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. Do they use attention?
Context 1: Background
Teaching machine to read and comprehend a given passage/paragraph and answer its corresponding questions is a challenging task  It is also one of the long-term goals of natural language understanding, and has important applications in e g , building intelligent agents for conversation and customer service support  In a real world setting, it is necessary to judge whether the given questions are answerable given the available knowledge, and then generate correct answers for the ones which are able to infer an answer in the passage or an empty answer (as an unanswerable question) otherwise 
In comparison with many existing MRC systems BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , which extract answers by finding a sub-string in the passages/paragraphs, we propose a model that not only extracts answers but also predicts whether such an answer should exist  Using a multi-task learning approach (c f  BIBREF5 ), we extend the Stochastic Answer Network (SAN) BIBREF1 for MRC answer span detector to include a classifier that whether the question is unanswerable  The unanswerable classifier is a pair-wise classification model BIBREF6 which predicts a label indicating whether the given pair of a passage and a question is unanswerable  The two models share the same lower layer to save the number of parameters, and separate the top layers for different tasks (the span detector and binary classifier) 
Our model is pretty simple and intuitive, yet efficient  Without relying on the large pre-trained language models (ELMo) BIBREF7 , the proposed model achieves competitive results to the state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2 0 
The contribution of this work is summarized as follows  First, we propose a simple yet efficient model for MRC that handles unanswerable questions and is optimized jointly  Second, our model achieves competitive results on SQuAD v2 0 
Model
The Machine Reading Comprehension is a task which takes a question INLINEFORM0 and a passage/paragraph INLINEFORM1 as inputs, and aims to find an answer span INLINEFORM2 in INLINEFORM3   We assume that if the question is answerable, the answer INLINEFORM4 exists in INLINEFORM5 as a contiguous text string; otherwise, INLINEFORM6 is an empty string indicating an unanswerable question  Note that to handle the unanswerable questions, we manually append a dumpy text string NULL at the end of each corresponding passage/paragraph  Formally, the answer is formulated as INLINEFORM7   In case of unanswerable questions, INLINEFORM8 points to the last token of the passage 
Our model is a variation of SAN BIBREF1 , as shown in Figure FIGREF2   The main difference is the additional binary classifier added in the model justifying whether the question is unanswerable  Roughly, the model includes two different layers: the shared layer and task specific layer  The shared layer is almost identical to the lower layers of SAN, which has a lexicon encoding layer, a contextual layer and a memory generation layer  On top of it, there are different answer modules for different tasks  We employ the SAN answer module for the span detector and a one-layer feed forward neural network for the binary classification task  It can also be viewed as a multi-task learning BIBREF8 , BIBREF5 , BIBREF9   We will briefly describe the model from ground up as follows  Detailed descriptions can be found in BIBREF1  
Lexicon Encoding Layer  We map the symbolic/surface feature of INLINEFORM0 and INLINEFORM1 into neural space via word embeddings , 16-dim part-of-speech (POS) tagging embeddings, 8-dim named-entity embeddings and 4-dim hard-rule features  Note that we use small embedding size of POS and NER to reduce model size and they mainly serve the role of coarse-grained word clusters  Additionally, we use question enhanced passages word embeddings which can viewwed as soft matching between questions and passages  At last, we use two separate two-layer position-wise Feed-Forward Networks (FFN) BIBREF11 , BIBREF1 to map both question and passage encodings into the same dimension  As results, we obtain the final lexicon embeddings for the tokens for INLINEFORM2 as a matrix INLINEFORM3 , and tokens in INLINEFORM4 as INLINEFORM5  
Contextual Encoding Layer  A shared two-layers BiLSTM is used on the top to encode the contextual information of both passages and questions  To avoid overfitting, we concatenate a pre-trained 600-dimensional CoVe vectors BIBREF12 trained on German-English machine translation dataset, with the aforementioned lexicon embeddings as the final input of the contextual encoding layer, and also with the output of the first contextual encoding layer as the input of its second encoding layer  Thus, we obtain the final representation of the contextual encoding layer by a concatenation of the outputs of two BiLSTM: INLINEFORM0 for questions and INLINEFORM1 for passages 
Memory Generation Layer  In this layer, we generate a working memory by fusing information from both passages INLINEFORM0 and questions INLINEFORM1   The attention function BIBREF11 is used to compute the similarity score between passages and questions as: INLINEFORM2
Note that INLINEFORM0 and INLINEFORM1 is transformed from INLINEFORM2 and INLINEFORM3 by one layer neural network INLINEFORM4 , respectively  A question-aware passage representation is computed as INLINEFORM5   After that, we use the method of BIBREF13 to apply self attention to the passage: INLINEFORM6
where INLINEFORM0 means that we only drop diagonal elements on the similarity matrix (i e , attention with itself)  At last, INLINEFORM1 and INLINEFORM2 are concatenated and are passed through a BiLSTM to form the final memory: INLINEFORM3  
Span detector  We adopt a multi-turn answer module for the span detector BIBREF1   Formally, at time step INLINEFORM0 in the range of INLINEFORM1 , the state is defined by INLINEFORM2   The initial state INLINEFORM3 is the summary of the INLINEFORM4 : INLINEFORM5 , where INLINEFORM6   Here, INLINEFORM7 is computed from the previous state INLINEFORM8 and memory INLINEFORM9 : INLINEFORM10 and INLINEFORM11   Finally, a bilinear function is used to find the begin and end point of answer spans at each reasoning step INLINEFORM12 : DISPLAYFORM0 DISPLAYFORM1
The final prediction is the average of each time step: INLINEFORM0   We randomly apply dropout on the step level in each time step during training, as done in BIBREF1  
Unanswerable classifier  We adopt a one-layer neural network as our unanswerable binary classifier: DISPLAYFORM0
, where INLINEFORM0 is the summary of the memory: INLINEFORM1 , where INLINEFORM2   INLINEFORM3 denotes the probability of the question which is unanswerable 
Objective The objective function of the joint model has two parts: DISPLAYFORM0
Following BIBREF0 , the span loss function is defined: DISPLAYFORM0
The objective function of the binary classifier is defined: DISPLAYFORM0
where INLINEFORM0 is a binary variable: INLINEFORM1 indicates the question is unanswerable and INLINEFORM2 denotes the question is answerable 
Setup
We evaluate our system on SQuAD 2 0 dataset BIBREF14 , a new MRC dataset which is a combination of Stanford Question Answering Dataset (SQuAD) 1 0 BIBREF15 and additional unanswerable question-answer pairs  The answerable pairs are around 100K; while the unanswerable questions are around 53K  This dataset contains about 23K passages and they come from approximately 500 Wikipedia articles  All the questions and answers are obtained by crowd-sourcing  Two evaluation metrics are used: Exact Match (EM) and Macro-averaged F1 score (F1) BIBREF14  
Implementation details
We utilize spaCy tool to tokenize the both passages and questions, and generate lemma, part-of-speech and named entity tags  The word embeddings are initialized with pre-trained 300-dimensional GloVe BIBREF10   A 2-layer BiLSTM is used encoding the contextual information of both questions and passages  Regarding the hidden size of our model, we search greedily among INLINEFORM0   During training, Adamax BIBREF16 is used as our optimizer  The min-batch size is set to 32  The learning rate is initialized to 0 002 and it is halved after every 10 epochs  The dropout rate is set to 0 1  To prevent overfitting, we also randomly set 0 5% words in both passages and questions as unknown words during the training  Here, we use a special token unk to indicate a word which doesn't appear in GloVe  INLINEFORM1 in Eq EQREF9 is set to 1 
Results
We would like to investigate effectiveness the proposed joint model  To do so, the same shared layer/architecture is employed in the following variants of the proposed model:
The results in terms of EM and F1 is summarized in Table TABREF20   We observe that Joint SAN outperforms the SAN baseline with a large margin, e g , 67 89 vs 69 27 (+1 38) and 70 68 vs 72 20 (+1 52) in terms of EM and F1 scores respectively, so it demonstrates the effectiveness of the joint optimization  By incorporating the output information of classifier into Joint SAN, it obtains a slight improvement, e g , 72 2 vs 72 66 (+0 46) in terms of F1 score  By analyzing the results, we found that in most cases when our model extract an NULL string answer, the classifier also predicts it as an unanswerable question with a high probability 
Table TABREF21 reports comparison results in literature published   Our model achieves state-of-the-art on development dataset in setting without pre-trained large language model (ELMo)  Comparing with the much complicated model R M -Reader + Verifier, which includes several components, our model still outperforms by 0 7 in terms of F1 score  Furthermore, we observe that ELMo gives a great boosting on the performance, e g , 2 8 points in terms of F1 for DocQA  This encourages us to incorporate ELMo into our model in future 
Analysis  To better understand our model, we analyze the accuracy of the classifier in our joint model  We obtain 75 3 classification accuracy on the development with the threshold 0 5  By increasing value of INLINEFORM0 in Eq EQREF9 , the classification accuracy reached to 76 8 ( INLINEFORM1 ), however the final results of our model only have a small improvement (+0 2 in terms of F1 score)  It shows that it is important to make balance between these two components: the span detector and unanswerable classifier 
Conclusion
To sum up, we proposed a simple yet efficient model based on SAN  It showed that the joint learning algorithm boosted the performance on SQuAD 2 0  We also would like to incorporate ELMo into our model in future 
Acknowledgments
We thank Yichong Xu, Shuohang Wang and Sheng Zhang for valuable discussions and comments  We also thank Robin Jia for the help on SQuAD evaluations 
Context 2: Introduction
In June 2015, the operators of the online discussion site Reddit banned several communities under new anti-harassment rules  BIBREF0 used this opportunity to combine rich online data with computational methods to study a current question: Does eliminating these “echo chambers” diminish the amount of hate speech overall? Exciting opportunities like these, at the intersection of “thick” cultural and societal questions on the one hand, and the computational analysis of rich textual data on larger-than-human scales on the other, are becoming increasingly common 
Indeed, computational analysis is opening new possibilities for exploring challenging questions at the heart of some of the most pressing contemporary cultural and social issues  While a human reader is better equipped to make logical inferences, resolve ambiguities, and apply cultural knowledge than a computer, human time and attention are limited  Moreover, many patterns are not obvious in any specific context, but only stand out in the aggregate  For example, in a landmark study, BIBREF1 analyzed the authorship of The Federalist Papers using a statistical text analysis by focusing on style, based on the distribution of function words, rather than content  As another example, BIBREF2 studied what defines English haiku and showed how computational analysis and close reading can complement each other  Computational approaches are valuable precisely because they help us identify patterns that would not otherwise be discernible 
Yet these approaches are not a panacea  Examining thick social and cultural questions using computational text analysis carries significant challenges  For one, texts are culturally and socially situated  They reflect the ideas, values and beliefs of both their authors and their target audiences, and such subtleties of meaning and interpretation are difficult to incorporate in computational approaches  For another, many of the social and cultural concepts we seek to examine are highly contested — hate speech is just one such example  Choices regarding how to operationalize and analyze these concepts can raise serious concerns about conceptual validity and may lead to shallow or obvious conclusions, rather than findings that reflect the depth of the questions we seek to address 
These are just a small sample of the many opportunities and challenges faced in computational analyses of textual data  New possibilities and frustrating obstacles emerge at every stage of research, from identification of the research question to interpretation of the results  In this article, we take the reader through a typical research process that involves measuring social or cultural concepts using computational methods, discussing both the opportunities and complications that often arise  In the Reddit case, for example, hate speech is measured, however imperfectly, by the presence of particular words semi-automatically extracted from a machine learning algorithm  Operationalizations are never perfect translations, and are often refined over the course of an investigation, but they are crucial 
We begin our exploration with the identification of research questions, proceed through data selection, conceptualization, and operationalization, and end with analysis and the interpretation of results  The research process sounds more or less linear this way, but each of these phases overlaps, and in some instances turns back upon itself  The analysis phase, for example, often feeds back into the original research questions, which may continue to evolve for much of the project  At each stage, our discussion is critically informed by insights from the humanities and social sciences, fields that have focused on, and worked to tackle, the challenges of textual analysis—albeit at smaller scales—since their inception 
In describing our experiences with computational text analysis, we hope to achieve three primary goals  First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods  Second, we hope to provide a set of best practices for working with thick social and cultural concepts  Our guidance is based on our own experiences and is therefore inherently imperfect  Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many  And this leads to our final goal: to help promote interdisciplinary collaborations  Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be 
Research questions
We typically start by identifying the questions we wish to explore  Can text analysis provide a new perspective on a “big question” that has been attracting interest for years? Or can we raise new questions that have only recently emerged, for example about social media? For social scientists working in computational analysis, the questions are often grounded in theory, asking: How can we explain what we observe? These questions are also influenced by the availability and accessibility of data sources  For example, the choice to work with data from a particular social media platform may be partly determined by the fact that it is freely available, and this will in turn shape the kinds of questions that can be asked  A key output of this phase are the concepts to measure, for example: influence; copying and reproduction; the creation of patterns of language use; hate speech  Computational analysis of text motivated by these questions is insight driven: we aim to describe a phenomenon or explain how it came about  For example, what can we learn about how and why hate speech is used or how this changes over time? Is hate speech one thing, or does it comprise multiple forms of expression? Is there a clear boundary between hate speech and other types of speech, and what features make it more or less ambiguous? In these cases, it is critical to communicate high-level patterns in terms that are recognizable 
This contrasts with much of the work in computational text analysis, which tends to focus on automating tasks that humans perform inefficiently  These tasks range from core linguistically motivated tasks that constitute the backbone of natural language processing, such as part-of-speech tagging and parsing, to filtering spam and detecting sentiment  Many tasks are motivated by applications, for example to automatically block online trolls  Success, then, is often measured by performance, and communicating why a certain prediction was made—for example, why a document was labeled as positive sentiment, or why a word was classified as a noun—is less important than the accuracy of the prediction itself  The approaches we use and what we mean by `success' are thus guided by our research questions 
Domain experts and fellow researchers can provide feedback on questions and help with dynamically revising them  For example, they may say “we already think we know that”, “that's too naïve”, “that doesn't reflect social reality” (negative); “two major camps in the field would give different answers to that question” (neutral); “we tried to look at that back in the 1960s, but we didn't have the technology” (positive); and “that sounds like something that people who made that archive would love”, “that's a really fundamental question” (very positive) 
Sometimes we also hope to connect to multiple disciplines  For example, while focusing on the humanistic concerns of an archive, we could also ask social questions such as “is this archive more about collaborative processes, culture-building or norm creation?” or “how well does this archive reflect the society in which it is embedded?" BIBREF3 used quantitative methods to tell a story about Darwin's intellectual development—an essential biographical question for a key figure in the history of science  At the same time, their methods connected Darwin's development to the changing landscape of Victorian scientific culture, allowing them to contrast Darwin's “foraging” in the scientific literature of his time to the ways in which that literature was itself produced  Finally, their methods provided a case study, and validation of technical approaches, for cognitive scientists who are interested in how people explore and exploit sources of knowledge 
Questions about potential “dual use” may also arise  Returning to our introductory example, BIBREF0 started with a deceptively simple question: if an internet platform eliminates forums for hate speech, does this impact hate speech in other forums? The research was motivated by the belief that a rising tide of online hate speech was (and is) making the internet increasingly unfriendly for disempowered groups, including minorities, women, and LBGTQ individuals  Yet the possibility of dual use troubled the researchers from the onset  Could the methodology be adopted to target the speech of groups like Black Lives Matter? Could it be adopted by repressive governments to minimize online dissent? While these concerns remained, they concluded that hypothetical dual use scenarios did not outweigh the tangible contribution this research could offer towards making the online environment more equal and just 
Data
The next step involves deciding on the data sources, collecting and compiling the dataset, and inspecting its metadata 
Data acquisition
Many scholars in the humanities and the social sciences work with sources that are not available in digital form, and indeed may never be digitized  Others work with both analogue and digitized materials, and the increasing digitization of archives has opened opportunities to study these archives in new ways  We can go to the canonical archive or open up something that nobody has studied before  For example, we might focus on major historical moments (French Revolution, post-Milosevic Serbia) or critical epochs (Britain entering the Victorian era, the transition from Latin to proto-Romance)  Or, we could look for records of how people conducted science, wrote and consumed literature, and worked out their philosophies 
A growing number of researchers work with born-digital sources or data  Born-digital data, e g , from social media, generally do not involve direct elicitation from participants and therefore enable unobtrusive measurements BIBREF5 , BIBREF6   In contrast, methods like surveys sometimes elicit altered responses from participants, who might adapt their responses to what they think is expected  Moreover, born-digital data is often massive, enabling large-scale studies of language and behavior in a variety of social contexts 
Still, many scholars in the social sciences and humanities work with multiple data sources  The variety of sources typically used means that more than one data collection method is often required  For example, a project examining coverage of a UK General Election, could draw data from traditional media, web archives, Twitter and Facebook, campaign manifestos, etc  and might combine textual analysis of these materials with surveys, laboratory experiments, or field observations offline  In contrast, many computational studies based on born-digital data have focused on one specific source, such as Twitter 
The use of born-digital data raises ethical concerns  Although early studies often treated privacy as a binary construct, many now acknowledge its complexity BIBREF7   Conversations on private matters can be posted online, visible for all, but social norms regarding what should be considered public information may differ from the data's explicit visibility settings  Often no informed consent has been obtained, raising concerns and challenges regarding publishing content and potentially harmful secondary uses BIBREF8 , BIBREF4  
Recently, concerns about potential harms stemming from secondary uses have led a number of digital service providers to restrict access to born-digital data  Facebook and Twitter, for example, have reduced or eliminated public access to their application programming interfaces (APIs) and expressed hesitation about allowing academic researchers to use data from their platforms to examine certain sensitive or controversial topics  Despite the seeming abundance of born-digital data, we therefore cannot take its availability for granted 
Working with data that someone else has acquired presents additional problems related to provenance and contextualisation  It may not always be possible to determine the criteria applied during the creation process  For example, why were certain newspapers digitized but not others, and what does this say about the collection? Similar questions arise with the use of born-digital data  For instance, when using the Internet Archive’s Wayback Machine to gather data from archived web pages, we need to consider what pages were captured, which are likely missing, and why 
We must often repurpose born-digital data (e g , Twitter was not designed to measure public opinion), but data biases may lead to spurious results and limit justification for generalization  In particular, data collected via black box APIs designed for commercial, not research, purposes are likely to introduce biases into the inferences we draw, and the closed nature of these APIs means we rarely know what biases are introduced, let alone how severely they might impact our research BIBREF10   These, however, are not new problems  Historians, for example, have always understood that their sources were produced within particular contexts and for particular purposes, which are not always apparent to us 
Non-representative data can still be useful for making comparisons within a sample  In the introductory example on hate speech BIBREF0 , the Reddit forums do not present a comprehensive or balanced picture of hate speech: the writing is almost exclusively in English, the targets of hate speech are mainly restricted (e g , to black people, or women), and the population of writers is shaped by Reddit's demographics, which skew towards young white men  These biases limit the generalizability of the findings, which cannot be extrapolated to other languages, other types of hate speech, and other demographic groups  However, because the findings are based on measurements on the same sort of hate speech and the same population of writers, as long as the collected data are representative of this specific population, these biases do not pose an intractable validity problem if claims are properly restricted 
The size of many newly available datasets is one of their most appealing characteristics  Bigger datasets often make statistics more robust  The size needed for a computational text analysis depends on the research goal: When it involves studying rare events, bigger datasets are needed  However, larger is not always better  Some very large archives are “secretly” collections of multiple and distinct processes that no in-field scholar would consider related  For example, Google Books is frequently used to study cultural patterns, but the over-representation of scientific articles in Google books can be problematic BIBREF11   Even very large born-digital datasets usually cover limited timespans compared to, e g , the Gutenberg archive of British novels 
This stage of the research also raises important questions about fairness  Are marginalized groups, for example, represented in the tweets we have collected? If not, what types of biases might result from analyses relying on those tweets?
Local experts and “informants” can help navigate the data  They can help understand the role an archive plays in the time and place  They might tell us: Is this the central archive, or a peripheral one? What makes it unusual? Or they might tell us how certain underrepresented communities use a social media platform and advise us on strategies for ensuring our data collection includes their perspectives 
However, when it is practically infeasible to navigate the data in this way—for instance, when we cannot determine what is missing from Twitter's Streaming API or what webpages are left out of the Internet Archive—we should be open about the limitations of our analyses, acknowledging the flaws in our data and drawing cautious and reasonable conclusions from them  In all cases, we should report the choices we have made when creating or re-using any dataset 
Compiling data
After identifying the data source(s), the next step is compiling the data  This step is fundamental: if the sources cannot support a convincing result, no result will be convincing  In many cases, this involves defining a “core" set of documents and a “comparison" set  We often have a specific set of documents in mind: an author's work, a particular journal, a time period  But if we want to say that this “core" set has some distinctive property, we need a “comparison" set  Expanding the collection beyond the documents that we would immediately think of has the beneficial effect of increasing our sample size  Having more sources increases the chance that we will notice something consistent across many individually varying contexts 
Comparing sets of documents can sometimes support causal inference, presented as a contrast between a treatment group and a control  In BIBREF0 , the treatment consisted of the text written in the two forums that were eventually closed by Reddit  However, identifying a control group required a considerable amount of time and effort  Reddit is a diverse platform, with a wide variety of interactional and linguistic styles; it would be pointless to compare hate speech forums against forums dedicated to, say, pictures of wrecked bicycles  Chandrasekharan et al  used a matching design, populating the control group with forums that were as similar as possible to the treatment group, but were not banned from Reddit  The goal is to estimate the counterfactual scenario: in this case, what would have happened had the site not taken action against these specific forums? An ideal control would make it possible to distinguish the effect of the treatment — closing the forums — from other idiosyncratic properties of texts that were treated 
We also look for categories of documents that might not be useful  We might remove documents that are meta-discourse, like introductions and notes, or documents that are in a language that is not the primary language of the collection, or duplicates when we are working with archived web pages  However, we need to carefully consider the potential consequences of information we remove  Does its removal alter the data, or the interpretation of the data, we are analyzing? Are we losing anything that might be valuable at a later stage?
Labels and metadata
Sometimes all we have is documents, but often we want to look at documents in the context of some additional information, or metadata  This additional information could tell us about the creation of documents (date, author, forum), or about the reception of documents (flagged as hate speech, helpful review)  Information about text segments can be extremely valuable, but it is also prone to errors, inconsistencies, bias, and missing information  Examining metadata is a good way to check a collection's balance and representativeness  Are sources disproportionately of one form? Is the collection missing a specific time window? This type of curation can be extremely time consuming as it may require expert labeling, but it often leads to the most compelling results  Sometimes metadata are also used as target labels to develop machine learning models  But using them as a “ground truth” requires caution  Labels sometimes mean something different than we expect  For example, a down vote for a social media post could indicate that the content is offensive, or that the voter simply disagreed with the expressed view 
Conceptualization
A core step in many analyses is translating social and cultural concepts (such as hate speech, rumor, or conversion) into measurable quantities  Before we can develop measurements for these concepts (the operationalization step, or the “implementation” step as denoted by BIBREF12 ), we need to define them  In the conceptualization phase we often start with questions such as: who are the domain experts, and how have they approached the topic? We are looking for a definition of the concept that is flexible enough to apply on our dataset, yet formal enough for computational research  For example, our introductory study on hate speech BIBREF0 used a statement on hate speech produced by the European Union Court of Human Rights  The goal was not to implement this definition directly in software but to use it as a reference point to anchor subsequent analyses 
If we want to move beyond the use of ad hoc definitions, it can be useful to distinguish between what political scientists Adcock and Collier call the “background concept” and the “systematized concept” BIBREF13   The background concept comprises the full and diverse set of meanings that might be associated with a particular term  This involves delving into theoretical, conceptual, and empirical studies to assess how a concept has been defined by other scholars and, most importantly, to determine which definition is most appropriate for the particular research question and the theoretical framework in which it is situated  That definition, in turn, represents the systematized concept: the formulation that is adopted for the study 
It is important to consider that for social and cultural concepts there is no absolute ground truth  There are often multiple valid definitions for a concept (the “background” concept in the terms of Adcock and Collier), and definitions might be contested over time  This may be uncomfortable for computer scientists, whose primary measure of success is often based on comparing a model's output against “ground truth” or a “gold standard”, e g , by comparing a sentiment classifier's output against manual annotations  However, the notion of ground truth is uncommon in the humanities and the social sciences and it is often taken too far in machine learning  BIBREF14 notes that in literary criticism and the digital humanities more broadly “interpretation, ambiguity, and argumentation are prized far above ground truth and definitive conclusions"  BIBREF15 draw attention to the different attitudes of literary scholars and computational linguists towards ambiguity, stating that “In Computational Linguistics [  ] ambiguity is almost uniformly treated as a problem to be solved; the focus is on disambiguation, with the assumption that one true, correct interpretation exists " The latter is probably true for tasks such as spam filtering, but in the social sciences and the humanities many relevant concepts are fundamentally unobservable, such as latent traits of political actors BIBREF16 or cultural fit in organizations BIBREF17 , leading to validation challenges  Moreover, when the ground truth comes from people, it may be influenced by ideological priors, priming, simple differences of opinion or perspective, and many other factors BIBREF18   We return to this issue in our discussions on validation and analysis 
Operationalization
In this phase we develop measures (or, “operationalizations”, or “indicators”) for the concepts of interest, a process called “operationalization”  Regardless of whether we are working with computers, the output produced coincides with Adcock and Collier's “scores”—the concrete translation and output of the systematized concept into numbers or labels BIBREF13   Choices made during this phase are always tied to the question “Are we measuring what we intend to measure?” Does our operationalization match our conceptual definition? To ensure validity we must recognize gaps between what is important and what is easy to measure  We first discuss modeling considerations  Next, we describe several frequently used computational approaches and their limitations and strengths 
Modeling considerations
The variables (both predictors and outcomes) are rarely simply binary or categorical  For example, a study on language use and age could focus on chronological age (instead of, e g , social age BIBREF19 )  However, even then, age can be modeled in different ways  Discretization can make the modeling easier and various NLP studies have modeled age as a categorical variable BIBREF20   But any discretization raises questions: How many categories? Where to place the boundaries? Fine distinctions might not always be meaningful for the analysis we are interested in, but categories that are too broad can threaten validity  Other interesting variables include time, space, and even the social network position of the author  It is often preferable to keep the variable in its most precise form  For example, BIBREF21 perform exploration in the context of hypothesis testing by using latitude and longitude coordinates — the original metadata attached to geotagged social media such as tweets — rather than aggregating into administrative units such as counties or cities  This is necessary when such administrative units are unlikely to be related to the target concept, as is the case in their analysis of dialect differences  Focusing on precise geographical coordinates also makes it possible to recognize fine-grained effects, such as language variation across the geography of a city 
Using a particular classification scheme means deciding which variations are visible, and which ones are hidden BIBREF22   We are looking for a categorization scheme for which it is feasible to collect a large enough labeled document collection (e g , to train supervised models), but which is also fine-grained enough for our purposes  Classification schemes rarely exhibit the ideal properties, i e , that they are consistent, their categories are mutually exclusive, and that the system is complete BIBREF22   Borderline cases are challenging, especially with social and cultural concepts, where the boundaries are often not clear-cut  The choice of scheme can also have ethical implications BIBREF22   For example, gender is usually represented as a binary variable in NLP and computational models tend to learn gender-stereotypical patterns  The operationalization of gender in NLP has been challenged only recently BIBREF23 , BIBREF24 , BIBREF25  
Supervised and unsupervised learning are the most common approaches to learning from data  With supervised learning, a model learns from labeled data (e g , social media messages labeled by sentiment) to infer (or predict) these labels from unlabeled texts  In contrast, unsupervised learning uses unlabeled data  Supervised approaches are especially suitable when we have a clear definition of the concept of interest and when labels are available (either annotated or native to the data)  Unsupervised approaches, such as topic models, are especially useful for exploration  In this setting, conceptualization and operationalization may occur simultaneously, with theory emerging from the data BIBREF26   Unsupervised approaches are also used when there is a clear way of measuring a concept, often based on strong assumptions  For example, BIBREF3 measure “surprise” in an analysis of Darwin's reading decisions based on the divergence between two probability distributions 
From an analysis perspective, the unit of text that we are labeling (or annotating, or coding), either automatic or manual, can sometimes be different than one's final unit of analysis  For example, if in a study on media frames in news stories, the theoretical framework and research question point toward frames at the story level (e g , what is the overall causal analysis of the news article?), the story must be the unit of analysis  Yet it is often difficult to validly and reliably code a single frame at the story level  Multiple perspectives are likely to sit side-by-side in a story  Thus, an article on income inequality might point to multiple causes, such as globalization, education, and tax policies  Coding at the sentence level would detect each of these causal explanations individually, but this information would need to be somehow aggregated to determine the overall story-level frame  Sometimes scholars solve this problem by only examining headlines and lead paragraphs, arguing that based on journalistic convention, the most important information can be found at the beginning of a story  However, this leads to a return to a shorter, less nuanced analysis 
From a computational perspective, the unit of text can also make a huge difference, especially when we are using bag-of-words models, where word order within a unit does not matter  Small segments, like tweets, sometimes do not have enough information to make their semantic context clear  In contrast, larger segments, like novels, have too much variation, making it difficult to train focused models  Finding a good segmentation sometimes means combining short documents and subdividing long documents  The word “document" can therefore be misleading  But it is so ingrained in the common NLP lexicon that we use it anyway in this article 
For insight-driven text analysis, it is often critical that high-level patterns can be communicated  Furthermore, interpretable models make it easier to find spurious features, to do error analysis, and to support interpretation of results  Some approaches are effective for prediction, but harder to interpret  The value we place on interpretability can therefore influence the approach we choose  There is an increasing interest in developing interpretable or transparent models in the NLP and machine learning communities 
Annotation
Many studies involve human coders  Sometimes the goal is to fully code the data, but in a computational analysis we often use the labels (or annotations) to train machine learning models to automatically recognize them, and to identify language patterns that are associated with these labels  For example, for a project analyzing rumors online BIBREF27 , conversation threads were annotated along different dimensions, including rumor versus non-rumor and stance towards a rumor 
The collection of annotation choices make up an annotation scheme (or “codebook”)  Existing schemes and annotations can be useful as starting points
Context 3: Introduction
Wikipedia is the largest source of open and collaboratively curated knowledge in the world  Introduced in 2001, it has evolved into a reference work with around 5m pages for the English Wikipedia alone  In addition, entities and event pages are updated quickly via collaborative editing and all edits are encouraged to include source citations, creating a knowledge base which aims at being both timely as well as authoritative  As a result, it has become the preferred source of information consumption about entities and events  Moreso, this knowledge is harvested and utilized in building knowledge bases like YAGO BIBREF0 and DBpedia BIBREF1 , and used in applications like text categorization BIBREF2 , entity disambiguation BIBREF3 , entity ranking BIBREF4 and distant supervision BIBREF5 , BIBREF6  
However, not all Wikipedia pages referring to entities (entity pages) are comprehensive: relevant information can either be missing or added with a delay  Consider the city of New Orleans and the state of Odisha which were severely affected by cyclones Hurricane Katrina and Odisha Cyclone, respectively  While Katrina finds extensive mention in the entity page for New Orleans, Odisha Cyclone which has 5 times more human casualties (cf  Figure FIGREF2 ) is not mentioned in the page for Odisha  Arguably Katrina and New Orleans are more popular entities, but Odisha Cyclone was also reported extensively in national and international news outlets  This highlights the lack of important facts in trunk and long-tail entity pages, even in the presence of relevant sources  In addition, previous studies have shown that there is an inherent delay or lag when facts are added to entity pages BIBREF7  
To remedy these problems, it is important to identify information sources that contain novel and salient facts to a given entity page  However, not all information sources are equal  The online presence of major news outlets is an authoritative source due to active editorial control and their articles are also a timely container of facts  In addition, their use is in line with current Wikipedia editing practice, as is shown in BIBREF7 that almost 20% of current citations in all entity pages are news articles  We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative 
Existing efforts to populate Wikipedia BIBREF8 start from an entity page and then generate candidate documents about this entity using an external search engine (and then post-process them)  However, such an approach lacks in (a) reproducibility since rankings vary with time with obvious bias to recent news (b) maintainability since document acquisition for each entity has to be periodically performed  To this effect, our news suggestion considers a news article as input, and determines if it is valuable for Wikipedia  Specifically, given an input news article INLINEFORM0 and a state of Wikipedia, the news suggestion problem identifies the entities mentioned in INLINEFORM1 whose entity pages can improve upon suggesting INLINEFORM2   Most of the works on knowledge base acceleration BIBREF9 , BIBREF10 , BIBREF11 , or Wikipedia page generation BIBREF8 rely on high quality input sources which are then utilized to extract textual facts for Wikipedia page population  In this work, we do not suggest snippets or paraphrases but rather entire articles which have a high potential importance for entity pages  These suggested news articles could be consequently used for extraction, summarization or population either manually or automatically – all of which rely on high quality and relevant input sources 
We identify four properties of good news recommendations: salience, relative authority, novelty and placement  First, we need to identify the most salient entities in a news article  This is done to avoid pollution of entity pages with only marginally related news  Second, we need to determine whether the news is important to the entity as only the most relevant news should be added to a precise reference work  To do this, we compute the relative authority of all entities in the news article: we call an entity more authoritative than another if it is more popular or noteworthy in the real world  Entities with very high authority have many news items associated with them and only the most relevant of these should be included in Wikipedia whereas for entities of lower authority the threshold for inclusion of a news article will be lower  Third, a good recommendation should be able to identify novel news by minimizing redundancy coming from multiple news articles  Finally, addition of facts is facilitated if the recommendations are fine-grained, i e , recommendations are made on the section level rather than the page level (placement) 
Approach and Contributions  We propose a two-stage news suggestion approach to entity pages  In the first stage, we determine whether a news article should be suggested for an entity, based on the entity's salience in the news article, its relative authority and the novelty of the article to the entity page  The second stage takes into account the class of the entity for which the news is suggested and constructs section templates from entities of the same class  The generation of such templates has the advantage of suggesting and expanding entity pages that do not have a complete section structure in Wikipedia, explicitly addressing long-tail and trunk entities  Afterwards, based on the constructed template our method determines the best fit for the news article with one of the sections 
We evaluate the proposed approach on a news corpus consisting of 351,982 articles crawled from the news external references in Wikipedia from 73,734 entity pages  Given the Wikipedia snapshot at a given year (in our case [2009-2014]), we suggest news articles that might be cited in the coming years  The existing news references in the entity pages along with their reference date act as our ground-truth to evaluate our approach  In summary, we make the following contributions 
Related Work
As we suggest a new problem there is no current work addressing exactly the same task  However, our task has similarities to Wikipedia page generation and knowledge base acceleration  In addition, we take inspiration from Natural Language Processing (NLP) methods for salience detection 
Wikipedia Page Generation is the problem of populating Wikipedia pages with content coming from external sources  Sauper and Barzilay BIBREF8 propose an approach for automatically generating whole entity pages for specific entity classes  The approach is trained on already-populated entity pages of a given class (e g  `Diseases') by learning templates about the entity page structure (e g  diseases have a treatment section)  For a new entity page, first, they extract documents via Web search using the entity title and the section title as a query, for example `Lung Cancer'+`Treatment'  As already discussed in the introduction, this has problems with reproducibility and maintainability  However, their main focus is on identifying the best paragraphs extracted from the collected documents  They rank the paragraphs via an optimized supervised perceptron model for finding the most representative paragraph that is the least similar to paragraphs in other sections  This paragraph is then included in the newly generated entity page  Taneva and Weikum BIBREF12 propose an approach that constructs short summaries for the long tail  The summaries are called `gems' and the size of a `gem' can be user defined  They focus on generating summaries that are novel and diverse  However, they do not consider any structure of entities, which is present in Wikipedia 
In contrast to BIBREF8 and BIBREF12 , we actually focus on suggesting entire documents to Wikipedia entity pages  These are authoritative documents (news), which are highly relevant for the entity, novel for the entity and in which the entity is salient  Whereas relevance in Sauper and Barzilay is implicitly computed by web page ranking we solve that problem by looking at relative authority and salience of an entity, using the news article and entity page only  As Sauper and Barzilay concentrate on empty entity pages, the problem of novelty of their content is not an issue in their work whereas it is in our case which focuses more on updating entities  Updating entities will be more and more important the bigger an existing reference work is  Both the approaches in BIBREF8 and BIBREF12 (finding paragraphs and summarization) could then be used to process the documents we suggest further  Our concentration on news is also novel 
Knowledge Base Acceleration  In this task, given specific information extraction templates, a given corpus is analyzed in order to find worthwhile mentions of an entity or snippets that match the templates  Balog BIBREF9 , BIBREF10 recommend news citations for an entity  Prior to that, the news articles are classified for their appropriateness for an entity, where as features for the classification task they use entity, document, entity-document and temporal features  The best performing features are those that measure similarity between an entity and the news document  West et al  BIBREF13 consider the problem of knowledge base completion, through question answering and complete missing facts in Freebase based on templates, i e  Frank_Zappa bornIn Baltymore, Maryland 
In contrast, we do not extract facts for pre-defined templates but rather suggest news articles based on their relevance to an entity  In cases of long-tail entities, we can suggest to add a novel section through our abstraction and generation of section templates at entity class level 
Entity Salience  Determining which entities are prominent or salient in a given text has a long history in NLP, sparked by the linguistic theory of Centering BIBREF14   Salience has been used in pronoun and co-reference resolution BIBREF15 , or to predict which entities will be included in an abstract of an article BIBREF11   Frequent features to measure salience include the frequency of an entity in a document, positioning of an entity, grammatical function or internal entity structure (POS tags, head nouns etc )  These approaches are not currently aimed at knowledge base generation or Wikipedia coverage extension but we postulate that an entity's salience in a news article is a prerequisite to the news article being relevant enough to be included in an entity page  We therefore use the salience features in BIBREF11 as part of our model  However, these features are document-internal — we will show that they are not sufficient to predict news inclusion into an entity page and add features of entity authority, news authority and novelty that measure the relations between several entities, between entity and news article as well as between several competing news articles 
Terminology and Problem Definition
We are interested in named entities mentioned in documents  An entity INLINEFORM0 can be identified by a canonical name, and can be mentioned differently in text via different surface forms  We canonicalize these mentions to entity pages in Wikipedia, a method typically known as entity linking  We denote the set of canonicalized entities extracted and linked from a news article INLINEFORM1 as INLINEFORM2   For example, in Figure FIGREF7 , entities are canonicalized into Wikipedia entity pages (e g  Odisha is canonicalized to the corresponding article)  For a collection of news articles INLINEFORM3 , we further denote the resulting set of entities by INLINEFORM4  
Information in an entity page is organized into sections and evolves with time as more content is added  We refer to the state of Wikipedia at a time INLINEFORM0 as INLINEFORM1 and the set of sections for an entity page INLINEFORM2 as its entity profile INLINEFORM3   Unlike news articles, text in Wikipedia could be explicitly linked to entity pages through anchors  The set of entities explicitly referred in text from section INLINEFORM4 is defined as INLINEFORM5   Furthermore, Wikipedia induces a category structure over its entities, which is exploited by knowledge bases like YAGO (e g  Barack_Obama isA Person)  Consequently, each entity page belongs to one or more entity categories or classes INLINEFORM6   Now we can define our news suggestion problem below:
Definition 1 (News Suggestion Problem) Given a set of news articles INLINEFORM0 and set of Wikipedia entity pages INLINEFORM1 (from INLINEFORM2 ) we intend to suggest a news article INLINEFORM3 published at time INLINEFORM4 to entity page INLINEFORM5 and additionally to the most relevant section for the entity page INLINEFORM6  
Approach Overview
We approach the news suggestion problem by decomposing it into two tasks:
AEP: Article–Entity placement
ASP: Article–Section placement
In this first step, for a given entity-news pair INLINEFORM0 , we determine whether the given news article INLINEFORM1 should be suggested (we will refer to this as `relevant') to entity INLINEFORM2   To generate such INLINEFORM3 pairs, we perform the entity linking process, INLINEFORM4 , for INLINEFORM5  
The article–entity placement task (described in detail in Section SECREF16 ) for a pair INLINEFORM0 outputs a binary label (either `non-relevant' or `relevant') and is formalized in Equation EQREF14   DISPLAYFORM0
In the second step, we take into account all `relevant' pairs INLINEFORM0 and find the correct section for article INLINEFORM1 in entity INLINEFORM2 , respectively its profile INLINEFORM3 (see Section SECREF30 )  The article–section placement task, determines the correct section for the triple INLINEFORM4 , and is formalized in Equation EQREF15   DISPLAYFORM0
In the subsequent sections we describe in details how we approach the two tasks for suggesting news articles to entity pages 
News Article Suggestion
In this section, we provide an overview of the news suggestion approach to Wikipedia entity pages (see Figure FIGREF7 )  The approach is split into two tasks: (i) article-entity (AEP) and (ii) article-section (ASP) placement  For a Wikipedia snapshot INLINEFORM0 and a news corpus INLINEFORM1 , we first determine which news articles should be suggested to an entity INLINEFORM2   We will denote our approach for AEP by INLINEFORM3   Finally, we determine the most appropriate section for the ASP task and we denote our approach with INLINEFORM4  
In the following, we describe the process of learning the functions INLINEFORM0 and INLINEFORM1   We introduce features for the learning process, which encode information regarding the entity salience, relative authority and novelty in the case of AEP task  For the ASP task, we measure the overall fit of an article to the entity sections, with the entity being an input from AEP task  Additionally, considering that the entity profiles INLINEFORM2 are incomplete, in the case of a missing section we suggest and expand the entity profiles based on section templates generated from entities of the same class INLINEFORM3 (see Section UID34 ) 
Article–Entity Placement
In this step we learn the function INLINEFORM0 to correctly determine whether INLINEFORM1 should be suggested for INLINEFORM2 , basically a binary classification model (0=`non-relevant' and 1=`relevant')  Note that we are mainly interested in finding the relevant pairs in this task  For every news article, the number of disambiguated entities is around 30 (but INLINEFORM3 is suggested for only two of them on average)  Therefore, the distribution of `non-relevant' and `relevant' pairs is skewed towards the earlier, and by simply choosing the `non-relevant' label we can achieve a high accuracy for INLINEFORM4   Finding the relevant pairs is therefore a considerable challenge 
An article INLINEFORM0 is suggested to INLINEFORM1 by our function INLINEFORM2 if it fulfills the following properties  The entity INLINEFORM3 is salient in INLINEFORM4 (a central concept), therefore ensuring that INLINEFORM5 is about INLINEFORM6 and that INLINEFORM7 is important for INLINEFORM8   Next, given the fact there might be many articles in which INLINEFORM9 is salient, we also look at the reverse property, namely whether INLINEFORM10 is important for INLINEFORM11   We do this by comparing the authority of INLINEFORM12 (which is a measure of popularity of an entity, such as its frequency of mention in a whole corpus) with the authority of its co-occurring entities in INLINEFORM13 , leading to a feature we call relative authority  The intuition is that for an entity that has overall lower authority than its co-occurring entities, a news article is more easily of importance  Finally, if the article we are about to suggest is already covered in the entity profile INLINEFORM14 , we do not wish to suggest redundant information, hence the novelty  Therefore, the learning objective of INLINEFORM15 should fulfill the following properties  Table TABREF21 shows a summary of the computed features for INLINEFORM16  
Salience: entity INLINEFORM0 should be a salient entity in news article INLINEFORM1
Relative Authority: the set of entities INLINEFORM0 with which INLINEFORM1 co-occurs should have higher authority than INLINEFORM2 , making INLINEFORM3 important for INLINEFORM4
Novelty: news article INLINEFORM0 should provide novel information for entity INLINEFORM1 taking into account its profile INLINEFORM2
Baseline Features  As discussed in Section SECREF2 , a variety of features that measure salience of an entity in text are available from the NLP community  We reimplemented the ones in Dunietz and Gillick BIBREF11   This includes a variety of features, e g  positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in  Table 2 in BIBREF11 gives details 
Relative Entity Frequency  Although frequency of mention and positional features play some role in baseline features, their interaction is not modeled by a single feature nor do the positional features encode more than sentence position  We therefore suggest a novel feature called relative entity frequency, INLINEFORM0 , that has three properties : (i) It rewards entities for occurring throughout the text instead of only in some parts of the text, measured by the number of paragraphs it occurs in (ii) it rewards entities that occur more frequently in the opening paragraphs of an article as we model INLINEFORM1 as an exponential decay function  The decay corresponds to the positional index of the news paragraph  This is inspired by the news-specific discourse structure that tends to give short summaries of the most important facts and entities in the opening paragraphs  (iii) it compares entity frequency to the frequency of its co-occurring mentions as the weight of an entity appearing in a specific paragraph, normalized by the sum of the frequencies of other entities in INLINEFORM2   DISPLAYFORM0
where, INLINEFORM0 represents a news paragraph from INLINEFORM1 , and with INLINEFORM2 we indicate the set of all paragraphs in INLINEFORM3   The frequency of INLINEFORM4 in a paragraph INLINEFORM5 is denoted by INLINEFORM6   With INLINEFORM7 and INLINEFORM8 we indicate the number of paragraphs in which entity INLINEFORM9 occurs, and the total number of paragraphs, respectively 
Relative Authority  In this case, we consider the comparative relevance of the news article to the different entities occurring in it  As an example, let us consider the meeting of the Sudanese bishop Elias Taban with Hillary Clinton  Both entities are salient for the meeting  However, in Taban's Wikipedia page, this meeting is discussed prominently with a corresponding news reference, whereas in Hillary Clinton's Wikipedia page it is not reported at all  We believe this is not just an omission in Clinton's page but mirrors the fact that for the lesser known Taban the meeting is big news whereas for the more famous Clinton these kind of meetings are a regular occurrence, not all of which can be reported in what is supposed to be a selection of the most important events for her  Therefore, if two entities co-occur, the news is more relevant for the entity with the lower a priori authority 
The a priori authority of an entity (denoted by INLINEFORM0 ) can be measured in several ways  We opt for two approaches: (i) probability of entity INLINEFORM1 occurring in the corpus INLINEFORM2 , and (ii) authority assessed through centrality measures like PageRank BIBREF16   For the second case we construct the graph INLINEFORM3 consisting of entities in INLINEFORM4 and news articles in INLINEFORM5 as vertices  The edges are established between INLINEFORM6 and entities in INLINEFORM7 , that is INLINEFORM8 , and the out-links from INLINEFORM9 , that is INLINEFORM10 (arrows present the edge direction) 
Starting from a priori authority, we proceed to relative authority by comparing the a priori authority of co-occurring entities in INLINEFORM0   We define the relative authority of INLINEFORM1 as the proportion of co-occurring entities INLINEFORM2 that have a higher a priori authority than INLINEFORM3 (see Equation EQREF28   DISPLAYFORM0
As we might run the danger of not suggesting any news articles for entities with very high a priori authority (such as Clinton) due to the strict inequality constraint, we can relax the constraint such that the authority of co-occurring entities is above a certain threshold 
News Domain Authority  The news domain authority addresses two main aspects  Firstly, if bundled together with the relative authority feature, we can ensure that dependent on the entity authority, we suggest news from authoritative sources, hence ensuring the quality of suggested articles  The second aspect is in a news streaming scenario where multiple news domains report the same event — ideally only articles coming from authoritative sources would fulfill the conditions for the news suggestion task 
The news domain authority is computed based on the number of news references in Wikipedia coming from a particular news domain INLINEFORM0   This represents a simple prior that a news article INLINEFORM1 is from domain INLINEFORM2 in corpus INLINEFORM3   We extract the domains by taking the base URLs from the news article URLs 
An important feature when suggesting an article INLINEFORM0 to an entity INLINEFORM1 is the novelty of INLINEFORM2 w r t the already existing entity profile INLINEFORM3   Studies BIBREF17 have shown that on comparable collections to ours (TREC GOV2) the number of duplicates can go up to INLINEFORM4   This figure is likely higher for major events concerning highly authoritative entities on which all news media will report 
Given an entity INLINEFORM0 and the already added news references INLINEFORM1 up to year INLINEFORM2 , the novelty of INLINEFORM3 at year INLINEFORM4 is measured by the KL divergence between the language model of INLINEFORM5 and articles in INLINEFORM6   We combine this measure with the entity overlap of INLINEFORM7 and INLINEFORM8   The novelty value of INLINEFORM9 is given by the minimal divergence value  Low scores indicate low novelty for the entity profile INLINEFORM10  
N(n|e) = n'Nt-1{DKL((n') || (n)) + DKL((N) || (n)) 
DKL((n') || (n))  (1-) jaccard((n'),(n))} where INLINEFORM0 is the KL divergence of the language models ( INLINEFORM1 and INLINEFORM2 ), whereas INLINEFORM3 is the mixing weight ( INLINEFORM4 ) between the language models INLINEFORM5 and the entity overlap in INLINEFORM6 and INLINEFORM7  
Here we introduce the evaluation setup and analyze the results for the article–entity (AEP) placement task  We only report the evaluation metrics for the `relevant' news-entity pairs  A detailed explanation on why we focus on the `relevant' pairs is provided in Section SECREF16  
Baselines  We consider the following baselines for this task 
B1  The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11  
B2  The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2  
Learning Models  We use Random Forests (RF) BIBREF23   We learn the RF on all computed features in Table TABREF21   The optimization on RF is done by splitting the feature space into multiple trees that are considered as ensemble classifiers  Consequently, for each classifier it computes the margin function as a measure of the average count of predicting the correct class in contrast to any other class  The higher the margin score the more robust the model 
Metrics  We compute precision P, recall R and F1 score for the relevant class  For example, precision is the number of news-entity pairs we correctly labeled as relevant compared to our ground truth divided by the number of all news-entity pairs we labeled as relevant 
The following results measure the effectiveness of our approach in three main aspects: (i) overall performance of INLINEFORM0 and comparison to baselines, (ii) robustness across the years, and (iii) optimal model for the AEP placement task 
Performance  Figure FIGREF55 shows the results for the years 2009 and 2013, where we optimized the learning objective with instances from year INLINEFORM0 and evaluate on the years INLINEFORM1 (see Section SECREF46 )  The results show the precision–recall curve  The red curve shows baseline B1 BIBREF11 , and the blue one shows the performance of INLINEFORM2   The curve shows for varying confidence scores (high to low) the precision on labeling the pair INLINEFORM3 as `relevant'  In addition, at each confidence score we can compute the corresponding recall for the `relevant' label  For high confidence scores on labeling the news-entity pairs, the baseline B1 achieves on average a precision score of P=0 50, while INLINEFORM4 has P=0 93  We note that with the drop in the confidence score the corresponding precision and recall values drop too, and the overall F1 score for B1 is around F1=0 2, in contrast we achieve an average score of F1=0 67 
It is evident from Figure FIGREF55 that for the years 2009 and 2013, INLINEFORM0 significantly outperforms the baseline B1  We measure the significance through the t-test statistic and get a p-value of INLINEFORM1   The improvement we achieve over B1 in absolute numbers, INLINEFORM2 P=+0 5 in terms of precision for the years between 2009 and 2014, and a similar improvement in terms of F1 score  The improvement for recall is INLINEFORM3 R=+0 4  The relative improvement over B1 for P and F1 is almost 1 8 times better, while for recall we are 3 5 times better  In Table TABREF58 we show the overall scores for the evaluation metrics for B1 and INLINEFORM4   Finally, for B2 we achieve much poorer performance, with average scores of P=0 21, R=0 20 and F1=0 21 
Robustness  In Table TABREF58 , we show the overall performance for the years between 2009 and 2013  An interesting observation we make is that we have a very robust performance and the results are stable across the years  If we consider the experimental setup, where for year INLINEFORM0 we optimize the learning objective with only 74k training instances and evaluate on the rest of the instances, it achieves a very good performance  We predict with F1=0 68 the remaining 469k instances for the years INLINEFORM1  
The results are particularly promising considering the fact that the distribution between our two classes is highly skewed  On average the number of `relevant' pairs account for only around INLINEFORM0 of all pairs  A good indicator to support such a statement is the kappa (denoted by INLINEFORM1 ) statistic  INLINEFORM2 measures agreement between the algorithm and the gold standard on both labels while correcting for chance agreement (often expected due to extreme distributions)  The INLINEFORM3 scores for B1 across the years is on average INLINEFORM4 , while for INLINEFORM5 we achieve a score of INLINEFORM6 (the maximum score for INLINEFORM7 is 1) 
In Figure FIGREF60 we show the impact of the individual feature groups that contribute to the superior performance in comparison to the baselines  Relative entity frequency from the salience feature, models the entity salience as an exponentially decaying function based on the positional index of the paragraph where the entity appears  The performance of INLINEFORM0 with relative entity frequency from the salience feature group is close to that of all the features combined  The authority and novelty features account to a further improvement in terms of precision, by adding roughly a 7%-10% increase  However, if both feature groups are considered separately, they significantly outperform the baseline B1 
Article–Section Placement
We model the ASP placement task as a successor of the AEP task  For all the `relevant' news entity pairs, the task is to determine the correct entity section  Each section in a Wikipedia entity page represents a different topic  For example, Barack Obama has the sections `Early Life', `Presidency', `Family and Personal Life' etc  However, many entity pages have an incomplete section structure  Incomplete or missing sections are due to two Wikipedia properties  First, long-tail entities miss information and sections due to their lack of popularity  Second, for all entities whether popular or not, certain sections might occur for the first time due to real world developments  As an example, the entity Germanwings did not have an `Accidents' section before this year's disaster, which was the first in the history of the airline 
Even if sections are missing for certain entities, similar sections usually occur in other entities of the same class (e g  other airlines had disasters and therefore their pages have an accidents section)  We exploit such homogeneity of section structure and construct templates that we use to expand entity profiles  The learning objective for INLINEFORM0 takes into account the following properties:
Section-templates: account for incomplete section structure for an entity profile INLINEFORM0 by constructing section templates INLINEFORM1 from an entity class INLINEFORM2
Overall fit: measures the overall fit of a news article to sections in the section templates INLINEFORM0
Given the fact that entity profiles are often incomplete, we construct section templates for every entity class  We group entities based on their class INLINEFORM0 and construct section templates INLINEFORM1   For different entity classes, e g  Person and Location, the section structure and the information represented in those section varies heavily  Therefore, the section templates are with respect to the individual classes in our experimental setup (see Figure FIGREF42 )  DISPLAYFORM0
Generating section templates has two main advantages  Firstly, by considering class-based profiles, we can overcome the problem of incomplete individual entity profiles and thereby are able to suggest news articles to sections that do not yet exist in a specific entity INLINEFORM0   The second advantage is that we are able to canonicalize the sections, i e  `Early Life' and `Early Life and Childhood' would be treated similarly 
To generate the section template INLINEFORM0 , we extract all sections from entities of a given type INLINEFORM1 at year INLINEFORM2   Next, we cluster the entity sections, based on an extended version of k–means clustering BIBREF18 , namely x–means clustering introduced in Pelleg et al  which estimates the number of clusters efficiently BIBREF19   As a similarity metric we use the cosine similarity computed based on the tf–idf models of the sections  Using the x–means algorithm we overcome the requirement to provide the number of clusters k beforehand  x–means extends the k–means algorithm, such that a user only specifies a range [ INLINEFORM3 , INLINEFORM4 ] that the number of clusters may reasonably lie in 
The learning objective of INLINEFORM0 is to determine the overall fit of a news article INLINEFORM1 to one of the sections in a given section template INLINEFORM2   The template is pre-determined by the class of the entity for which the news is suggested as relevant by INLINEFORM3   In all cases, we measure how well INLINEFORM4 fits each of the sections INLINEFORM5 as well as the specific entity section INLINEFORM6   The section profiles in INLINEFORM7 represent the aggregated entity profiles from all entities of class INLINEFORM8 at year INLINEFORM9  
To learn INLINEFORM0 we rely on a variety of features that consider several similarity aspects as shown in Table TABREF31   For the sake of simplicity we do not make the distinction in Table TABREF31 between the individual entity section and class-based section similarities, INLINEFORM1 and INLINEFORM2 , respectively  Bear in mind that an entity section INLINEFORM3 might be present at year INLINEFORM4 but not at year INLINEFORM5 (see for more details the discussion on entity profile expansion in Section UID69 ) 
Topic  We use topic similarities to ensure (i) that the content of INLINEFORM0 fits topic-wise with a specific section text and (ii) that it has a similar topic to previously referred news articles in that section  In a pre-processing stage we compute the topic models for the news articles, entity sections INLINEFORM1 and the aggregated class-based sections in INLINEFORM2   The topic models are computed using LDA BIBREF20   We only computed a single topic per article/section as we are only interested in topic term overlaps between article and sections  We distinguish two main features: the first feature measures the overlap of topic terms between INLINEFORM3 and the entity section INLINEFORM4 and INLINEFORM5 , and the second feature measures the overlap of the topic model of INLINEFORM6 against referred news articles in INLINEFORM7 at time INLINEFORM8  
Syntactic  These features represent a mechanism for conveying the importance of a specific text snippet, solely based on the frequency of specific POS tags (i e  NNP, CD etc ), as commonly used in text summarization tasks  Following the same intuition as in BIBREF8 , we weigh the importance of articles by the count of specific POS tags  We expect that for different sections, the importance of POS tags will vary  We measure the similarity of POS tags in a news article against the section text  Additionally, we consider bi-gram and tri-gram POS tag overlap  This exploits similarity in syntactical patterns between the news and section text 
Lexical  As lexical features, we measure the similarity of INLINEFORM0 against the entity section text INLINEFORM1 and the aggregate section text INLINEFORM2   Further, we distinguish between the overall similarity of INLINEFORM3 and that of the different news paragraphs ( INLINEFORM4 which denotes the paragraphs of INLINEFORM5 up to the 5th paragraph)  A higher similarity on the first paragraphs represents a more confident indicator that INLINEFORM6 should be suggested to a specific section INLINEFORM7   We measure the similarity based on two metrics: (i) the KL-divergence between the computed language models and (ii) cosine similarity of the corresponding paragraph text INLINEFORM8 and section text 
Entity-based  Another feature set we consider is the overlap of named entities and their corresponding entity classes  For different entity sections, we expect to find a particular set of entity classes that will correlate with the section, e g  `Early Life' contains mostly entities related to family, school, universities etc 
Frequency  Finally, we gather statistics about the number of entities, paragraphs, news article length, top– INLINEFORM0 entities and entity classes, and the frequency of different POS tags  Here we try to capture patterns of articles that are usually cited in specific sections 
Evaluation Plan
In this section we outline the evaluation plan to verify the effectiveness of our learning approaches  To evaluate the news suggestion problem we are faced with two challenges 
What comprises the ground truth for such a task ?
How do we construct training and test splits given that entity pages consists of text added at different points in time ?
Consider the ground truth challenge  Evaluating if an arbitrary news article should be included in Wikipedia is both subjective and difficult for a human if she is not an expert  An invasive approach, which was proposed by Barzilay and Sauper BIBREF8 , adds content directly to Wikipedia and expects the editors or other users to redact irrelevant content over a period of time  The limitations of such an evaluation technique is that content added to long-tail entities might not be evaluated by informed users or editors in the experiment time frame  It is hard to estimate how much time the added content should be left on the entity page  A more non-invasive approach could involve crowdsourcing of entity and news article pairs in an IR style relevance assessment setup  The problem of such an approach is again finding knowledgeable users or experts for long-tail entities  Thus the notion of relevance of a news recommendation is challenging to evaluate in a crowd setup 
We take a slightly different approach by making an assumption that the news articles already present in Wikipedia entity pages are relevant  To this extent, we extract a dataset comprising of all news articles referenced in entity pages (details in Section SECREF40 )  At the expense of not evaluating the space comprising of news articles absent in Wikipedia, we succeed in (i) avoiding restrictive assumptions about the quality of human judgments, (ii) being invasive and polluting Wikipedia, and (iii) deriving a reusable test bed for quicker experimentation 
The second challenge of construction of training and test set separation is slightly easier and is addressed in Section SECREF46  
Datasets
The datasets we use for our experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history  The generated data represents one of the contributions of our paper  The datasets are the following:
Entity Classes  We focus on a manually predetermined set of entity classes for which we expect to have news coverage  The number of analyzed entity classes is 27, including INLINEFORM0 entities with at least one news reference  The entity classes were selected from the DBpedia class ontology  Figure FIGREF42 shows the number of entities per class for the years (2009-2014) 
News Articles  We extract all news references from the collected Wikipedia entity pages  The extracted news references are associated with the sections in which they appear  In total there were INLINEFORM0 news references, and after crawling we end up with INLINEFORM1 successfully crawled news articles  The details of the news article distribution, and the number of entities and sections from which they are referred are shown in Table TABREF44  
Article-Entity Ground-truth  The dataset comprises of the news and entity pairs INLINEFORM0   News-entity pairs are relevant if the news article is referenced in the entity page  Non-relevant pairs (i e  negative training examples) consist of news articles that contain an entity but are not referenced in that entity's page  If a news article INLINEFORM1 is referred from INLINEFORM2 at year INLINEFORM3 , the features are computed taking into account the entity profiles at year INLINEFORM4  
Article-Section Ground-truth  The dataset consists of the triple INLINEFORM0 , where INLINEFORM1 , where we assume that INLINEFORM2 has already been determined as relevant  We therefore have a multi-class classification problem where we need to determine the section of INLINEFORM3 where INLINEFORM4 is cited  Similar to the article-entity ground truth, here too the features compute the similarity between INLINEFORM5 , INLINEFORM6 and INLINEFORM7  
Data Pre-Processing
We POS-tag the news articles and entity profiles INLINEFORM0 with the Stanford tagger BIBREF21   For entity linking the news articles, we use TagMe! BIBREF22 with a confidence score of 0 3  On a manual inspection of a random sample of 1000 disambiguated entities, the accuracy is above 0 9  On average, the number of entities per news article is approximately 30  For entity linking the entity profiles, we simply follow the anchor text that refers to Wikipedia entities 
Train and Testing Evaluation Setup
We evaluate the generated supervised models for the two tasks, AEP and ASP, by splitting the train and testing instances  It is important to note that for the pairs INLINEFORM0 and the triple INLINEFORM1 , the news article INLINEFORM2 is referenced at time INLINEFORM3 by entity INLINEFORM4 , while the features take into account the entity profile at time INLINEFORM5   This avoids any `overlapping' content between the news article and the entity page, which could affect the learning task of the functions INLINEFORM6 and INLINEFORM7   Table TABREF47 shows the statistics of train and test instances  We learn the functions at year INLINEFORM8 and test on instances for the years greater than INLINEFORM9   Please note that we do not show the performance for year 2014 as we do not have data for 2015 for evaluation 
Article-Section Placement
Here we show the evaluation setup for ASP task and discuss the results with a focus on three main aspects, (i) the overall performance across the years, (ii) the entity class specific performance, and (iii) the impact on entity profile expansion by suggesting missing sections to entities based on the pre-computed templates 
Baselines  To the best of our knowledge, we are not aware of any comparable approach for this task  Therefore, the baselines we consider are the following:
S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2
S2: Place the news into the most frequent section in INLINEFORM0
Learning Models  We use Random Forests (RF) BIBREF23 and Support Vector Machines (SVM) BIBREF24   The models are optimized taking into account the features in Table TABREF31   In contrast to the AEP task, here the scale of the number of instances allows us to learn the SVM models  The SVM model is optimized using the INLINEFORM0 loss function and uses the Gaussian kernels 
Metrics  We compute precision P as the ratio of news for which we pick a section INLINEFORM0 from INLINEFORM1 and INLINEFORM2 conforms to the one in our ground-truth (see Section SECREF40 )  The definition of recall R and F1 score follows from that of precision 
Figure FIGREF66 shows the overall performance and a comparison of our approach (when INLINEFORM0 is optimized using SVM) against the best performing baseline S2  With the increase in the number of training instances for the ASP task the performance is a monotonically non-decreasing function  For the year 2009, we optimize the learning objective of INLINEFORM1 with around 8% of the total instances, and evaluate on the rest  The performance on average is around P=0 66 across all classes  Even though for many classes the performance is already stable (as we will see in the next section), for some classes we improve further  If we take into account the years between 2010 and 2012, we have an increase of INLINEFORM2 P=0 17, with around 70% of instances used for training and the remainder for evaluation  For the remaining years the total improvement is INLINEFORM3 P=0 18 in contrast to the performance at year 2009 
On the other hand, the baseline S1 has an average precision of P=0 12  The performance across the years varies slightly, with the year 2011 having the highest average precision of P=0 13  Always picking the most frequent section as in S2, as shown in Figure FIGREF66 , results in an average precision of P=0 17, with a uniform distribution across the years 
Here we show the performance of INLINEFORM0 decomposed for the different entity classes  Specifically we analyze the 27 classes in Figure FIGREF42   In Table TABREF68 , we show the results for a range of years (we omit showing all years due to space constraints)  For illustration purposes only, we group them into four main classes ( INLINEFORM1 Person, Organization, Location, Event INLINEFORM2 ) and into the specific sub-classes shown in the second column in Table TABREF68   For instance, the entity classes OfficeHolder and Politician are aggregated into Person–Politics 
It is evident that in the first year the performance is lower in contrast to the later years  This is due to the fact that as we proceed, we can better generalize and accurately determine the correct fit of an article INLINEFORM0 into one of the sections from the pre-computed templates INLINEFORM1   The results are already stable for the year range INLINEFORM2   For a few Person sub-classes, e g  Politics, Entertainment, we achieve an F1 score above 0 9  These additionally represent classes with a sufficient number of training instances for the years INLINEFORM3   The lowest F1 score is for the Criminal and Television classes  However, this is directly correlated with the insufficient number of instances 
The baseline approaches for the ASP task perform poorly  S1, based on lexical similarity, has a varying performance for different entity classes  The best performance is achieved for the class Person – Politics, with P=0 43  This highlights the importance of our feature choice and that the ASP cannot be considered as a linear function, where the maximum similarity yields the best results  For different entity classes different features and combination of features is necessary  Considering that S2 is the overall best performing baseline, through our approach INLINEFORM0 we have a significant improvement of over INLINEFORM1 P=+0 64 
The models we learn are very robust and obtain high accuracy, fulfilling our pre-condition for accurate news suggestions into the entity sections  We measure the robustness of INLINEFORM0 through the INLINEFORM1 statistic  In this case, we have a model with roughly 10 labels (corresponding to the number of sections in a template INLINEFORM2 )  The score we achieve shows that our model predicts with high confidence with INLINEFORM3  
The last analysis is the impact we have on expanding entity profiles INLINEFORM0 with new sections  Figure FIGREF70 shows the ratio of sections for which we correctly suggest an article INLINEFORM1 to the right section in the section template INLINEFORM2   The ratio here corresponds to sections that are not present in the entity profile at year INLINEFORM3 , that is INLINEFORM4   However, given the generated templates INLINEFORM5 , we can expand the entity profile INLINEFORM6 with a new section at time INLINEFORM7   In details, in the absence of a section at time INLINEFORM8 , our model trains well on similar sections from the section template INLINEFORM9 , hence we can predict accurately the section and in this case suggest its addition to the entity profile  With time, it is obvious that the expansion rate decreases at later years as the entity profiles become more `complete' 
This is particularly interesting for expanding the entity profiles of long-tail entities as well as updating entities with real-world emerging events that are added constantly  In many cases such missing sections are present at one of the entities of the respective entity class INLINEFORM0   An obvious case is the example taken in Section SECREF16 , where the `Accidents' is rather common for entities of type Airline  However, it is non-existent for some specific entity instances, i e Germanwings airline 
Through our ASP approach INLINEFORM0 , we are able to expand both long-tail and trunk entities  We distinguish between the two types of entities by simply measuring their section text length  The real distribution in the ground truth (see Section SECREF40 ) is 27% and 73% are long-tail and trunk entities, respectively  We are able to expand the entity profiles for both cases and all entity classes without a significant difference, with the only exception being the class Creative Work, where we expand significantly more trunk entities 
Conclusion and Future Work
In this work, we have proposed an automated approach for the novel task of suggesting news articles to Wikipedia entity pages to facilitate Wikipedia updating  The process consists of two stages  In the first stage, article–entity placement, we suggest news articles to entity pages by considering three main factors, such as entity salience in a news article, relative authority and novelty of news articles for an entity page  In the second stage, article–section placement, we determine the best fitting section in an entity page  Here, we remedy the problem of incomplete entity section profiles by constructing section templates for specific entity classes  This allows us to add missing sections to entity pages  We carry out an extensive experimental evaluation on 351,983 news articles and 73,734 entities coming from 27 distinct entity classes  For the first stage, we achieve an overall performance with P=0 93, R=0 514 and F1=0 676, outperforming our baseline competitors significantly  For the second stage, we show that we can learn incrementally to determine the correct section for a news article based on section templates  The overall performance across different classes is P=0 844, R=0 885 and F1=0 860 
In the future, we will enhance our work by extracting facts from the suggested news articles  Results suggest that the news content cited in entity pages comes from the first paragraphs  However, challenging task such as the canonicalization and chronological ordering of facts, still remain 

ground_truth: Yes

Yes

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 background do they have?
Context 2:  Usually settling on an annotation scheme requires several iterations, in which the guidelines are updated and annotation examples are added  For example, a political scientist could use a mixed deductive-inductive strategy for developing a codebook  She starts by laying out a set of theory-driven deductive coding rules, which means that the broad principles of the coding rules are laid out without examining examples first  These are then tested (and possibly adjusted) based on a sample of the data  In line with Adcock and Collier's notion of “content validity” BIBREF13 , the goal is to assess whether the codebook adequately captures the systematized concept  By looking at the data themselves, she gains a better sense of whether some things have been left out of the coding rules and whether anything is superfluous, misleading, or confusing  Adjustments are made and the process is repeated, often with another researcher involved 
The final annotations can be collected using a crowdsourcing platform, a smaller number of highly-trained annotators, or a group of experts  Which type of annotator to use should be informed by the complexity and specificity of the concept  For more complex concepts, highly-trained or expert annotators tend to produce more reliable results  However, complex concepts can sometimes be broken down into micro-tasks that can be performed independently in parallel by crowdsourced annotators  Concepts from highly specialized domains may require expert annotators  In all cases, however, some training will be required, and the training phase should involve continual checks of inter-annotator agreement (i e  intercoder reliability) or checks against a gold standard (e g  quizzes in crowdsourcing platforms) 
We also need to decide how inter-annotator agreement will be measured and what an acceptable level of agreement would be  Krippendorff's alpha is frequently used in the social sciences, but the right measure depends on the type of data and task  For manual coding, we can continually check inter-annotator agreement and begin introducing checks of intra-annotator agreement, too  For most communication scholars using only manual content analysis, an acceptable rate of agreement is achieved when Krippendorf's alpha reaches 0 80 or above  When human-coded data are used to validate machine learning algorithms, the reliability of the human-coded data is even more important  Disagreement between annotators can signal weaknesses of the annotation scheme, or highlight the inherent ambiguity in what we are trying to measure  Disagreement itself can be meaningful and can be integrated in subsequent analyses BIBREF28 , BIBREF29  
Data pre-processing
Preparing the data can be a complex and time-consuming process, often involving working with partially or wholly unstructured data  The pre-processing steps have a big impact on the operationalizations, subsequent analyses and reproducibility efforts BIBREF30 , and they are usually tightly linked to what we intend to measure  Unfortunately, these steps tend to be underreported, but documenting the pre-processing choices made is essential and is analogous to recording the decisions taken during the production of a scholarly edition or protocols in biomedical research  Data may also vary enormously in quality, depending on how it has been generated  Many historians, for example, work with text produced from an analogue original using Optical Character Recognition (OCR)  Often, there will be limited information available regarding the accuracy of the OCR, and the degree of accuracy may even vary within a single corpus (e g  where digitized text has been produced over a period of years, and the software has gradually improved)  The first step, then, is to try to correct for common OCR errors  These will vary depending on the type of text, the date at which the `original' was produced, and the nature of the font and typesetting 
One step that almost everyone takes is to tokenize the original character sequence into the words and word-like units  Tokenization is a more subtle and more powerful process than people expect  It is often done using regular expressions or scripts that have been circulating within the NLP community  Tokenization heuristics, however, can be badly confused by emoticons, creative orthography (e g , U$A, sh!t), and missing whitespace  Multi-word terms are also challenging  Treating them as a single unit can dramatically alter the patterns in text  Many words that are individually ambiguous have clear, unmistakable meanings as terms, like “black hole" or “European Union"  However, deciding what constitutes a multi-word term is a difficult problem  In writing systems like Chinese, tokenization is a research problem in its own right 
Beyond tokenization, common steps include lowercasing, removing punctuation, stemming (removing suffixes), lemmatization (converting inflections to a base lemma), and normalization, which has never been clearly defined, but often includes grouping abbreviations like “U S A " and “USA", ordinals like “1st" and “first", and variant spellings like “noooooo"  The main goal of these steps is to improve the ratio of tokens (individual occurrences) to types (the distinct things in a corpus)  Each step requires making additional assumptions about which distinctions are relevant: is “apple” different from “Apple”? Is “burnt” different from “burned”? Is “cool" different from “coooool"? Sometimes these steps can actively hide useful patterns, like social meaning BIBREF32   Some of us therefore try do as little modification as possible 
From a multilingual perspective, English and Chinese have an unusually simple inflectional system, and so it is statistically reasonable to treat each inflection as a unique word type  Romance languages have considerably more inflections than English; many indigenous North American languages have still more  For these languages, unseen data is far more likely to include previously-unseen inflections, and therefore, dealing with inflections is more important  On the other hand, the resources for handling inflections vary greatly by language, with European languages dominating the attention of the computational linguistics community thus far 
We sometimes also remove words that are not relevant to our goals, for example by calculating vocabulary frequencies  We construct a “stoplist” of words that we are not interested in  If we are looking for semantic themes we might remove function words like determiners and prepositions  If we are looking for author-specific styles, we might remove all words except function words  Some words are generally meaningful but too frequent to be useful within a specific collection  We sometimes also remove very infrequent words  Their occurrences are too low for robust patterns and removing them helps reducing the vocabulary size 
The choice of processing steps can be guided by theory or knowledge about the domain as well as experimental investigation  When we have labels, predictive accuracy of a model is a way to assess the effect of the processing steps  In unsupervised settings, it is more challenging to understand the effects of different steps  Inferences drawn from unsupervised settings can be sensitive to pre-processing choices BIBREF33   Stemming has been found to provide little measurable benefits for topic modeling and can sometimes even be harmful BIBREF34   All in all, this again highlights the need to document these steps 
Finally, we can also mark up the data, e g , by identifying entities (people, places, organizations, etc ) or parts of speech  Although many NLP tools are available for such tasks, they are often challenged by linguistic variation, such as orthographic variation in historical texts BIBREF35 and social media BIBREF32   Moreover, the performance of NLP tools often drops when applying them outside the training domain, such as applying tools developed on newswire texts to texts written by younger authors BIBREF36   Problems (e g , disambiguation in named entity recognition) are sometimes resolved using considerable manual intervention  This combination of the automated and the manual, however, becomes more difficult as the scale of the data increases, and the `certainty' brought by the latter may have to be abandoned 
Dictionary-based approaches
Dictionaries are frequently used to code texts in content analyses BIBREF37   Dictionaries consist of one or more categories (i e  word lists)  Sometimes the output is simply the number of category occurrences (e g , positive sentiment), thus weighting words within a category equally  In some other cases, words are assigned continuous scores  The high transparency of dictionaries makes them sometimes more suitable than supervised machine learning models  However, dictionaries should only be used if the scores assigned to words match how the words are used in the data (see BIBREF38 for a detailed discussion on limitations)  There are many off-the-shelf dictionaries available (e g , LIWC BIBREF39 )  These are often well-validated, but applying them on a new domain may not be appropriate without additional validation  Corpus- or domain-specific dictionaries can overcome limitations of general-purpose dictionaries 
The dictionaries are often manually compiled, but increasingly they are constructed semi-automatically (e g , BIBREF40 )  When we semi-automatically create a word list, we use automation to identify an initial word list, and human insight to filter it  By automatically generating the initial words lists, words can be identified that human annotators might have difficulty intuiting  By manually filtering the lists, we use our theoretical understanding of the target concept to remove spurious features 
In the introduction study, SAGE BIBREF41 was used to obtain a list of words that distinguished the text in the treatment group (subreddits that were closed by Reddit) from text in the control group (similar subreddits that were not closed)  The researchers then returned to the hate speech definition provided by the European Court of Human Rights, and manually filtered the top SAGE words based on this definition  Not all identified words fitted the definition  The others included: the names of the subreddits themselves, names of related subreddits, community-specific jargon that was not directly related to hate speech, and terms such as IQ and welfare, which were frequently used in discourses of hate speech, but had significant other uses  The word lists provided the measurement instrument for their main result, which is that the use of hate speech throughout Reddit declined after the two treatment subreddits were closed 
Supervised models
Supervised learning is frequently used to scale up analyses  For example, BIBREF42 wanted to analyze the motivations of Movember campaign participants  By developing a classifier based on a small set of annotations, they were able to expand the analysis to over 90k participants 
The choice of supervised learning model is often guided by the task definition and the label types  For example, to identify stance towards rumors based on sequential annotations, an algorithm for learning from sequential BIBREF43 or time series data BIBREF44 could be used  The features (sometimes called variables or predictors) are used by the model to make the predictions  They may vary from content-based features such as single words, sequences of words, or information about their syntactic structure, to meta-information such as user or network information  Deciding on the features requires experimentation and expert insight and is often called feature engineering  For insight-driven analysis, we are often interested in why a prediction has been made and features that can be interpreted by humans may be preferred  Recent neural network approaches often use simple features as input (such as word embeddings or character sequences), which requires less feature engineering but make interpretation more difficult 
Supervised models are powerful, but they can latch on to spurious features of the dataset  This is particularly true for datasets that are not well-balanced, and for annotations that are noisy  In our introductory example on hate speech in Reddit BIBREF0 , the annotations are automatically derived from the forum in which each post appears, and indeed, many of the posts in the forums (subreddits) that were banned by Reddit would be perceived by many as hate speech  But even in banned subreddits, not all of the content is hate speech (e g , some of the top features were self-referential like the name of the subreddit) but a classifier would learn a high weight for these features 
Even when expert annotations are available on the level of individual posts, spurious features may remain  BIBREF45 produced expert annotations of hate speech on Twitter  They found that one of the strongest features for sexism is the name of an Australian TV show, because people like to post sexist comments about the contestants  If we are trying to make claims about what inhibits or encourages hate speech, we would not want those claims to be tied to the TV show's popularity  Such problems are inevitable when datasets are not well-balanced over time, across genres, topics, etc  Especially with social media data, we lack a clear and objective definition of `balance' at this time 
The risk of supervised models latching on to spurious features reinforces the need for interpretability  Although the development of supervised models is usually performance driven, placing more emphasis on interpretability could increase the adoption of these models in insight-driven analyses  One way would be to only use models that are already somewhat interpretable, for example models that use a small number of human-interpretable features  Rather than imposing such restrictions, there is also work on generating post-hoc explanations for individual predictions (e g , BIBREF46 ), even when the underlying model itself is very complex 
Topic modeling
Topic models (e g , LDA BIBREF47 ) are usually unsupervised and therefore less biased towards human-defined categories  They are especially suited for insight-driven analysis, because they are constrained in ways that make their output interpretable  Although there is no guarantee that a “topic” will correspond to a recognizable theme or event or discourse, they often do so in ways that other methods do not  Their easy applicability without supervision and ready interpretability make topic models good for exploration  Topic models are less successful for many performance-driven applications  Raw word features are almost always better than topics for search and document classification  LSTMs and other neural network models are better as language models  Continuous word embeddings have more expressive power to represent fine-grained semantic similarities between words 
A topic model provides a different perspective on a collection  It creates a set of probability distributions over the vocabulary of the collection, which, when combined together in different proportions, best match the content of the collection  We can sort the words in each of these distributions in descending order by probability, take some arbitrary number of most-probable words, and get a sense of what (if anything) the topic is “about”  Each of the text segments also has its own distribution over the topics, and we can sort these segments by their probability within a given topic to get a sense of how that topic is used 
One of the most common questions about topic models is how many topics to use, usually with the implicit assumption that there is a “right” number that is inherent in the collection  We prefer to think of this parameter as more like the scale of a map or the magnification of a microscope  The “right” number is determined by the needs of the user, not by the collection  If the analyst is looking for a broad overview, a relatively small number of topics may be best  If the analyst is looking for fine-grained phenomena, a larger number is better 
After fitting the model, it may be necessary to circle back to an earlier phase  Topic models find consistent patterns  When authors repeatedly use a particular theme or discourse, that repetition creates a consistent pattern  But other factors can also create similar patterns, which look as good to the algorithm  We might notice a topic that has highest probability on French stopwords, indicating that we need to do a better job of filtering by language  We might notice a topic of word fragments, such as “ing”, “tion”, “inter”, indicating that we are not handling end-of-line hyphenation correctly  We may need to add to our stoplist or change how we curate multi-word terms 
Validation
The output of our measurement procedures (in the social sciences often called the “scores”) must now be assessed in terms of their reliability and validity with regard to the (systemized) concept  Reliability aims to capture repeatability, i e  the extent to which a given tool provides consistent results 
Validity assesses the extent to which a given measurement tool measures what it is supposed to measure  In NLP and machine learning, most models are primarily evaluated by comparing the machine-generated labels against an annotated sample  This approach presumes that the human output is the “gold standard" against which performance should be tested  In contrast, when the reliability is measured based on the output of different annotators, no coder is taken as the standard and the likelihood of coders reaching agreement by chance (rather than because they are “correct") is factored into the resulting statistic  Comparing against a “gold standard” suggests that the threshold for human inter- and intra-coder reliability should be particularly high 
Accuracy, as well as other measures such as precision, recall and F-score, are sometimes presented as a measure of validity, but if we do not have a genuinely objective determination of what something is supposed measure—as is often the case in text analysis—then accuracy is perhaps a better indication of reliability than of validity  In that case, validity needs to be assessed based on other techniques like those we discuss later in this section  It is also worth asking what level of accuracy is sufficient for our analysis and to what extent there may be an upper bound, especially when the labels are native to the data or when the notion of a “gold standard” is not appropriate 
For some in the humanities, validation takes the form of close reading, not designed to confirm whether the model output is correct, but to present what BIBREF48 refers to as a form of “further discovery in two directions”  Model outputs tell us something about the texts, while a close reading of the texts alongside those outputs tells us something about the models that can be used for more effective model building  Applying this circular, iterative process to 450 18th-century novels written in three languages, Piper was able to uncover a new form of “conversional novel” that was not previously captured in “literary history's received critical categories” BIBREF48  
Along similar lines, we can subject both the machine-generated output and the human annotations to another round of content validation  That is, take a stratified random sample, selecting observations from the full range of scores, and ask: Do these make sense in light of the systematized concept? If not, what seems to be missing? Or is something extraneous being captured? This is primarily a qualitative process that requires returning to theory and interrogating the systematized concept, indicators, and scores together  This type of validation is rarely done in NLP, but it is especially important when it is difficult to assess what drives a given machine learning model  If there is a mismatch between the scores and systematized concept at this stage, the codebook may need to be adjusted, human coders retrained, more training data prepared, algorithms adjusted, or in some instances, even a new analytical method adopted 
Other types of validation are also possible, such as comparing with other approaches that aim to capture the same concept, or comparing the output with external measures (e g , public opinion polls, the occurrence of future events)  We can also go beyond only evaluating the labels (or point estimates)  BIBREF16 used human judgments to not only assess the positional estimates from a scaling method of latent political traits but also to assess uncertainty intervals  Using different types of validation can increase our confidence in the approach, especially when there is no clear notion of ground truth 
Besides focusing on rather abstract evaluation measures, we could also assess the models in task-based settings using human experts  Furthermore, for insight-driven analyses, it can be more useful to focus on improving explanatory power than making small improvements in predictive performance 
Analysis
In this phase, we use our models to explore or answer our research questions  For example, given a topic model we can look at the connection between topics and metadata elements  Tags such as “hate speech" or metadata information imply a certain way of organizing the collection  Computational models provide another organization, which may differ in ways that provide more insight into how these categories manifest themselves, or fail to do so 
Moreover, when using a supervised approach, the “errors”, i e  disagreement between the system output and human-provided labels, can point towards interesting cases for closer analysis and help us reflect on our conceptualizations  In the words of BIBREF2 , they can be “opportunities for interpretation”  Other types of “failures” can be insightful as well  Sometimes there is a “dog that didn't bark” BIBREF49 –i e , something that everyone thinks we should have found, but we did not  Or, sometimes the failures are telling us about the existence of something in the data that nobody noticed, or thought important, until then (e g , the large number of travel journals in Darwin's reading lists) 
Computational text analysis is not a replacement for but rather an addition to the approaches one can take to analyze social and cultural phenomena using textual data  By moving back and forth between large-scale computational analyses and small-scale qualitative analyses, we can combine their strengths so that we can identify large-scale and long-term trends, but also tell individual stories  For example, the Reddit study on hate speech BIBREF0 raised various follow-up questions: Can we distinguish hate speech from people talking about hate speech? Did people find new ways to express hate speech? If so, did the total amount of online hate speech decrease after all? As possible next steps, a qualitative discourse analyst might examine a smaller corpus to investigate whether commenters were indeed expressing hate speech in new ways; a specialist in interview methodologies might reach out to commenters to better understand the role of online hate speech in their lives  Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them 
Conclusion
Insight-driven computational analysis of text is becoming increasingly common  It not only helps us see more broadly, it helps us see subtle patterns more clearly and allows us to explore radical new questions about culture and society  In this article we have consolidated our experiences, as scholars from very different disciplines, in analyzing text as social and cultural data and described how the research process often unfolds  Each of the steps in the process is time-consuming and labor-intensive  Each presents challenges  And especially when working across disciplines, the research often involves a fair amount of discussion—even negotiation—about what means of operationalization and approaches to analysis are appropriate and feasible  And yet, with a bit of perseverance and mutual understanding, conceptually sound and meaningful work results so that we can truly make use of the exciting opportunities rich textual data offers 
Acknowledgements
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1  Dong Nguyen is supported with an Alan Turing Institute Fellowship (TU/A/000006)  Maria Liakata is a Turing fellow at 40%  We would also like to thank the participants of the “Bridging disciplines in analysing text as social and cultural data” workshop held at the Turing Institute (2017) for insightful discussions  The workshop was funded by a Turing Institute seed funding award to Nguyen and Liakata 

ground_truth: 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. What baseline model is used?
Context 4: 
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 

ground_truth: For Article-Entity placement, they consider two baselines: the first one using only salience-based features, and the second baseline checks if the entity appears in the title of the article. 

For Article-Section Placement, they consider two baselines: the first picks the section with the highest lexical similarity to the article, and the second one picks the most frequent section.

B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 ., B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 .

, S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2, S2: Place the news into the most frequent section in INLINEFORM0