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. LDA is an unsupervised method; is this paper introducing an unsupervised approach to spam detection?
Context 1: Introduction
Microblogging such as Twitter and Weibo is a popular social networking service, which allows users to post messages up to 140 characters  There are millions of active users on the platform who stay connected with friends  Unfortunately, spammers also use it as a tool to post malicious links, send unsolicited messages to legitimate users, etc  A certain amount of spammers could sway the public opinion and cause distrust of the social platform  Despite the use of rigid anti-spam rules, human-like spammers whose homepages having photos, detailed profiles etc  have emerged  Unlike previous "simple" spammers, whose tweets contain only malicious links, those "smart" spammers are more difficult to distinguish from legitimate users via content-based features alone BIBREF0  
There is a considerable amount of previous work on spammer detection on social platforms  Researcher from Twitter Inc  BIBREF1 collect bot accounts and perform analysis on the user behavior and user profile features  Lee et al  lee2011seven use the so-called social honeypot by alluring social spammers' retweet to build a benchmark dataset, which has been extensively explored in our paper  Some researchers focus on the clustering of urls in tweets and network graph of social spammers BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , showing the power of social relationship features As for content information modeling, BIBREF6 apply improved sparse learning methods  However, few studies have adopted topic-based features  Some researchers BIBREF7 discuss topic characteristics of spamming posts, indicating that spammers are highly likely to dwell on some certain topics such as promotion  But this may not be applicable to the current scenario of smart spammers 
In this paper, we propose an efficient feature extraction method  In this method, two new topic-based features are extracted and used to discriminate human-like spammers from legitimate users  We consider the historical tweets of each user as a document and use the Latent Dirichlet Allocation (LDA) model to compute the topic distribution for each user  Based on the calculated topic probability, two topic-based features, the Local Outlier Standard Score (LOSS) which captures the user's interests on different topics and the Global Outlier Standard Score (GOSS) which reveals the user's interests on specific topic in comparison with other users', are extracted  The two features contain both local and global information, and the combination of them can distinguish human-like spammers effectively 
To the best of our knowledge, it is the first time that features based on topic distributions are used in spammer classification  Experimental results on one public dataset and one self-collected dataset further validate that the two sets of extracted topic-based features get excellent performance on human-like spammer classification problem compared with other state-of-the-art methods  In addition, we build a Weibo dataset, which contains both legitimate users and spammers 
To summarize, our major contributions are two-fold:
In the following sections, we first propose the topic-based features extraction method in Section 2, and then introduce the two datasets in Section 3  Experimental results are discussed in Section 4, and we conclude the paper in Section 5  Future work is presented in Section 6 
Methodology
In this section, we first provide some observations we obtained after carefully exploring the social network, then the LDA model is introduced  Based on the LDA model, the ways to obtain the topic probability vector for each user and the two topic-based features are provided 
Observation
After exploring the homepages of a substantial number of spammers, we have two observations  1) social spammers can be divided into two categories  One is content polluters, and their tweets are all about certain kinds of advertisement and campaign  The other is fake accounts, and their tweets resemble legitimate users' but it seems they are simply random copies of others to avoid being detected by anti-spam rules  2) For legitimate users, content polluters and fake accounts, they show different patterns on topics which interest them 
Legitimate users mainly focus on limited topics which interest him  They seldom post contents unrelated to their concern 
Content polluters concentrate on certain topics 
Fake accounts focus on a wide range of topics due to random copying and retweeting of other users' tweets 
Spammers and legitimate users show different interests on some topics e g  commercial, weather, etc 
To better illustrate our observation, Figure  1 shows the topic distribution of spammers and legitimate users in two employed datasets(the Honeypot dataset and Weibo dataset)  We can see that on both topics (topic-3 and topic-11) there exists obvious difference between the red bars and green bars, representing spammers and legitimate users  On the Honeypot dataset, spammers have a narrower shape of distribution (the outliers on the red bar tail are not counted) than that of legitimate users  This is because there are more content polluters than fake accounts  In other word, spammers in this dataset tend to concentrate on limited topics  While on the Weibo dataset, fake accounts who are interested in different topics take large proportion of spammers  Their distribution is more flat (i e  red bars) than that of the legitimate users  Therefore we can detect spammers by means of the difference of their topic distribution patterns 
LDA model
Blei et al blei2003latent first presented Latent Dirichlet Allocation(LDA) as an example of topic model 
Each document $i$ is deemed as a bag of words $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $ and $M$ is the number of words  Each word is attributable to one of the document's topics $Z=\left\lbrace  z_{i1},z_{i2},   ,z_{iK}\right\rbrace $ and $K$ is the number of topics  $\psi _{k}$ is a multinomial distribution over words for topic $k$   $\theta _i$ is another multinomial distribution over topics for document $i$   The smoothed generative model is illustrated in Figure  2   $\alpha $ and $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $0 are hyper parameter that affect scarcity of the document-topic and topic-word distributions  In this paper, $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $1 , $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $2 and $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $3 are empirically set to 0 3, 0 01 and 15  The entire content of each Twitter user is regarded as one document  We adopt Gibbs Sampling BIBREF8 to speed up the inference of LDA  Based on LDA, we can get the topic probabilities for all users in the employed dataset as: $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $4 , where $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $5 is the number of users  Each element $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $6 is a topic probability vector for the $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $7 document  $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $8 is the raw topic probability vector and our features are developed on top of it 
Topic-based Features
Using the LDA model, each person in the dataset is with a topic probability vector $X_i$   Assume $x_{ik}\in X_{i}$ denotes the likelihood that the $\emph {i}^{th}$ tweet account favors $\emph {k}^{th}$ topic in the dataset  Our topic based features can be calculated as below 
Global Outlier Standard Score measures the degree that a user's tweet content is related to a certain topic compared to the other users  Specifically, the "GOSS" score of user $i$ on topic $k$ can be calculated as Eq ( 12 ):
$$\centering \begin{array}{ll} \mu \left(x_{k}\right)=\frac{\sum _{i=1}^{n} x_{ik}}{n},\\ GOSS\left(x_{ik}\right)=\frac{x_{ik}-\mu \left(x_k\right)}{\sqrt{\underset{i}{\sum }\left(x_{ik}-\mu \left(x_{k}\right)\right)^{2}}}  \end{array}$$   (Eq  12)
The value of $GOSS\left(x_{ik}\right)$ indicates the interesting degree of this person to the $\emph {k}^{th}$ topic  Specifically, if $GOSS\left(x_{ik}\right)$ > $GOSS\left(x_{jk}\right)$ , it means that the $\emph {i}^{th}$ person has more interest in topic $k$ than the $\emph {j}^{th}$ person  If the value $GOSS\left(x_{ik}\right)$ is extremely high or low, the $\emph {i}^{th}$ person showing extreme interest or no interest on topic $k$ which will probably be a distinctive pattern in the fowllowing classfication  Therefore, the topics interested or disliked by the $\emph {k}^{th}$0 person can be manifested by $\emph {k}^{th}$1 , from which the pattern of the interested topics with regarding to this person is found  Denote $\emph {k}^{th}$2 our first topic-based feature, and it hopefully can get good performance on spammer detection 
Local Outlier Standard Score measures the degree of interest someone shows to a certain topic by considering his own homepage content only  For instance, the "LOSS" score of account $i$ on topic $k$ can be calculated as Eq ( 13 ):
$$\centering \begin{array}{ll} \mu \left(x_{i}\right)=\frac{\sum _{k=1}^{K} x_{ik}}{K},\\ LOSS\left(x_{ik}\right)=\frac{x_{ik}-\mu \left(x_i\right)}{\sqrt{\underset{k}{\sum }\left(x_{ik}-\mu \left(x_{i}\right)\right)^{2}}}  \end{array}$$   (Eq  13)
$\mu (x_i)$ represents the averaged interesting degree for all topics with regarding to $\emph {i}^{th}$ user and his tweet content  Similarly to $GOSS$ , the topics interested or disliked by the $\emph {i}^{th}$ person via considering his single post information can be manifested by $f_{LOSS}^{i}=[LOSS(x_{i1})\cdots LOSS(x_{iK})]$ , and $LOSS$ becomes our second topic-based features for the $\emph {i}^{th}$ person 
Dataset
We use one public dataset Social Honeypot dataset and one self-collected dataset Weibo dataset to validate the effectiveness of our proposed features 
Social Honeypot Dataset: Lee et al  lee2010devils created and deployed 60 seed social accounts on Twitter to attract spammers by reporting back what accounts interact with them  They collected 19,276 legitimate users and 22,223 spammers in their datasets along with their tweet content in 7 months  This is our first test dataset 
Our Weibo Dataset: Sina Weibo is one of the most famous social platforms in China  It has implemented many features from Twitter  The 2197 legitimate user accounts in this dataset are provided by the Tianchi Competition held by Sina Weibo  The spammers are all purchased commercially from multiple vendors on the Internet  We checked them manually and collected 802 suitable "smart" spammers accounts 
Preprocessing: Before directly performing the experiments on the employed datasets, we first delete some accounts with few posts in the two employed since the number of tweets is highly indicative of spammers  For the English Honeypot dataset, we remove stopwords, punctuations, non-ASCII words and apply stemming  For the Chinese Weibo dataset, we perform segmentation with "Jieba", a Chinese text segmentation tool  After preprocessing steps, the Weibo dataset contains 2197 legitimate users and 802 spammers, and the honeypot dataset contains 2218 legitimate users and 2947 spammers  It is worth mentioning that the Honeypot dataset has been slashed because most of the Twitter accounts only have limited number of posts, which are not enough to show their interest inclination 
Evaluation Metrics
The evaluating indicators in our model are show in 2   We calculate precision, recall and F1-score (i e  F1 score) as in Eq  ( 19 )  Precision is the ratio of selected accounts that are spammers  Recall is the ratio of spammers that are detected so  F1-score is the harmonic mean of precision and recall 
$$precision =\frac{TP}{TP+FP}, recall =\frac{TP}{TP+FN}\nonumber \\ F1-score = \frac{2\times precision \times recall}{precision + recall}$$   (Eq  19)
Performance Comparisons with Baseline
Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features  We test each classification algorithm with scikit-learn BIBREF9 and run a 10-fold cross validation  On each dataset, the employed classifiers are trained with individual feature first, and then with the combination of the two features  From 1 , we can see that GOSS+LOSS achieves the best performance on F1-score among all others  Besides, the classification by combination of LOSS and GOSS can increase accuracy by more than 3% compared with raw topic distribution probability 
Comparison with Other Features
To compare our extracted features with previously used features for spammer detection, we use three most discriminative feature sets according to Lee et al  lee2011seven( 4 )  Two classifiers (Adaboost and SVM) are selected to conduct feature performance comparisons  Using Adaboost, our LOSS+GOSS features outperform all other features except for UFN which is 2% higher than ours with regard to precision on the Honeypot dataset  It is caused by the incorrectly classified spammers who are mostly news source after our manual check  They keep posting all kinds of news pieces covering diverse topics, which is similar to the behavior of fake accounts  However, UFN based on friendship networks is more useful for public accounts who possess large number of followers  The best recall value of our LOSS+GOSS features using SVM is up to 6% higher than the results by other feature groups  Regarding F1-score, our features outperform all other features  To further show the advantages of our proposed features, we compare our combined LOSS+GOSS with the combination of all the features from Lee et al  lee2011seven (UFN+UC+UH)  It's obvious that LOSS+GOSS have a great advantage over UFN+UC+UH in terms of recall and F1-score  Moreover, by combining our LOSS+GOSS features and UFN+UC+UH features together, we obtained another 7 1% and 2 3% performance gain with regard to precision and F1-score by Adaboost  Though there is a slight decline in terms of recall  By SVM, we get comparative results on recall and F1-score but about 3 5% improvement on precision 
Conclusion
In this paper, we propose a novel feature extraction method to effectively detect "smart" spammers who post seemingly legitimate tweets and are thus difficult to identify by existing spammer classification methods  Using the LDA model, we obtain the topic probability for each Twitter user  By utilizing the topic probability result, we extract our two topic-based features: GOSS and LOSS which represent the account with global and local information  Experimental results on a public dataset and a self-built Chinese microblog dataset validate the effectiveness of the proposed features 
Future Work
In future work, the combination method of local and global information can be further improved to maximize their individual strengths  We will also apply decision theory to enhancing the performance of our proposed features  Moreover, we are also building larger datasets on both Twitter and Weibo to validate our method  Moreover, larger datasets on both Twitter and Weibo will be built to further validate our method 
Context 3: Introduction
The automatic processing of medical texts and documents plays an increasingly important role in the recent development of the digital health area  To enable dedicated Natural Language Processing (NLP) that is highly accurate with respect to medically relevant categories, manually annotated data from this domain is needed  One category of high interest and relevance are medical entities  Only very few annotated corpora in the medical domain exist  Many of them focus on the relation between chemicals and diseases or proteins and diseases, such as the BC5CDR corpus BIBREF0, the Comparative Toxicogenomics Database BIBREF1, the FSU PRotein GEne corpus BIBREF2 or the ADE (adverse drug effect) corpus BIBREF3  The NCBI Disease Corpus BIBREF4 contains condition mention annotations along with annotations of symptoms  Several new corpora of annotated case reports were made available recently  grouin-etal-2019-clinical presented a corpus with medical entity annotations of clinical cases written in French, copdPhenotype presented a corpus focusing on phenotypic information for chronic obstructive pulmonary disease while 10 1093/database/bay143 presented a corpus focusing on identifying main finding sentences in case reports 
The corpus most comparable to ours is the French corpus of clinical case reports by grouin-etal-2019-clinical  Their annotations are based on UMLS semantic types  Even though there is an overlap in annotated entities, semantic classes are not the same  Lab results are subsumed under findings in our corpus and are not annotated as their own class  Factors extend beyond gender and age and describe any kind of risk factor that contributes to a higher probability of having a certain disease  Our corpus includes additional entity types  We annotate conditions, findings (including medical findings such as blood values), factors, and also modifiers which indicate the negation of other entities as well as case entities, i  e , entities specific to one case report  An overview is available in Table TABREF3 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation tasks
Case reports are standardized in the CARE guidelines BIBREF5  They represent a detailed description of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient  We focus on documents freely available through PubMed Central (PMC)  The presentation of the patient's case can usually be found in a dedicated section or the abstract  We perform a manual annotation of all mentions of case entities, conditions, findings, factors and modifiers  The scope of our manual annotation is limited to the presentation of a patient's signs and symptoms  In addition, we annotate the title of the case report 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation Guidelines
We annotate the following entities:
case entity marks the mention of a patient  A case report can contain more than one case description  Therefore, all the findings, factors and conditions related to one patient are linked to the respective case entity  Within the text, this entity is often represented by the first mention of the patient and overlaps with the factor annotations which can, e  g , mark sex and age (cf  Figure FIGREF12) 
condition marks a medical disease such as pneumothorax or dislocation of the shoulder 
factor marks a feature of a patient which might influence the probability for a specific diagnosis  It can be immutable (e  g , sex and age), describe a specific medical history (e  g , diabetes mellitus) or a behaviour (e  g , smoking) 
finding marks a sign or symptom a patient shows  This can be visible (e  g , rash), described by a patient (e  g , headache) or measurable (e  g , decreased blood glucose level) 
negation modifier explicitly negate the presence of a certain finding usually setting the case apart from common cases 
We also annotate relations between these entities, where applicable  Since we work on case descriptions, the anchor point of these relations is the case that is described  The following relations are annotated:
has relations exist between a case entity and factor, finding or condition entities 
modifies relations exist between negation modifiers and findings 
causes relations exist between conditions and findings 
Example annotations are shown in Figure FIGREF16 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotators
We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above  Initially, we asked four annotators to test our guidelines on two texts  Subsequently, identified issues were discussed and resolved  Following this pilot annotation phase, we asked two different annotators to annotate two case reports according to our guidelines  The same annotators annotated an overall collection of 53 case reports

ground_truth: No

No

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 were the scores of their system?
Context 2: Introduction
Understanding the emotions expressed in a text or message is of high relevance nowadays  Companies are interested in this to get an understanding of the sentiment of their current customers regarding their products and the sentiment of their potential customers to attract new ones  Moreover, changes in a product or a company may also affect the sentiment of a customer  However, the intensity of an emotion is crucial in determining the urgency and importance of that sentiment  If someone is only slightly happy about a product, is a customer willing to buy it again? Conversely, if someone is very angry about customer service, his or her complaint might be given priority over somewhat milder complaints 
BIBREF0 present four tasks in which systems have to automatically determine the intensity of emotions (EI) or the intensity of the sentiment (Valence) of tweets in the languages English, Arabic, and Spanish  The goal is to either predict a continuous regression (reg) value or to do ordinal classification (oc) based on a number of predefined categories  The EI tasks have separate training sets for four different emotions: anger, fear, joy and sadness  Due to the large number of subtasks and the fact that this language does not have many resources readily available, we only focus on the Spanish subtasks  Our work makes the following contributions:
Our submissions ranked second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accurate in automatically determining the intensity of emotions and sentiment of Spanish tweets  This paper will first focus on the datasets, the data generation procedure, and the techniques and tools used  Then we present the results in detail, after which we perform a small error analysis on the largest mistakes our model made  We conclude with some possible ideas for future work 
Data
For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1   Links and usernames were replaced by the general tokens URL and @username, after which the tweets were tokenized by using TweetTokenizer  All text was lowercased  In a post-processing step, it was ensured that each emoji is tokenized as a single token 
Word Embeddings
To be able to train word embeddings, Spanish tweets were scraped between November 8, 2017 and January 12, 2018  We chose to create our own embeddings instead of using pre-trained embeddings, because this way the embeddings would resemble the provided data set: both are based on Twitter data  Added to this set was the Affect in Tweets Distant Supervision Corpus (DISC) made available by the organizers BIBREF0 and a set of 4 1 million tweets from 2015, obtained from BIBREF2   After removing duplicate tweets and tweets with fewer than ten tokens, this resulted in a set of 58 7 million tweets, containing 1 1 billion tokens  The tweets were preprocessed using the method described in Section SECREF6   The word embeddings were created using word2vec in the gensim library BIBREF3 , using CBOW, a window size of 40 and a minimum count of 5  The feature vectors for each tweet were then created by using the AffectiveTweets WEKA package BIBREF4  
Translating Lexicons
Most lexical resources for sentiment analysis are in English  To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
All lexicons from the AffectiveTweets package were translated, except for SentiStrength  Instead of translating this lexicon, the English version was replaced by the Spanish variant made available by BIBREF6  
For each subtask, the optimal combination of lexicons was determined  This was done by first calculating the benefits of adding each lexicon individually, after which only beneficial lexicons were added until the score did not increase anymore (e g  after adding the best four lexicons the fifth one did not help anymore, so only four were added)  The tests were performed using a default SVM model, with the set of word embeddings described in the previous section  Each subtask thus uses a different set of lexicons (see Table TABREF1 for an overview of the lexicons used in our final ensemble)  For each subtask, this resulted in a (modest) increase on the development set, between 0 01 and 0 05 
Translating Data
The training set provided by BIBREF0 is not very large, so it was interesting to find a way to augment the training set  A possible method is to simply translate the datasets into other languages, leaving the labels intact  Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish  This new set of “Spanish” data was then added to our original training set  Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets 
Algorithms Used
Three types of models were used in our system, a feed-forward neural network, an LSTM network and an SVM regressor  The neural nets were inspired by the work of Prayas BIBREF7 in the previous shared task  Different regression algorithms (e g  AdaBoost, XGBoost) were also tried due to the success of SeerNet BIBREF8 , but our study was not able to reproduce their results for Spanish 
For both the LSTM network and the feed-forward network, a parameter search was done for the number of layers, the number of nodes and dropout used  This was done for each subtask, i e  different tasks can have a different number of layers  All models were implemented using Keras BIBREF9   After the best parameter settings were found, the results of 10 system runs to produce our predictions were averaged (note that this is different from averaging our different type of models in Section SECREF16 )  For the SVM (implemented in scikit-learn BIBREF10 ), the RBF kernel was used and a parameter search was conducted for epsilon  Detailed parameter settings for each subtask are shown in Table TABREF12   Each parameter search was performed using 10-fold cross validation, as to not overfit on the development set 
Semi-supervised Learning
One of the aims of this study was to see if using semi-supervised learning is beneficial for emotion intensity tasks  For this purpose, the DISC BIBREF0 corpus was used  This corpus was created by querying certain emotion-related words, which makes it very suitable as a semi-supervised corpus  However, the specific emotion the tweet belonged to was not made public  Therefore, a method was applied to automatically assign the tweets to an emotion by comparing our scraped tweets to this new data set 
First, in an attempt to obtain the query-terms, we selected the 100 words which occurred most frequently in the DISC corpus, in comparison with their frequencies in our own scraped tweets corpus  Words that were clearly not indicators of emotion were removed  The rest was annotated per emotion or removed if it was unclear to which emotion the word belonged  This allowed us to create silver datasets per emotion, assigning tweets to an emotion if an annotated emotion-word occurred in the tweet 
Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data  This silver data is then simply added to our training set, after which the model is retrained  However, an extra step is applied to ensure that the silver data is of reasonable quality  Instead of training a single model initially, ten different models were trained which predict the labels of the silver instances  If the highest and lowest prediction do not differ more than a certain threshold the silver instance is maintained, otherwise it is discarded 
This results in two parameters that could be optimized: the threshold and the number of silver instances that would be added  This method can be applied to both the LSTM and feed-forward networks that were used  An overview of the characteristics of our data set with the final parameter settings is shown in Table TABREF14   Usually, only a small subset of data was added to our training set, meaning that most of the silver data is not used in the experiments  Note that since only the emotions were annotated, this method is only applicable to the EI tasks 
Ensembling
To boost performance, the SVM, LSTM, and feed-forward models were combined into an ensemble  For both the LSTM and feed-forward approach, three different models were trained  The first model was trained on the training data (regular), the second model was trained on both the training and translated training data (translated) and the third one was trained on both the training data and the semi-supervised data (silver)  Due to the nature of the SVM algorithm, semi-supervised learning does not help, so only the regular and translated model were trained in this case  This results in 8 different models per subtask  Note that for the valence tasks no silver training data was obtained, meaning that for those tasks the semi-supervised models could not be used 
Per task, the LSTM and feed-forward model's predictions were averaged over 10 prediction runs  Subsequently, the predictions of all individual models were combined into an average  Finally, models were removed from the ensemble in a stepwise manner if the removal increased the average score  This was done based on their original scores, i e  starting out by trying to remove the worst individual model and working our way up to the best model  We only consider it an increase in score if the difference is larger than 0 002 (i e  the difference between 0 716 and 0 718)  If at some point the score does not increase and we are therefore unable to remove a model, the process is stopped and our best ensemble of models has been found  This process uses the scores on the development set of different combinations of models  Note that this means that the ensembles for different subtasks can contain different sets of models  The final model selections can be found in Table TABREF17  
Results and Discussion
Table TABREF18 shows the results on the development set of all individuals models, distinguishing the three types of training: regular (r), translated (t) and semi-supervised (s)  In Tables TABREF17 and TABREF18 , the letter behind each model (e g  SVM-r, LSTM-r) corresponds to the type of training used  Comparing the regular and translated columns for the three algorithms, it shows that in 22 out of 30 cases, using translated instances as extra training data resulted in an improvement  For the semi-supervised learning approach, an improvement is found in 15 out of 16 cases  Moreover, our best individual model for each subtask (bolded scores in Table TABREF18 ) is always either a translated or semi-supervised model  Table TABREF18 also shows that, in general, our feed-forward network obtained the best results, having the highest F-score for 8 out of 10 subtasks 
However, Table TABREF19 shows that these scores can still be improved by averaging or ensembling the individual models  On the dev set, averaging our 8 individual models results in a better score for 8 out of 10 subtasks, while creating an ensemble beats all of the individual models as well as the average for each subtask  On the test set, however, only a small increase in score (if any) is found for stepwise ensembling, compared to averaging  Even though the results do not get worse, we cannot conclude that stepwise ensembling is a better method than simply averaging 
Our official scores (column Ens Test in Table TABREF19 ) have placed us second (EI-Reg, EI-Oc), fourth (V-Reg) and fifth (V-Oc) on the SemEval AIT-2018 leaderboard  However, it is evident that the results obtained on the test set are not always in line with those achieved on the development set  Especially on the anger subtask for both EI-Reg and EI-Oc, the scores are considerably lower on the test set in comparison with the results on the development set  Therefore, a small error analysis was performed on the instances where our final model made the largest errors 
Error Analysis
Due to some large differences between our results on the dev and test set of this task, we performed a small error analysis in order to see what caused these differences  For EI-Reg-anger, the gold labels were compared to our own predictions, and we manually checked 50 instances for which our system made the largest errors 
Some examples that were indicative of the shortcomings of our system are shown in Table TABREF20   First of all, our system did not take into account capitalization  The implications of this are shown in the first sentence, where capitalization intensifies the emotion used in the sentence  In the second sentence, the name Imperator Furiosa is not understood  Since our texts were lowercased, our system was unable to capture the named entity and thought the sentence was about an angry emperor instead  In the third sentence, our system fails to capture that when you are so angry that it makes you laugh, it results in a reduced intensity of the angriness  Finally, in the fourth sentence, it is the figurative language me infla la vena (it inflates my vein) that the system is not able to understand 
The first two error-categories might be solved by including smart features regarding capitalization and named entity recognition  However, the last two categories are problems of natural language understanding and will be very difficult to fix 
Conclusion
To conclude, the present study described our submission for the Semeval 2018 Shared Task on Affect in Tweets  We participated in four Spanish subtasks and our submissions ranked second, second, fourth and fifth place  Our study aimed to investigate whether the automatic generation of additional training data through translation and semi-supervised learning, as well as the creation of stepwise ensembles, increase the performance of our Spanish-language models  Strong support was found for the translation and semi-supervised learning approaches; our best models for all subtasks use either one of these approaches  These results suggest that both of these additional data resources are beneficial when determining emotion intensity (for Spanish)  However, the creation of a stepwise ensemble from the best models did not result in better performance compared to simply averaging the models  In addition, some signs of overfitting on the dev set were found  In future work, we would like to apply the methods (translation and semi-supervised learning) used on Spanish on other low-resource languages and potentially also on other tasks 
Context 3: 
Inter-annotator agreement is calculated based on two case reports  We reach a Cohen's kappa BIBREF6 of 0 68  Disagreements mainly appear for findings that are rather unspecific such as She no longer eats out with friends which can be seen as a finding referring to “avoidance behaviour” 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation Tools and Format
The annotation was performed using WebAnno BIBREF7, a web-based tool for linguistic annotation  The annotators could choose between a pre-annotated version or a blank version of each text  The pre-annotated versions contained suggested entity spans based on string matches from lists of conditions and findings synonym lists  Their quality varied widely throughout the corpus  The blank version was preferred by the annotators  We distribute the corpus in BioC JSON format  BioC was chosen as it allows us to capture the complexities of the annotations in the biomedical domain  It represented each documents properties ranging from full text, individual passages/sentences along with captured annotations and relationships in an organized manner  BioC is based on character offsets of annotations and allows the stacking of different layers 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Corpus Overview
The corpus consists of 53 documents, which contain an average number of 156 1 sentences per document, each with 19 55 tokens on average  The corpus comprises 8,275 sentences and 167,739 words in total  However, as mentioned above, only case presentation sections, headings and abstracts are annotated  The numbers of annotated entities are summarized in Table TABREF24 
Findings are the most frequently annotated type of entity  This makes sense given that findings paint a clinical picture of the patient's condition  The number of tokens per entity ranges from one token for all types to 5 tokens for cases (average length 3 1), nine tokens for conditions (average length 2 0), 16 tokens for factors (average length 2 5), 25 tokens for findings (average length 2 6) and 18 tokens for modifiers (average length 1 4) (cf  Table TABREF24)  Examples of rather long entities are given in Table TABREF25 
Entities can appear in a discontinuous way  We model this as a relation between two spans which we call “discontinuous” (cf  Figure FIGREF26)  Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations  The numbers for conditions and factors are lower with seven and two, respectively  Entities can also be nested within one another  This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf  Figure FIGREF12), or when there is a partial overlapping between the spans of two different entities (partially-nested; cf  Figure FIGREF12)  There is a high number of inter-sentential relations in the corpus (cf  Table TABREF27)  This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences 
The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case  This correlates with the high number of finding entities  The relations contained in our corpus are summarized in Table TABREF27 
Baseline systems for Named Entity Recognition in medical case reports
We evaluate the corpus using Named Entity Recognition (NER), i  e , the task of finding mentions of concepts of interest in unstructured text  We focus on detecting cases, conditions, factors, findings and modifiers in case reports (cf  Section SECREF6)  We approach this as a sequence labeling problem  Four systems were developed to offer comparable robust baselines 
The original documents are pre-processed (sentence splitting and tokenization with ScispaCy)  We do not perform stop word removal or lower-casing of the tokens  The BIO labeling scheme is used to capture the order of tokens belonging to the same entity type and enable span-level detection of entities  Detection of nested and/or discontinuous entities is not supported  The annotated corpus is randomized and split in five folds using scikit-learn BIBREF9  Each fold has a train, test and dev split with the test split defined as  15% of the train split  This ensures comparability between the presented systems 
Baseline systems for Named Entity Recognition in medical case reports ::: Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling  We use a combination of linguistic and semantic features, with a context window of size five, to describe each of the tokens and the dependencies between them  Hyper-parameter optimization is performed using randomized search and cross validation

ground_truth: column Ens Test in Table TABREF19

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 large is the corpus?
Context 3:  Span-based F1 score is used as the optimization metric 
Baseline systems for Named Entity Recognition in medical case reports ::: BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling  We use a BiLSTM-CRF model with both word-level and character-level input  BioWordVec BIBREF12 pre-trained word embeddings are used in the embedding layer for the input representation  A bidirectional LSTM layer is applied to a multiplication of the two input representations  Finally, a CRF layer is applied to predict the sequence of labels  Dropout and L1/L2 regularization is used where applicable  He (uniform) initialization BIBREF13 is used to initialize the kernels of the individual layers  As the loss metric, CRF-based loss is used, while optimizing the model based on the CRF Viterbi accuracy  Additionally, span-based F1 score is used to serialize the best performing model  We train for a maximum of 100 epochs, or until an early stopping criterion is reached (no change in validation loss value grater than 0 01 for ten consecutive epochs)  Furthermore, Adam BIBREF14 is used as the optimizer  The learning rate is reduced by a factor of 0 3 in case no significant increase of the optimization metric is achieved in three consecutive epochs 
Baseline systems for Named Entity Recognition in medical case reports ::: Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning  This model family is characterized by simultaneous optimization of multiple loss functions and transfer of knowledge achieved this way  The knowledge is transferred through the use of one or multiple shared layers  Through finding supporting patterns in related tasks, MTL provides better generalization on unseen cases and the main tasks we are trying to solve 
We rely on the model presented by bekoulis2018joint and reuse the implementation provided by the authors  The model jointly trains two objectives supported by the dataset: the main task of NER and a supporting task of Relation Extraction (RE)  Two separate models are developed for each of the tasks  The NER task is solved with the help of a BiLSTM-CRF model, similar to the one presented in Section SECREF32 The RE task is solved by using a multi-head selection approach, where each token can have none or more relationships to in-sentence tokens  Additionally, this model also leverages the output of the NER branch model (the CRF prediction) to learn label embeddings  Shared layers consist of a concatenation of word and character embeddings followed by two bidirectional LSTM layers  We keep most of the parameters suggested by the authors and change (1) the number of training epochs to 100 to allow the comparison to other deep learning approaches in this work, (2) use label embeddings of size 64, (3) allow gradient clipping and (4) use $d=0 8$ as the pre-trained word embedding dropout and $d=0 5$ for all other dropouts  $\eta =1^{-3}$ is used as the learning rate with the Adam optimizer and tanh activation functions across layers  Although it is possible to use adversarial training BIBREF16, we omit from using it  We also omit the publication of results for the task of RE as we consider it to be a supporting task and no other competing approaches have been developed 
Baseline systems for Named Entity Recognition in medical case reports ::: BioBERT
Deep neural language models have recently evolved to a successful method for representing text  In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17  For our experiments, we use BioBERT, an adaptation of BERT for the biomedical domain, pre-trained on PubMed abstracts and PMC full-text articles BIBREF18  The BERT architecture for deriving text representations uses 12 hidden layers, consisting of 768 units each  For NER, token level BIO-tag probabilities are computed with a single output layer based on the representations from the last layer of BERT  We fine-tune the model on the entity recognition task during four training epochs with batch size $b=32$, dropout probability $d=0 1$ and learning rate $\eta =2^{-5}$  These hyper-parameters are proposed by Devlin2018 for BERT fine-tuning 
Baseline systems for Named Entity Recognition in medical case reports ::: Evaluation
To evaluate the performance of the four systems, we calculate the span-level precision (P), recall (R) and F1 scores, along with corresponding micro and macro scores  The reported values are shown in Table TABREF29 and are averaged over five folds, utilising the seqeval framework 
With a macro avg  F1-score of 0 59, MTL achieves the best result with a significant margin compared to CRF, BiLSTM-CRF and BERT  This confirms the usefulness of jointly training multiple objectives (minimizing multiple loss functions), and enabling knowledge transfer, especially in a setting with limited data (which is usually the case in the biomedical NLP domain)  This result also suggest the usefulness of BioBERT for other biomedical datasets as reported by Lee2019  Despite being a rather standard approach, CRF outperforms the more elaborated BiLSTM-CRF, presumably due to data scarcity and class imbalance  We hypothesize that an increase in training data would yield better results for BiLSTM-CRF but not outperform transfer learning approach of MTL (or even BioBERT)  In contrast to other common NER corpora, like CoNLL 2003, even the best baseline system only achieves relatively low scores  This outcome is due to the inherent difficulty of the task (annotators are experienced medical doctors) and the small number of training samples 
Conclusion
We present a new corpus, developed to facilitate the processing of case reports  The corpus focuses on five distinct entity types: cases, conditions, factors, findings and modifiers  Where applicable, relationships between entities are also annotated  Additionally, we annotate discontinuous entities with a special relationship type (discontinuous)  The corpus presented in this paper is the very first of its kind and a valuable addition to the scarce number of corpora available in the field of biomedical NLP  Its complexity, given the discontinuous nature of entities and a high number of nested and multi-label entities, poses new challenges for NLP methods applied for NER and can, hence, be a valuable source for insights into what entities “look like in the wild”  Moreover, it can serve as a playground for new modelling techniques such as the resolution of discontinuous entities as well as multi-task learning given the combination of entities and their relations  We provide an evaluation of four distinct NER systems that will serve as robust baselines for future work but which are, as of yet, unable to solve all the complex challenges this dataset holds  A functional service based on the presented corpus is currently being integrated, as a NER service, in the QURATOR platform BIBREF20 
Acknowledgments
The research presented in this article is funded by the German Federal Ministry of Education and Research (BMBF) through the project QURATOR (Unternehmen Region, Wachstumskern, grant no  03WKDA1A), see http://qurator ai  We want to thank our medical experts for their help annotating the data set, especially Ashlee Finckh and Sophie Klopfenstein 

ground_truth: 8,275 sentences and 167,739 words in total

The corpus comprises 8,275 sentences and 167,739 words in total.