advantage with +1 08 F1 improvements on DL-PS, +1 24 on EC-MT, and +2 38 on EC-UQ, respectively  This indicates that adding the crowd-annotation learning is quite useful for building NER systems  In addition, ALCrowd also outperforms LSTM-Crowd on all the datasets consistently, demonstrating the high effectiveness of ALCrowd in extracting worker independent features  Among all the systems, ALCrowd performs the best, and significantly better than all the other models (the p-value is below $10^{-5}$ by using t-test)  The results indicate that with the help of adversarial training, our system can learn a better feature representation from crowd annotation 
Discussion
Impact of Character Embeddings  First, we investigate the effect of the pretrained character embeddings in our proposed crowdsourcing learning model  The comparison results are shown in Figure 2 , where Random refers to the random initialized character embeddings, and Pretrained refers to the embeddings pretrained on the unlabeled data  According to the results, we find that our model with the pretrained embeddings significantly outperforms that using the random embeddings, demonstrating that the pretrained embeddings successfully provide useful information 
Case Studies  Second, we present several case studies in order to study the differences between our baseline and the worker adversarial models  We conduct a closed test on the training set, the results of which can be regarded as modifications of the training corpus, since there exist inconsistent annotations for each training sentence among the different workers  Figure 3 shows the two examples from the DL-PS dataset, which compares the outputs of the baseline and our final models, as well as the majority-voting strategy 
In the first case, none of the annotations get the correct NER result, but our proposed model can capture it  The result of LSTM-CRF is the same as majority-voting  In the second example, the output of majority-voting is the worst, which can account for the reason why the same model trained on the voted corpus performs so badly, as shown in Table 2   The model of LSTM-CRF fails to recognize the named entity “Xiexie” because of not trusting the second annotation, treating it as one noise annotation  Our proposed model is able to recognize it, because of its ability of extracting worker independent features 
Conclusions
In this paper, we presented an approach to performing crowd annotation learning based on the idea of adversarial training for Chinese Named Entity Recognition (NER)  In our approach, we use a common and private Bi-LSTMs for representing annotator-generic and -specific information, and learn a label Bi-LSTM from the crowd-annotated NE label sequences  Finally, the proposed approach adopts a LSTM-CRF model to perform tagging  In our experiments, we create two data sets for Chinese NER tasks in the dialog and e-commerce domains  The experimental results show that the proposed approach outperforms strong baseline systems 
 Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No  61572338, 61525205, and 61602160)  This work is also partially supported by the joint research project of Alibaba and Soochow University  Wenliang is also partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization 
Context 3: Introduction
In recent years, there has been a movement to leverage social medial data to detect, estimate, and track the change in prevalence of disease  For example, eating disorders in Spanish language Twitter tweets BIBREF0 and influenza surveillance BIBREF1   More recently, social media has been leveraged to monitor social risks such as prescription drug and smoking behaviors BIBREF2 , BIBREF3 , BIBREF4 as well as a variety of mental health disorders including suicidal ideation BIBREF5 , attention deficient hyperactivity disorder BIBREF6 and major depressive disorder BIBREF7   In the case of major depressive disorder, recent efforts range from characterizing linguistic phenomena associated with depression BIBREF8 and its subtypes e g , postpartum depression BIBREF5 , to identifying specific depressive symptoms BIBREF9 , BIBREF10 e g , depressed mood  However, more research is needed to better understand the predictive power of supervised machine learning classifiers and the influence of feature groups and feature sets for efficiently classifying depression-related tweets to support mental health monitoring at the population-level BIBREF11  
This paper builds upon related works toward classifying Twitter tweets representing symptoms of major depressive disorder by assessing the contribution of lexical features (e g , unigrams) and emotion (e g , strongly negative) to classification performance, and by applying methods to eliminate low-value features 
METHODS
Specifically, we conducted a feature ablation study to assess the informativeness of each feature group and a feature elimination study to determine the optimal feature sets for classifying Twitter tweets  We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13   The dataset contains 9,473 annotations for 9,300 tweets  Each tweet is annotated as no evidence of depression (e g , “Citizens fear an economic depression") or evidence of depression (e g , “depressed over disappointment")  If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e g , “feeling down in the dumps"), disturbed sleep (e g , “another restless night"), or fatigue or loss of energy (e g , “the fatigue is unbearable") BIBREF10   For each class, every annotation (9,473 tweets) is binarized as the positive class e g , depressed mood=1 or negative class e g , not depressed mood=0 
Features
Furthermore, this dataset was encoded with 7 feature groups with associated feature values binarized (i e , present=1 or absent=0) to represent potentially informative features for classifying depression-related classes  We describe the feature groups by type, subtype, and provide one or more examples of words representing the feature subtype from a tweet:
lexical features, unigrams, e g , “depressed”;
syntactic features, parts of speech, e g , “cried” encoded as V for verb;
emotion features, emoticons, e g , :( encoded as SAD;
demographic features, age and gender e g , “this semester” encoded as an indicator of 19-22 years of age and “my girlfriend” encoded as an indicator of male gender, respectively;
sentiment features, polarity and subjectivity terms with strengths, e g , “terrible” encoded as strongly negative and strongly subjective;
personality traits, neuroticism e g , “pissed off” implies neuroticism;
LIWC Features, indicators of an individual's thoughts, feelings, personality, and motivations, e g , “feeling” suggestions perception, feeling, insight, and cognitive mechanisms experienced by the Twitter user 
A more detailed description of leveraged features and their values, including LIWC categories, can be found in BIBREF10  
Based on our prior initial experiments using these feature groups BIBREF10 , we learned that support vector machines perform with the highest F1-score compared to other supervised approaches  For this study, we aim to build upon this work by conducting two experiments: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Feature Contribution
Feature ablation studies are conducted to assess the informativeness of a feature group by quantifying the change in predictive power when comparing the performance of a classifier trained with the all feature groups versus the performance without a particular feature group  We conducted a feature ablation study by holding out (sans) each feature group and training and testing the support vector model using a linear kernel and 5-fold, stratified cross-validation  We report the average F1-score from our baseline approach (all feature groups) and report the point difference (+ or -) in F1-score performance observed by ablating each feature set 
By ablating each feature group from the full dataset, we observed the following count of features - sans lexical: 185, sans syntactic: 16,935, sans emotion: 16,954, sans demographics: 16,946, sans sentiment: 16,950, sans personality: 16,946, and sans LIWC: 16,832  In Figure 1, compared to the baseline performance, significant drops in F1-scores resulted from sans lexical for depressed mood (-35 points), disturbed sleep (-43 points), and depressive symptoms (-45 points)  Less extensive drops also occurred for evidence of depression (-14 points) and fatigue or loss of energy (-3 points)  In contrast, a 3 point gain in F1-score was observed for no evidence of depression  We also observed notable drops in F1-scores for disturbed sleep by ablating demographics (-7 points), emotion (-5 points), and sentiment (-5 points) features  These F1-score drops were accompanied by drops in both recall and precision  We found equal or higher F1-scores by removing non-lexical feature groups for no evidence of depression (0-1 points), evidence of depression (0-1 points), and depressive symptoms (2 points) 
Unsurprisingly, lexical features (unigrams) were the largest contributor to feature counts in the dataset  We observed that lexical features are also critical for identifying depressive symptoms, specifically for depressed mood and for disturbed sleep  For the classes higher in the hierarchy - no evidence of depression, evidence of depression, and depressive symptoms - the classifier produced consistent F1-scores, even slightly above the baseline for depressive symptoms and minor fluctuations of change in recall and precision when removing other feature groups suggesting that the contribution of non-lexical features to classification performance was limited  However, notable changes in F1-score were observed for the classes lower in the hierarchy including disturbed sleep and fatigue or loss of energy  For instance, changes in F1-scores driven by both recall and precision were observed for disturbed sleep by ablating demographics, emotion, and sentiment features, suggesting that age or gender (“mid-semester exams have me restless”), polarity and subjective terms (“lack of sleep is killing me”), and emoticons (“wide awake :(”) could be important for both identifying and correctly classifying a subset of these tweets 
Feature Elimination
Feature elimination strategies are often taken 1) to remove irrelevant or noisy features, 2) to improve classifier performance, and 3) to reduce training and run times  We conducted an experiment to determine whether we could maintain or improve classifier performances by applying the following three-tiered feature elimination approach:
Reduction We reduced the dataset encoded for each class by eliminating features that occur less than twice in the full dataset 
Selection We iteratively applied Chi-Square feature selection on the reduced dataset, selecting the top percentile of highest ranked features in increments of 5 percent to train and test the support vector model using a linear kernel and 5-fold, stratified cross-validation 
Rank We cumulatively plotted the average F1-score performances of each incrementally added percentile of top ranked features  We report the percentile and count of features resulting in the first occurrence of the highest average F1-score for each class 
All experiments were programmed using scikit-learn 0 18 
The initial matrices of almost 17,000 features were reduced by eliminating features that only occurred once in the full dataset, resulting in 5,761 features  We applied Chi-Square feature selection and plotted the top-ranked subset of features for each percentile (at 5 percent intervals cumulatively added) and evaluated their predictive contribution using the support vector machine with linear kernel and stratified, 5-fold cross validation 
In Figure 2, we observed optimal F1-score performance using the following top feature counts: no evidence of depression: F1: 87 (15th percentile, 864 features), evidence of depression: F1: 59 (30th percentile, 1,728 features), depressive symptoms: F1: 55 (15th percentile, 864 features), depressed mood: F1: 39 (55th percentile, 3,168 features), disturbed sleep: F1: 46 (10th percentile, 576 features), and fatigue or loss of energy: F1: 72 (5th percentile, 288 features) (Figure 1)  We note F1-score improvements for depressed mood from F1: 13 at the 1st percentile to F1: 33 at the 20th percentile 
We observed peak F1-score performances at low percentiles for fatigue or loss of energy (5th percentile), disturbed sleep (10th percentile) as well as depressive symptoms and no evidence of depression (both 15th percentile) suggesting fewer features are needed to reach optimal performance  In contrast, peak F1-score performances occurred at moderate percentiles for evidence of depression (30th percentile) and depressed mood (55th percentile) suggesting that more features are needed to reach optimal performance  However, one notable difference between these two classes is the dramatic F1-score improvements for depressed mood i e , 20 point increase from the 1st percentile to the 20th percentile compared to the more gradual F1-score improvements for evidence of depression i e , 11 point increase from the 1st percentile to the 20th percentile  This finding suggests that for identifying depressed mood a variety of features are needed before incremental gains are observed 
RESULTS
From our annotated dataset of Twitter tweets (n=9,300 tweets), we conducted two feature studies to better understand the predictive power of several feature groups for classifying whether or not a tweet contains no evidence of depression (n=6,829 tweets) or evidence of depression (n=2,644 tweets)  If there was evidence of depression, we determined whether the tweet contained one or more depressive symptoms (n=1,656 tweets) and further classified the symptom subtype of depressed mood (n=1,010 tweets), disturbed sleep (n=98 tweets), or fatigue or loss of energy (n=427 tweets) using support vector machines  From our prior work BIBREF10 and in Figure 1, we report the performance for prediction models built by training a support vector machine using 5-fold, stratified cross-validation with all feature groups as a baseline for each class  We observed high performance for no evidence of depression and fatigue or loss of energy and moderate performance for all remaining classes 
Discussion
We conducted two feature study experiments: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Future Work
Our next step is to address the classification of rarer depressive symptoms suggestive of major depressive disorder from our dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia BIBREF15 , BIBREF16   We are developing a population-level monitoring framework designed to estimate the prevalence of depression (and depression-related symptoms and psycho-social stressors) over millions of United States-geocoded tweets  Identifying the most discriminating feature sets and natural language processing classifiers for each depression symptom is vital for this goal 
Conclusions
In summary, we conducted two feature study experiments to assess the contribution of feature groups and to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy  From these experiments, we conclude that simple lexical features and reduced feature sets can produce comparable results to the much larger feature dataset 
Acknowledgments
Research reported in this publication was supported by the National Library of Medicine of the [United States] National Institutes of Health under award numbers K99LM011393 and R00LM011393  This study was granted an exemption from review by the University of Utah Institutional Review Board (IRB 00076188)  Note that in order to protect tweeter anonymity, we have not reproduced tweets verbatim  Example tweets shown were generated by the researchers as exemplars only  Finally, we would like to thank the anonymous reviewers of this paper for their valuable comments 

ground_truth: probabilistic model

Logistic Regression, Multilayer Perceptron

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 accuracy does the proposed system achieve?
Context 4: Introduction
Relation classification is the task of assigning sentences with two marked entities to a predefined set of relations  The sentence “We poured the <e1>milk</e1> into the <e2>pumpkin mixture</e2> ”, for example, expresses the relation Entity-Destination(e1,e2)  While early research mostly focused on support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 , recent research showed performance improvements by applying neural networks (NNs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 on the benchmark data from SemEval 2010 shared task 8 BIBREF8  
This study investigates two different types of NNs: recurrent neural networks (RNNs) and convolutional neural networks (CNNs) as well as their combination  We make the following contributions:
(1) We propose extended middle context, a new context representation for CNNs for relation classification  The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part 
(2) We present connectionist bi-directional RNN models which are especially suited for sentence classification tasks since they combine all intermediate hidden layers for their final decision  Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before 
(3) Finally, we combine CNNs and RNNs using a simple voting scheme and achieve new state-of-the-art results on the SemEval 2010 benchmark dataset 
Related Work
In 2010, manually annotated data for relation classification was released in the context of a SemEval shared task BIBREF8   Shared task participants used, i a , support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1   Recently, their results on this data set were outperformed by applying NNs BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6  
zeng2014 built a CNN based only on the context between the relation arguments and extended it with several lexical features  kim2014 and others used convolutional filters of different sizes for CNNs  nguyen applied this to relation classification and obtained improvements over single filter sizes  deSantos2015 replaced the softmax layer of the CNN with a ranking layer  They showed improvements and published the best result so far on the SemEval dataset, to our knowledge 
socher used another NN architecture for relation classification: recursive neural networks that built recursive sentence representations based on syntactic parsing  In contrast, zhang investigated a temporal structured RNN with only words as input  They used a bi-directional model with a pooling layer on top 
Convolutional Neural Networks (CNN)
CNNs perform a discrete convolution on an input matrix with a set of different filters  For NLP tasks, the input matrix represents a sentence: Each column of the matrix stores the word embedding of the corresponding word  By applying a filter with a width of, e g , three columns, three neighboring words (trigram) are convolved  Afterwards, the results of the convolution are pooled  Following collobertWeston, we perform max-pooling which extracts the maximum value for each filter and, thus, the most informative n-gram for the following steps  Finally, the resulting values are concatenated and used for classifying the relation expressed in the sentence 
Input: Extended Middle Context
One of our contributions is a new input representation especially designed for relation classification  The contexts are split into three disjoint regions based on the two relation arguments: the left context, the middle context and the right context  Since in most cases the middle context contains the most relevant information for the relation, we want to focus on it but not ignore the other regions completely  Hence, we propose to use two contexts: (1) a combination of the left context, the left entity and the middle context; and (2) a combination of the middle context, the right entity and the right context  Due to the repetition of the middle context, we force the network to pay special attention to it  The two contexts are processed by two independent convolutional and max-pooling layers  After pooling, the results are concatenated to form the sentence representation  Figure FIGREF3 depicts this procedure  It shows an examplary sentence: “He had chest pain and <e1>headaches</e1> from <e2>mold</e2> in the bedroom ” If we only considered the middle context “from”, the network might be tempted to predict a relation like Entity-Origin(e1,e2)  However, by also taking the left and right context into account, the model can detect the relation Cause-Effect(e2,e1)  While this could also be achieved by integrating the whole context into the model, using the whole context can have disadvantages for longer sentences: The max pooling step can easily choose a value from a part of the sentence which is far away from the mention of the relation  With splitting the context into two parts, we reduce this danger  Repeating the middle context increases the chance for the max pooling step to pick a value from the middle context 
Convolutional Layer
Following previous work (e g , BIBREF5 , BIBREF6 ), we use 2D filters spanning all embedding dimensions  After convolution, a max pooling operation is applied that stores only the highest activation of each filter  We apply filters with different window sizes 2-5 (multi-windows) as in BIBREF5 , i e  spanning a different number of input words 
Recurrent Neural Networks (RNN)
Traditional RNNs consist of an input vector, a history vector and an output vector  Based on the representation of the current input word and the previous history vector, a new history is computed  Then, an output is predicted (e g , using a softmax layer)  In contrast to most traditional RNN architectures, we use the RNN for sentence modeling, i e , we predict an output vector only after processing the whole sentence and not after each word  Training is performed using backpropagation through time BIBREF9 which unfolds the recurrent computations of the history vector for a certain number of time steps  To avoid exploding gradients, we use gradient clipping with a threshold of 10 BIBREF10  
Input of the RNNs
Initial experiments showed that using trigrams as input instead of single words led to superior results  Hence, at timestep INLINEFORM0 we do not only give word INLINEFORM1 to the model but the trigram INLINEFORM2 by concatenating the corresponding word embeddings 
Connectionist Bi-directional RNNs
Especially for relation classification, the processing of the relation arguments might be easier with knowledge of the succeeding words  Therefore in bi-directional RNNs, not only a history vector of word INLINEFORM0 is regarded but also a future vector  This leads to the following conditioned probability for the history INLINEFORM1 at time step INLINEFORM2 : DISPLAYFORM0
Thus, the network can be split into three parts: a forward pass which processes the original sentence word by word (Equation EQREF6 ); a backward pass which processes the reversed sentence word by word (Equation ); and a combination of both (Equation )  All three parts are trained jointly  This is also depicted in Figure FIGREF7  
Combining forward and backward pass by adding their hidden layer is similar to BIBREF7   We, however, also add a connection to the previous combined hidden layer with weight INLINEFORM0 to be able to include all intermediate hidden layers into the final decision of the network (see Equation )  We call this “connectionist bi-directional RNN” 
In our experiments, we compare this RNN with uni-directional RNNs and bi-directional RNNs without additional hidden layer connections 
Word Representations
Words are represented by concatenated vectors: a word embedding and a position feature vector 
Pretrained word embeddings  In this study, we used the word2vec toolkit BIBREF11 to train embeddings on an English Wikipedia from May 2014

ground_truth: F1 scores of 85.99 on the DL-PS data,  75.15 on the EC-MT data and 71.53 on the EC-UQ data 

F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)

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 is the dataset annotated?
Context 4:  We only considered words appearing more than 100 times and added a special PADDING token for convolution  This results in an embedding training text of about 485,000 terms and INLINEFORM0 tokens  During model training, the embeddings are updated 
Position features  We incorporate randomly initialized position embeddings similar to zeng2014, nguyen and deSantos2015  In our RNN experiments, we investigate different possibilities of integrating position information: position embeddings, position embeddings with entity presence flags (flags indicating whether the current word is one of the relation arguments), and position indicators BIBREF7  
Objective Function: Ranking Loss
Ranking  We applied the ranking loss function proposed in deSantos2015 to train our models  It maximizes the distance between the true label INLINEFORM0 and the best competitive label INLINEFORM1 given a data point INLINEFORM2   The objective function is DISPLAYFORM0
with INLINEFORM0 and INLINEFORM1 being the scores for the classes INLINEFORM2 and INLINEFORM3 respectively  The parameter INLINEFORM4 controls the penalization of the prediction errors and INLINEFORM5 and INLINEFORM6 are margins for the correct and incorrect classes  Following deSantos2015, we set INLINEFORM7   We do not learn a pattern for the class Other but increase its difference to the best competitive label by using only the second summand in Equation EQREF10 during training 
Experiments and Results
We used the relation classification dataset of the SemEval 2010 task 8 BIBREF8   It consists of sentences which have been manually labeled with 19 relations (9 directed relations and one artificial class Other)  8,000 sentences have been distributed as training set and 2,717 sentences served as test set  For evaluation, we applied the official scoring script and report the macro F1 score which also served as the official result of the shared task 
RNN and CNN models were implemented with theano BIBREF12 , BIBREF13   For all our models, we use L2 regularization with a weight of 0 0001  For CNN training, we use mini batches of 25 training examples while we perform stochastic gradient descent for the RNN  The initial learning rates are 0 2 for the CNN and 0 01 for the RNN  We train the models for 10 (CNN) and 50 (RNN) epochs without early stopping  As activation function, we apply tanh for the CNN and capped ReLU for the RNN  For tuning the hyperparameters, we split the training data into two parts: 6 5k (training) and 1 5k (development) sentences  We also tuned the learning rate schedule on dev 
Beside of training single models, we also report ensemble results for which we combined the presented single models with a voting process 
Performance of CNNs
As a baseline system, we implemented a CNN similar to the one described by zeng2014  It consists of a standard convolutional layer with filters with only one window size, followed by a softmax layer  As input it uses the middle context  In contrast to zeng2014, our CNN does not have an additional fully connected hidden layer  Therefore, we increased the number of convolutional filters to 1200 to keep the number of parameters comparable  With this, we obtain a baseline result of 73 0  After including 5 dimensional position features, the performance was improved to 78 6 (comparable to 78 9 as reported by zeng2014 without linguistic features) 
In the next step, we investigate how this result changes if we successively add further features to our CNN: multi-windows for convolution (window sizes: 2,3,4,5 and 300 feature maps each), ranking layer instead of softmax and our proposed extended middle context  Table TABREF12 shows the results  Note that all numbers are produced by CNNs with a comparable number of parameters  We also report F1 for increasing the word embedding dimensionality from 50 to 400  The position embedding dimensionality is 5 in combination with 50 dimensional word embeddings and 35 with 400 dimensional word embeddings  Our results show that especially the ranking layer and the embedding size have an important impact on the performance 
Performance of RNNs
As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence  With this model, we achieve an F1 score of 61 2 on the SemEval test set 
Afterwards, we investigate the impact of different position features on the performance of uni-directional RNNs (position embeddings, position embeddings concatenated with a flag indicating whether the current word is an entity or not, and position indicators BIBREF7 )  The results indicate that position indicators (i e  artificial words that indicate the entity presence) perform the best on the SemEval data  We achieve an F1 score of 73 4 with them  However, the difference to using position embeddings with entity flags is not statistically significant 
Similar to our CNN experiments, we successively vary the RNN models by using bi-directionality, by adding connections between the hidden layers (“connectionist”), by applying ranking instead of softmax to predict the relation and by increasing the word embedding dimension to 400 
The results in Table TABREF14 show that all of these variations lead to statistically significant improvements  Especially the additional hidden layer connections and the integration of the ranking layer have a large impact on the performance 
Combination of CNNs and RNNs
Finally, we combine our CNN and RNN models using a voting process  For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes  In case of a tie, we pick one of the most frequent classes randomly  The combination achieves an F1 score of 84 9 which is better than the performance of the two NN types alone  It, thus, confirms our assumption that the networks provide complementary information: while the RNN computes a weighted combination of all words in the sentence, the CNN extracts the most informative n-grams for the relation and only considers their resulting activations 
Comparison with State of the Art
Table TABREF16 shows the results of our models ER-CNN (extended ranking CNN) and R-RNN (ranking RNN) in the context of other state-of-the-art models  Our proposed models obtain state-of-the-art results on the SemEval 2010 task 8 data set without making use of any linguistic features 
Conclusion
In this paper, we investigated different features and architectural choices for convolutional and recurrent neural networks for relation classification without using any linguistic features  For convolutional neural networks, we presented a new context representation for relation classification  Furthermore, we introduced connectionist recurrent neural networks for sentence classification tasks and performed the first experiments with ranking recurrent neural networks  Finally, we showed that even a simple combination of convolutional and recurrent neural networks improved results  With our neural models, we achieved new state-of-the-art results on the SemEval 2010 task 8 benchmark data 
Acknowledgments
Heike Adel is a recipient of the Google European Doctoral Fellowship in Natural Language Processing and this research is supported by this fellowship 
This research was also supported by Deutsche Forschungsgemeinschaft: grant SCHU 2246/4-2 

ground_truth: no evidence of depression, depressed mood, disturbed sleep, fatigue or loss of energy

The annotations are based on evidence of depression and further annotated by the depressive symptom if there is evidence of depression