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 2: Introduction
Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger com or livejournal com  Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0  
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U S , with the purpose of creating insightful mappings of the blogging community  In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U S , along with a number of posts for all their associated blogs  We utilize this blog collection to generate maps of the U S  that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes  We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5  
Data Collection
Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have specific location information  Moreover, we aim to find blogs belonging to writers from all 50 U S  states, which will allow us to build U S  maps for various dimensions of interest 
We first started by collecting a set of profiles of bloggers that met our location specifications by searching individual states on the profile finder on http://www blogger com  Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth  It is important to note that the profile finder only identifies users that have an exact match to the location specified in the query; we thus built and ran queries that used both state abbreviations (e g , TX, AL), as well as the states' full names (e g , Texas, Alabama) 
After completing all the processing steps, we identified 197,527 bloggers with state location information  For each of these bloggers, we found their blogs (note that a blogger can have multiple blogs), for a total of 335,698 blogs  For each of these blogs, we downloaded the 21 most recent blog postings, which were cleaned of HTML tags and tokenized, resulting in a collection of 4,600,465 blog posts 
Maps from Blogs
Our dataset provides mappings between location, profile information, and language use, which we can leverage to generate maps that reflect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset 
People Maps
The first map we generate depicts the distribution of the bloggers in our dataset across the U S  Figure FIGREF1 shows the density of users in our dataset in each of the 50 states  For instance, the densest state was found to be California with 11,701 users  The second densest is Texas, with 9,252 users, followed by New York, with 9,136  The state with the fewest bloggers is Delaware with 1,217 users  Not surprisingly, this distribution correlates well with the population of these states, with a Spearman's rank correlation INLINEFORM0 of 0 91 and a p-value INLINEFORM1 0 0001, and is very similar to the one reported in Lin and Halavais Lin04 
Figure FIGREF1 also shows the cities mentioned most often in our dataset  In particular, it illustrates all 227 cities that have at least 100 bloggers  The bigger the dot on the map, the larger the number of users found in that city  The five top blogger-dense cities, in order, are: Chicago, New York, Portland, Seattle, and Atlanta
Context 3: Introduction
Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data  As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable  Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2  However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3  In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10  This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents 
With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented  The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing  This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact 
[1]https://play google com/store/apps/details?id=com gotokeep keep intl [2]https://play google com/store/apps/details?id=com viber voip [3]A question might not have any supporting evidence for an answer within the privacy policy 
Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations  The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day  In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent  By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain 
Related Work
Prior work has aimed to make privacy policies easier to understand  Prescriptive approaches towards communicating privacy information BIBREF21, BIBREF22, BIBREF23 have not been widely adopted by industry  Recently, there have been significant research effort devoted to understanding privacy policies by leveraging NLP techniques BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, especially by identifying specific data practices within a privacy policy  We adopt a personalized approach to understanding privacy policies, that allows users to query a document and selectively explore content salient to them  Most similar is the PolisisQA corpus BIBREF29, which examines questions users ask corporations on Twitter  Our approach differs in several ways: 1) The PrivacyQA dataset is larger, containing 10x as many questions and answers  2) Answers are formulated by domain experts with legal training  3) PrivacyQA includes diverse question types, including unanswerable and subjective questions 
Our work is also related to reading comprehension in the open domain, which is frequently based upon Wikipedia passages BIBREF16, BIBREF17, BIBREF15, BIBREF30 and news articles BIBREF20, BIBREF31, BIBREF32  Table TABREF4 presents the desirable attributes our dataset shares with past approaches  This work is also tied into research in applying NLP approaches to legal documents BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39  While privacy policies have legal implications, their intended audience consists of the general public rather than individuals with legal expertise  This arrangement is problematic because the entities that write privacy policies often have different goals than the audience  feng2015applying, tan-EtAl:2016:P16-1 examine question answering in the insurance domain, another specialized domain similar to privacy, where the intended audience is the general public 
Data Collection
We describe the data collection methodology used to construct PrivacyQA  With the goal of achieving broad coverage across application types, we collect privacy policies from 35 mobile applications representing a number of different categories in the Google Play Store  One of our goals is to include both policies from well-known applications, which are likely to have carefully-constructed privacy policies, and lesser-known applications with smaller install bases, whose policies might be considerably less sophisticated  Thus, setting 5 million installs as a threshold, we ensure each category includes applications with installs on both sides of this threshold  All policies included in the corpus are in English, and were collected before April 1, 2018, predating many companies' GDPR-focused BIBREF41 updates  We leave it to future studies BIBREF42 to look at the impact of the GDPR (e g , to what extent GDPR requirements contribute to making it possible to provide users with more informative answers, and to what extent their disclosures continue to omit issues that matter to users) 
Data Collection ::: Crowdsourced Question Elicitation
The intended audience for privacy policies consists of the general public  This informs the decision to elicit questions from crowdworkers on the contents of privacy policies  We choose not to show the contents of privacy policies to crowdworkers, a procedure motivated by a desire to avoid inadvertent biases BIBREF43, BIBREF44, BIBREF45, BIBREF46, BIBREF47, and encourage crowdworkers to ask a variety of questions beyond only asking questions based on practices described in the document 
Instead, crowdworkers are presented with public information about a mobile application available on the Google Play Store including its name, description and navigable screenshots  Figure FIGREF9 shows an example of our user interface  Crowdworkers are asked to imagine they have access to a trusted third-party privacy assistant, to whom they can ask any privacy question about a given mobile application  We use the Amazon Mechanical Turk platform and recruit crowdworkers who have been conferred “master” status and are located within the United States of America  Turkers are asked to provide five questions per mobile application, and are paid $2 per assignment, taking ~eight minutes to complete the task 
Data Collection ::: Answer Selection
To identify legally sound answers, we recruit seven experts with legal training to construct answers to Turker questions  Experts identify relevant evidence within the privacy policy, as well as provide meta-annotation on the question's relevance, subjectivity, OPP-115 category BIBREF49, and how likely any privacy policy is to contain the answer to the question asked 
Data Collection ::: Analysis
Table TABREF17 presents aggregate statistics of the PrivacyQA dataset  1750 questions are posed to our imaginary privacy assistant over 35 mobile applications and their associated privacy documents  As an initial step, we formulate the problem of answering user questions as an extractive sentence selection task, ignoring for now background knowledge, statistical data and legal expertise that could otherwise be brought to bear  The dataset is partitioned into a training set featuring 27 mobile applications and 1350 questions, and a test set consisting of 400 questions over 8 policy documents  This ensures that documents in training and test splits are mutually exclusive  Every question is answered by at least one expert  In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts 
Table TABREF14 describes the distribution over first words of questions posed by crowdworkers  We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49 94 unique questions despite crowdworkers independently posing questions  Questions are on average 8 4 words long  As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset  Privacy policies are ~3000 words long on average  The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document 
Data Collection ::: Analysis ::: Categories of Questions
Questions are organized under nine categories from the OPP-115 Corpus annotation scheme BIBREF49:
First Party Collection/Use: What, why and how information is collected by the service provider
Third Party Sharing/Collection: What, why and how information shared with or collected by third parties
Data Security: Protection measures for user information
Data Retention: How long user information will be stored
User Choice/Control: Control options available to users
User Access, Edit and Deletion: If/how users can access, edit or delete information
Policy Change: Informing users if policy information has been changed
International and Specific Audiences: Practices pertaining to a specific group of users
Other: General text, contact information or practices not covered by other categories 
For each question, domain experts indicate one or more relevant OPP-115 categories  We mark a category as relevant to a question if it is identified as such by at least two annotators  If no such category exists, the category is marked as `Other' if atleast one annotator has identified the `Other' category to be relevant  If neither of these conditions is satisfied, we label the question as having no agreement  The distribution of questions in the corpus across OPP-115 categories is as shown in Table TABREF16  First party and third party related questions are the largest categories, forming nearly 66 4% of all questions asked to the privacy assistant 
Data Collection ::: Analysis ::: Answer Validation
When do experts disagree? We would like to analyze the reasons for potential disagreement on the annotation task, to ensure disagreements arise due to valid differences in opinion rather than lack of adequate specification in annotation guidelines  It is important to note that the annotators are experts rather than crowdworkers  Accordingly, their judgements can be considered valid, legally-informed opinions even when their perspectives differ  For the sake of this question we randomly sample 100 instances in the test data and analyze them for likely reasons for disagreements  We consider a disagreement to have occurred when more than one expert does not agree with the majority consensus  By disagreement we mean there is no overlap between the text identified as relevant by one expert and another 
We find that the annotators agree on the answer for 74% of the questions, even if the supporting evidence they identify is not identical i e full overlap  They disagree on the remaining 26%  Sources of apparent disagreement correspond to situations when different experts: have differing interpretations of question intent (11%) (for example, when a user asks 'who can contact me through the app', the questions admits multiple interpretations, including seeking information about the features of the app, asking about first party collection/use of data or asking about third party collection/use of data), identify different sources of evidence for questions that ask if a practice is performed or not (4%), have differing interpretations of policy content (3%), identify a partial answer to a question in the privacy policy (2%) (for example, when the user asks `who is allowed to use the app' a majority of our annotators decline to answer, but the remaining annotators highlight partial evidence in the privacy policy which states that children under the age of 13 are not allowed to use the app), and other legitimate sources of disagreement (6%) which include personal subjective views of the annotators (for example, when the user asks `is my DNA information used in any way other than what is specified', some experts consider the boilerplate text of the privacy policy which states that it abides to practices described in the policy document as sufficient evidence to answer this question, whereas others do not) 
Experimental Setup
We evaluate the ability of machine learning methods to identify relevant evidence for questions in the privacy domain  We establish baselines for the subtask of deciding on the answerability (§SECREF33) of a question, as well as the overall task of identifying evidence for questions from policies (§SECREF37)  We describe aspects of the question that can render it unanswerable within the privacy domain (§SECREF41) 
Experimental Setup ::: Answerability Identification Baselines
We define answerability identification as a binary classification task, evaluating model ability to predict if a question can be answered, given a question in isolation  This can serve as a prior for downstream question-answering  We describe three baselines on the answerability task, and find they considerably improve performance over a majority-class baseline 
SVM: We define 3 sets of features to characterize each question  The first is a simple bag-of-words set of features over the question (SVM-BOW), the second is bag-of-words features of the question as well as length of the question in words (SVM-BOW + LEN), and lastly we extract bag-of-words features, length of the question in words as well as part-of-speech tags for the question (SVM-BOW + LEN + POS)  This results in vectors of 200, 201 and 228 dimensions respectively, which are provided to an SVM with a linear kernel 
CNN: We utilize a CNN neural encoder for answerability prediction  We use GloVe word embeddings BIBREF50, and a filter size of 5 with 64 filters to encode questions 
BERT: BERT BIBREF51 is a bidirectional transformer-based language-model BIBREF52  We fine-tune BERT-base on our binary answerability identification task with a learning rate of 2e-5 for 3 epochs, with a maximum sequence length of 128 
Experimental Setup ::: Privacy Question Answering
Our goal is to identify evidence within a privacy policy for questions asked by a user  This is framed as an answer sentence selection task, where models identify a set of evidence sentences from all candidate sentences in each policy 
Experimental Setup ::: Privacy Question Answering ::: Evaluation Metric
Our evaluation metric for answer-sentence selection is sentence-level F1, implemented similar to BIBREF30, BIBREF16  Precision and recall are implemented by measuring the overlap between predicted sentences and sets of gold-reference sentences  We report the average of the maximum F1 from each n$-$1 subset, in relation to the heldout reference 
Experimental Setup ::: Privacy Question Answering ::: Baselines
We describe baselines on this task, including a human performance baseline 
No-Answer Baseline (NA) : Most of the questions we receive are difficult to answer in a legally-sound way on the basis of information present in the privacy policy  We establish a simple baseline to quantify the effect of identifying every question as unanswerable 
Word Count Baseline : To quantify the effect of using simple lexical matching to answer the questions, we retrieve the top candidate policy sentences for each question using a word count baseline BIBREF53, which counts the number of question words that also appear in a sentence  We include the top 2, 3 and 5 candidates as baselines 
BERT: We implement two BERT-based baselines BIBREF51 for evidence identification  First, we train BERT on each query-policy sentence pair as a binary classification task to identify if the sentence is evidence for the question or not (Bert)  We also experiment with a two-stage classifier, where we separately train the model on questions only to predict answerability  At inference time, if the answerable classifier predicts the question is answerable, the evidence identification classifier produces a set of candidate sentences (Bert + Unanswerable) 
Human Performance: We pick each reference answer provided by an annotator, and compute the F1 with respect to the remaining references, as described in section 4 2 1  Each reference answer is treated as the prediction, and the remaining n-1 answers are treated as the gold reference  The average of the maximum F1 across all reference answers is computed as the human baseline 
Results and Discussion
The results of the answerability baselines are presented in Table TABREF31, and on answer sentence selection in Table TABREF32  We observe that bert exhibits the best performance on a binary answerability identification task  However, most baselines considerably exceed the performance of a majority-class baseline  This suggests considerable information in the question, indicating it's possible answerability within this domain 
Table TABREF32 describes the performance of our baselines on the answer sentence selection task  The No-answer (NA) baseline performs at 28 F1, providing a lower bound on performance at this task  We observe that our best-performing baseline, Bert + Unanswerable achieves an F1 of 39 8  This suggest that bert is capable of making some progress towards answering questions in this difficult domain, while still leaving considerable headroom for improvement to reach human performance  Bert + Unanswerable performance suggests that incorporating information about answerability can help in this difficult domain  We examine this challenging phenomena of unanswerability further in Section  
Results and Discussion ::: Error Analysis
Disagreements are analyzed based on the OPP-115 categories of each question (Table TABREF34)  We compare our best performing BERT variant against the NA model and human performance  We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories 
We analyze the performance of our strongest BERT variant, to identify classes of errors and directions for future improvement (Table 8)  We observe that a majority of answerability mistakes made by the BERT model are questions which are in fact answerable, but are identified as unanswerable by BERT  We observe that BERT makes 124 such mistakes on the test set  We collect expert judgments on relevance, subjectivity , silence and information about how likely the question is to be answered from the privacy policy from our experts  We find that most of these mistakes are relevant questions  However many of them were identified as subjective by the annotators, and at least one annotator marked 19 of these questions as having no answer within the privacy policy  However, only 6 of these questions were unexpected or do not usually have an answer in privacy policies  These findings suggest that a more nuanced understanding of answerability might help improve model performance in his challenging domain 
Results and Discussion ::: What makes Questions Unanswerable?
We further ask legal experts to identify potential causes of unanswerability of questions  This analysis has considerable implications  While past work BIBREF17 has treated unanswerable questions as homogeneous, a question answering system might wish to have different treatments for different categories of `unanswerable' questions  The following factors were identified to play a role in unanswerability:
Incomprehensibility: If a question is incomprehensible to the extent that its meaning is not intelligible 
Relevance: Is this question in the scope of what could be answered by reading the privacy policy 
Ill-formedness: Is this question ambiguous or vague  An ambiguous statement will typically contain expressions that can refer to multiple potential explanations, whereas a vague statement carries a concept with an unclear or soft definition 
Silence: Other policies answer this type of question but this one does not 
Atypicality: The question is of a nature such that it is unlikely for any policy policy to have an answer to the question 
Our experts attempt to identify the different `unanswerable' factors for all 573 such questions in the corpus  4 18% of the questions were identified as being incomprehensible (for example, `any difficulties to occupy the privacy assistant')  Amongst the comprehendable questions, 50% were identified as likely to have an answer within the privacy policy, 33 1% were identified as being privacy-related questions but not within the scope of a privacy policy (e g , 'has Viber had any privacy breaches in the past?') and 16 9% of questions were identified as completely out-of-scope (e g , `'will the app consume much space?')  In the questions identified as relevant, 32% were ill-formed questions that were phrased by the user in a manner considered vague or ambiguous  Of the questions that were both relevant as well as `well-formed', 95 7% of the questions were not answered by the policy in question but it was reasonable to expect that a privacy policy would contain an answer  The remaining 4 3% were described as reasonable questions, but of a nature generally not discussed in privacy policies  This suggests that the answerability of questions over privacy policies is a complex issue, and future systems should consider each of these factors when serving user's information seeking intent 
We examine a large-scale dataset of “natural” unanswerable questions BIBREF54 based on real user search engine queries to identify if similar unanswerability factors exist  It is important to note that these questions have previously been filtered, according to a criteria for bad questions defined as “(questions that are) ambiguous, incomprehensible, dependent on clear false presuppositions, opinion-seeking, or not clearly a request for factual information ” Annotators made the decision based on the content of the question without viewing the equivalent Wikipedia page  We randomly sample 100 questions from the development set which were identified as unanswerable, and find that 20% of the questions are not questions (e g , “all I want for christmas is you mariah carey tour”)  12% of questions are unlikely to ever contain an answer on Wikipedia, corresponding closely to our atypicality category  3% of questions are unlikely to have an answer anywhere (e g , `what guides Santa home after he has delivered presents?')  7% of questions are incomplete or open-ended (e g , `the south west wind blows across nigeria between')  3% of questions have an unresolvable coreference (e g , `how do i get to Warsaw Missouri from here')  4% of questions are vague, and a further 7% have unknown sources of error  2% still contain false presuppositions (e g , `what is the only fruit that does not have seeds?') and the remaining 42% do not have an answer within the document  This reinforces our belief that though they have been understudied in past work, any question answering system interacting with real users should expect to receive such unanticipated and unanswerable questions 
Conclusion
We present PrivacyQA, the first significant corpus of privacy policy questions and more than 3500 expert annotations of relevant answers  The goal of this work is to promote question-answering research in the specialized privacy domain, where it can have large real-world impact  Strong neural baselines on PrivacyQA achieve a performance of only 39 8 F1 on this corpus, indicating considerable room for future research  Further, we shed light on several important considerations that affect the answerability of questions  We hope this contribution leads to multidisciplinary efforts to precisely understand user intent and reconcile it with information in policy documents, from both the privacy and NLP communities 
Acknowledgements
This research was supported in part by grants from the National Science Foundation Secure and Trustworthy Computing program (CNS-1330596, CNS-1330214, CNS-15-13957, CNS-1801316, CNS-1914486, CNS-1914444) and a DARPA Brandeis grant on Personalized Privacy Assistants (FA8750-15-2-0277)  The US Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NSF, DARPA, or the US Government  The authors would like to extend their gratitude to Elias Wright, Gian Mascioli, Kiara Pillay, Harrison Kay, Eliel Talo, Alexander Fagella and N  Cameron Russell for providing their valuable expertise and insight to this effort  The authors are also grateful to Eduard Hovy, Lorrie Cranor, Florian Schaub, Joel Reidenberg, Aditya Potukuchi and Igor Shalyminov for helpful discussions related to this work, and to the three anonymous reviewers of this draft for their constructive feedback  Finally, the authors would like to thank all crowdworkers who consented to participate in this study 

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. How do they obtain psychological dimensions of people?
Context 2: 
We also generate two maps that delineate the gender distribution in the dataset  Overall, the blogging world seems to be dominated by females: out of 153,209 users who self-reported their gender, only 52,725 are men and 100,484 are women  Figures FIGREF1 and FIGREF1 show the percentage of male and female bloggers in each of the 50 states  As seen in this figure, there are more than the average number of male bloggers in states such as California and New York, whereas Utah and Idaho have a higher percentage of women bloggers 
Another profile element that can lead to interesting maps is the Industry field BIBREF6   Using this field, we created different maps that plot the geographical distribution of industries across the country  As an example, Figure FIGREF2 shows the percentage of the users in each state working in the automotive and tourism industries respectively 
Linguistic Maps
Another use of the information found in our dataset is to build linguistic maps, which reflect the geographic lexical variation across the 50 states BIBREF7   We generate maps that represent the relative frequency by which a word occurs in the different states  Figure FIGREF3 shows sample maps created for two different words  The figure shows the map generated for one location specific word, Maui, which unsurprisingly is found predominantly in Hawaii, and a map for a more common word, lake, which has a high occurrence rate in Minnesota (Land of 10,000 Lakes) and Utah (home of the Great Salt Lake)  Our demo described in Section SECREF4 , can also be used to generate maps for function words, which can be very telling regarding people's personality BIBREF8  
Psycholinguistic and Semantic Maps
LIWC  In addition to individual words, we can also create maps for word categories that reflect a certain psycholinguistic or semantic property  Several lexical resources, such as Roget or Linguistic Inquiry and Word Count BIBREF9 , group words into categories  Examples of such categories are Money, which includes words such as remuneration, dollar, and payment; or Positive feelings with words such as happy, cheerful, and celebration  Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories  For instance, figure FIGREF8 shows the maps created for two categories: Positive Feelings and Money  The maps are not surprising, and interestingly they also reflect an inverse correlation between Money and Positive Feelings  
Values  We also measure the usage of words related to people's core values as reported by Boyd et al  boyd2015  The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10   MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from the U S  who were asked to freely write about their personal values  To illustrate, Figure FIGREF9 shows the geographical distributions of two of these value themes: Religion and Hard Work  Southeastern states often considered as the nation's “Bible Belt” BIBREF11 were found to have generally higher usage of Religion words such as God, bible, and church  Another broad trend was that western-central states (e g , Wyoming, Nebraska, Iowa) commonly blogged about Hard Work, using words such as hard, work, and job more often than bloggers in other regions 
Web Demonstration
A prototype, interactive charting demo is available at http://lit eecs umich edu/~geoliwc/  In addition to drawing maps of the geographical distributions on the different LIWC categories, the tool can report the three most and least correlated LIWC categories in the U S  and compare the distributions of any two categories 
Conclusions
In this paper, we showed how we can effectively leverage a prodigious blog dataset  Not only does the dataset bring out the extensive linguistic content reflected in the blog posts, but also includes location information and rich metadata  These data allow for the generation of maps that reflect the demographics of the population, variations in language use, and differences in psycholinguistic and semantic categories  These mappings can be valuable to both psychologists and linguists, as well as lexicographers  A prototype demo has been made available together with the code used to collect our dataset 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation  We would like to thank our colleagues Hengjing Wang, Jiatao Fan, Xinghai Zhang, and Po-Jung Huang who provided technical help with the implementation of the demo 

ground_truth: using the Meaning Extraction Method

Unanswerable

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. Who were the experts used for annotation?
Context 4: 5 after embedding layer, the output between LSTM layers, and the output of final LSTM layer  (2) Advanced dropout: We use the same dropout techniques with the same dropout values as in BIBREF0   We call this model as AWD-PRU 
Results
Table TABREF23 compares the performance of the PRU with state-of-the-art methods  We can see that the PRU achieves the best performance with fewer parameters 
PRUs achieve either the same or better performance than LSTMs  In particular, the performance of PRUs improves with the increasing value of INLINEFORM0   At INLINEFORM1 , PRUs outperform LSTMs by about 4 points on the PTB dataset and by about 3 points on the WT-2 dataset  This is explained in part by the regularization effect of the grouped linear transformation (Figure FIGREF1 )  With grouped linear and pyramidal transformations, PRUs learn rich representations at very high dimensional space while learning fewer parameters  On the other hand, LSTMs overfit to the training data at such high dimensions and learn INLINEFORM2 to INLINEFORM3 more parameters than PRUs 
With the advanced dropouts, the performance of PRUs improves by about 4 points on the PTB dataset and 7 points on the WT-2 dataset  This further improves with finetuning on the PTB (about 2 points) and WT-2 (about 1 point) datasets 
For similar number of parameters, the PRU with standard dropout outperforms most of the state-of-the-art methods by large margin on the PTB dataset (e g  RAN BIBREF7 by 16 points with 4M less parameters, QRNN BIBREF33 by 16 points with 1M more parameters, and NAS BIBREF31 by 1 58 points with 6M less parameters)  With advanced dropouts, the PRU delivers the best performance  On both datasets, the PRU improves the perplexity by about 1 point while learning 15-20% fewer parameters 
PRU is a drop-in replacement for LSTM, therefore, it can improve language models with modern inference techniques such as dynamic evaluation BIBREF21   When we evaluate PRU-based language models (only with standard dropout) with dynamic evaluation on the PTB test set, the perplexity of PRU ( INLINEFORM0 ) improves from 62 42 to 55 23 while the perplexity of an LSTM ( INLINEFORM1 ) with similar settings improves from 66 29 to 58 79; suggesting that modern inference techniques are equally applicable to PRU-based language models 
Analysis
It is shown above that the PRU can learn representations at higher dimensionality with more generalization power, resulting in performance gains for language modeling  A closer analysis of the impact of the PRU in a language modeling system reveals several factors that help explain how the PRU achieves these gains 
As exemplified in Table TABREF34 , the PRU tends toward more confident decisions, placing more of the probability mass on the top next-word prediction than the LSTM  To quantify this effect, we calculate the entropy of the next-token distribution for both the PRU and the LSTM using 3687 contexts from the PTB validation set  Figure FIGREF32 shows a histogram of the entropies of the distribution, where bins of size 0 23 are used to effect categories  We see that the PRU more often produces lower entropy distributions corresponding to higher confidences for next-token choices  This is evidenced by the mass of the red PRU curve lying in the lower entropy ranges compared to the blue LSTM's curve  The PRU can produce confident decisions in part because more information is encoded in the higher dimensional context vectors 
The PRU has the ability to model individual words at different resolutions through the pyramidal transform; which provides multiple paths for the gradient to the embedding layer (similar to multi-task learning) and improves the flow of information  When considering the embeddings by part of speech, we find that the pyramid level 1 embeddings exhibit higher variance than the LSTM across all POS categories (Figure FIGREF33 ), and that pyramid level 2 embeddings show extremely low variance  We hypothesize that the LSTM must encode both coarse group similarities and individual word differences into the same vector space, reducing the space between individual words of the same category  The PRU can rely on the subsampled embeddings to account for coarse-grained group similarities, allowing for finer individual word distinctions in the embedding layer  This hypothesis is strengthened by the entropy results described above: a model which can make finer distinctions between individual words can more confidently assign probability mass  A model that cannot make these distinctions, such as the LSTM, must spread its probability mass across a larger class of similar words 
Saliency analysis using gradients help identify relevant words in a test sequence that contribute to the prediction BIBREF34 , BIBREF35 , BIBREF36   These approaches compute the relevance as the squared norm of the gradients obtained through back-propagation  Table TABREF34 visualizes the heatmaps for different sequences  PRUs, in general, give more relevance to contextual words than LSTMs, such as southeast (sample 1), cost (sample 2), face (sample 4), and introduced (sample 5), which help in making more confident decisions  Furthermore, when gradients during back-propagation are visualized BIBREF37 (Table TABREF34 ), we find that PRUs have better gradient coverage than LSTMs, suggesting PRUs use more features than LSTMs that contributes to the decision  This also suggests that PRUs update more parameters at each iteration which results in faster training  Language model in BIBREF0 takes 500 and 750 epochs to converge with PRU and LSTM as a recurrent unit, respectively 
Ablation studies
In this section, we provide a systematic analysis of our design choices  Our training methodology is the same as described in Section SECREF19 with the standard dropouts  For a thorough understanding of our design choices, we use a language model with a single layer of PRU and fix the size of embedding and hidden layers to 600  The word-level perplexities are reported on the validation sets of the PTB and the WT-2 datasets 
The two hyper-parameters that control the trade-off between performance and number of parameters in PRUs are the number of pyramidal levels INLINEFORM0 and groups INLINEFORM1   Figure FIGREF35 provides a trade-off between perplexity and recurrent unit (RU) parameters 
Variable INLINEFORM0 and fixed INLINEFORM1 : When we increase the number of pyramidal levels INLINEFORM2 at a fixed value of INLINEFORM3 , the performance of the PRU drops by about 1 to 4 points while reducing the total number of recurrent unit parameters by up to 15%  We note that the PRU with INLINEFORM4 at INLINEFORM5 delivers similar performance as the LSTM while learning about 15% fewer recurrent unit parameters 
Fixed INLINEFORM0 and variable INLINEFORM1 : When we vary the value of INLINEFORM2 at fixed number of pyramidal levels INLINEFORM3 , the total number of recurrent unit parameters decreases significantly with a minimal impact on the perplexity  For example, PRUs with INLINEFORM4 and INLINEFORM5 learns 77% fewer recurrent unit parameters while its perplexity (lower is better) increases by about 12% in comparison to LSTMs  Moreover, the decrease in number of parameters at higher value of INLINEFORM6 enables PRUs to learn the representations in high dimensional space with better generalizability (Table TABREF23 ) 
Table TABREF43 shows the impact of different transformations of the input vector INLINEFORM0 and the context vector INLINEFORM1   We make following observations: (1) Using the pyramidal transformation for the input vectors improves the perplexity by about 1 point on both the PTB and WT-2 datasets while reducing the number of recurrent unit parameters by about 14% (see R1 and R4)  We note that the performance of the PRU drops by up to 1 point when residual connections are not used (R4 and R6)  (2) Using the grouped linear transformation for context vectors reduces the total number of recurrent unit parameters by about 75% while the performance drops by about 11% (see R3 and R4)  When we use the pyramidal transformation instead of the linear transformation, the performance drops by up to 2% while there is no significant drop in the number of parameters (R4 and R5) 
We set sub-sampling kernel INLINEFORM0 (Eq  EQREF12 ) with stride INLINEFORM1 and size of 3 ( INLINEFORM2 ) in four different ways: (1) Skip: We skip every other element in the input vector  (2) Convolution: We initialize the elements of INLINEFORM3 randomly from normal distribution and learn them during training the model  We limit the output values between -1 and 1 using INLINEFORM4 activation function to make training stable  (3) Avg  pool: We initialize the elements of INLINEFORM5 to INLINEFORM6   (4) Max pool: We select the maximum value in the kernel window INLINEFORM7  
Table TABREF45 compares the performance of the PRU with different sampling methods  Average pooling performs the best while skipping give comparable performance  Both of these methods enable the network to learn richer word representations while representing the input vector in different forms, thus delivering higher performance  Surprisingly, a convolution-based sub-sampling method does not perform as well as the averaging method  The INLINEFORM0 function used after convolution limits the range of output values which are further limited by the LSTM gating structure, thereby impeding in the flow of information inside the cell  Max pooling forces the network to learn representations from high magnitude elements, thus distinguishing features between elements vanishes, resulting in poor performance 
Conclusion
We introduce the Pyramidal Recurrent Unit, which better model contextual information by admitting higher dimensional representations with good generalizability  When applied to the task of language modeling, PRUs improve perplexity across several settings, including recent state-of-the-art systems  Our analysis shows that the PRU improves the flow of gradient and expand the word embedding subspace, resulting in more confident decisions  Here we have shown improvements for language modeling  In future, we plan to study the performance of PRUs on different tasks, including machine translation and question answering  In addition, we will study the performance of the PRU on language modeling with more recent inference techniques, such as dynamic evaluation and mixture of softmax 
Acknowledgments
This research was supported by NSF (IIS 1616112, III 1703166), Allen Distinguished Investigator Award, and gifts from Allen Institute for AI, Google, Amazon, and Bloomberg  We are grateful to Aaron Jaech, Hannah Rashkin, Mandar Joshi, Aniruddha Kembhavi, and anonymous reviewers for their helpful comments 

ground_truth: Individuals with legal training

Yes