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

Please answer the questions based on the text provided below. How is the dataset annotated?
Context 1: Introduction
In recent years, there has been a movement to leverage social medial data to detect, estimate, and track the change in prevalence of disease  For example, eating disorders in Spanish language Twitter tweets BIBREF0 and influenza surveillance BIBREF1   More recently, social media has been leveraged to monitor social risks such as prescription drug and smoking behaviors BIBREF2 , BIBREF3 , BIBREF4 as well as a variety of mental health disorders including suicidal ideation BIBREF5 , attention deficient hyperactivity disorder BIBREF6 and major depressive disorder BIBREF7   In the case of major depressive disorder, recent efforts range from characterizing linguistic phenomena associated with depression BIBREF8 and its subtypes e g , postpartum depression BIBREF5 , to identifying specific depressive symptoms BIBREF9 , BIBREF10 e g , depressed mood  However, more research is needed to better understand the predictive power of supervised machine learning classifiers and the influence of feature groups and feature sets for efficiently classifying depression-related tweets to support mental health monitoring at the population-level BIBREF11  
This paper builds upon related works toward classifying Twitter tweets representing symptoms of major depressive disorder by assessing the contribution of lexical features (e g , unigrams) and emotion (e g , strongly negative) to classification performance, and by applying methods to eliminate low-value features 
METHODS
Specifically, we conducted a feature ablation study to assess the informativeness of each feature group and a feature elimination study to determine the optimal feature sets for classifying Twitter tweets  We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13   The dataset contains 9,473 annotations for 9,300 tweets  Each tweet is annotated as no evidence of depression (e g , “Citizens fear an economic depression") or evidence of depression (e g , “depressed over disappointment")  If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e g , “feeling down in the dumps"), disturbed sleep (e g , “another restless night"), or fatigue or loss of energy (e g , “the fatigue is unbearable") BIBREF10   For each class, every annotation (9,473 tweets) is binarized as the positive class e g , depressed mood=1 or negative class e g , not depressed mood=0 
Features
Furthermore, this dataset was encoded with 7 feature groups with associated feature values binarized (i e , present=1 or absent=0) to represent potentially informative features for classifying depression-related classes  We describe the feature groups by type, subtype, and provide one or more examples of words representing the feature subtype from a tweet:
lexical features, unigrams, e g , “depressed”;
syntactic features, parts of speech, e g , “cried” encoded as V for verb;
emotion features, emoticons, e g , :( encoded as SAD;
demographic features, age and gender e g , “this semester” encoded as an indicator of 19-22 years of age and “my girlfriend” encoded as an indicator of male gender, respectively;
sentiment features, polarity and subjectivity terms with strengths, e g , “terrible” encoded as strongly negative and strongly subjective;
personality traits, neuroticism e g , “pissed off” implies neuroticism;
LIWC Features, indicators of an individual's thoughts, feelings, personality, and motivations, e g , “feeling” suggestions perception, feeling, insight, and cognitive mechanisms experienced by the Twitter user 
A more detailed description of leveraged features and their values, including LIWC categories, can be found in BIBREF10  
Based on our prior initial experiments using these feature groups BIBREF10 , we learned that support vector machines perform with the highest F1-score compared to other supervised approaches  For this study, we aim to build upon this work by conducting two experiments: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Feature Contribution
Feature ablation studies are conducted to assess the informativeness of a feature group by quantifying the change in predictive power when comparing the performance of a classifier trained with the all feature groups versus the performance without a particular feature group  We conducted a feature ablation study by holding out (sans) each feature group and training and testing the support vector model using a linear kernel and 5-fold, stratified cross-validation  We report the average F1-score from our baseline approach (all feature groups) and report the point difference (+ or -) in F1-score performance observed by ablating each feature set 
By ablating each feature group from the full dataset, we observed the following count of features - sans lexical: 185, sans syntactic: 16,935, sans emotion: 16,954, sans demographics: 16,946, sans sentiment: 16,950, sans personality: 16,946, and sans LIWC: 16,832  In Figure 1, compared to the baseline performance, significant drops in F1-scores resulted from sans lexical for depressed mood (-35 points), disturbed sleep (-43 points), and depressive symptoms (-45 points)  Less extensive drops also occurred for evidence of depression (-14 points) and fatigue or loss of energy (-3 points)  In contrast, a 3 point gain in F1-score was observed for no evidence of depression  We also observed notable drops in F1-scores for disturbed sleep by ablating demographics (-7 points), emotion (-5 points), and sentiment (-5 points) features  These F1-score drops were accompanied by drops in both recall and precision  We found equal or higher F1-scores by removing non-lexical feature groups for no evidence of depression (0-1 points), evidence of depression (0-1 points), and depressive symptoms (2 points) 
Unsurprisingly, lexical features (unigrams) were the largest contributor to feature counts in the dataset  We observed that lexical features are also critical for identifying depressive symptoms, specifically for depressed mood and for disturbed sleep  For the classes higher in the hierarchy - no evidence of depression, evidence of depression, and depressive symptoms - the classifier produced consistent F1-scores, even slightly above the baseline for depressive symptoms and minor fluctuations of change in recall and precision when removing other feature groups suggesting that the contribution of non-lexical features to classification performance was limited  However, notable changes in F1-score were observed for the classes lower in the hierarchy including disturbed sleep and fatigue or loss of energy  For instance, changes in F1-scores driven by both recall and precision were observed for disturbed sleep by ablating demographics, emotion, and sentiment features, suggesting that age or gender (“mid-semester exams have me restless”), polarity and subjective terms (“lack of sleep is killing me”), and emoticons (“wide awake :(”) could be important for both identifying and correctly classifying a subset of these tweets 
Feature Elimination
Feature elimination strategies are often taken 1) to remove irrelevant or noisy features, 2) to improve classifier performance, and 3) to reduce training and run times  We conducted an experiment to determine whether we could maintain or improve classifier performances by applying the following three-tiered feature elimination approach:
Reduction We reduced the dataset encoded for each class by eliminating features that occur less than twice in the full dataset 
Selection We iteratively applied Chi-Square feature selection on the reduced dataset, selecting the top percentile of highest ranked features in increments of 5 percent to train and test the support vector model using a linear kernel and 5-fold, stratified cross-validation 
Rank We cumulatively plotted the average F1-score performances of each incrementally added percentile of top ranked features  We report the percentile and count of features resulting in the first occurrence of the highest average F1-score for each class 
All experiments were programmed using scikit-learn 0 18 
The initial matrices of almost 17,000 features were reduced by eliminating features that only occurred once in the full dataset, resulting in 5,761 features  We applied Chi-Square feature selection and plotted the top-ranked subset of features for each percentile (at 5 percent intervals cumulatively added) and evaluated their predictive contribution using the support vector machine with linear kernel and stratified, 5-fold cross validation 
In Figure 2, we observed optimal F1-score performance using the following top feature counts: no evidence of depression: F1: 87 (15th percentile, 864 features), evidence of depression: F1: 59 (30th percentile, 1,728 features), depressive symptoms: F1: 55 (15th percentile, 864 features), depressed mood: F1: 39 (55th percentile, 3,168 features), disturbed sleep: F1: 46 (10th percentile, 576 features), and fatigue or loss of energy: F1: 72 (5th percentile, 288 features) (Figure 1)  We note F1-score improvements for depressed mood from F1: 13 at the 1st percentile to F1: 33 at the 20th percentile 
We observed peak F1-score performances at low percentiles for fatigue or loss of energy (5th percentile), disturbed sleep (10th percentile) as well as depressive symptoms and no evidence of depression (both 15th percentile) suggesting fewer features are needed to reach optimal performance  In contrast, peak F1-score performances occurred at moderate percentiles for evidence of depression (30th percentile) and depressed mood (55th percentile) suggesting that more features are needed to reach optimal performance  However, one notable difference between these two classes is the dramatic F1-score improvements for depressed mood i e , 20 point increase from the 1st percentile to the 20th percentile compared to the more gradual F1-score improvements for evidence of depression i e , 11 point increase from the 1st percentile to the 20th percentile  This finding suggests that for identifying depressed mood a variety of features are needed before incremental gains are observed 
RESULTS
From our annotated dataset of Twitter tweets (n=9,300 tweets), we conducted two feature studies to better understand the predictive power of several feature groups for classifying whether or not a tweet contains no evidence of depression (n=6,829 tweets) or evidence of depression (n=2,644 tweets)  If there was evidence of depression, we determined whether the tweet contained one or more depressive symptoms (n=1,656 tweets) and further classified the symptom subtype of depressed mood (n=1,010 tweets), disturbed sleep (n=98 tweets), or fatigue or loss of energy (n=427 tweets) using support vector machines  From our prior work BIBREF10 and in Figure 1, we report the performance for prediction models built by training a support vector machine using 5-fold, stratified cross-validation with all feature groups as a baseline for each class  We observed high performance for no evidence of depression and fatigue or loss of energy and moderate performance for all remaining classes 
Discussion
We conducted two feature study experiments: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Future Work
Our next step is to address the classification of rarer depressive symptoms suggestive of major depressive disorder from our dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia BIBREF15 , BIBREF16   We are developing a population-level monitoring framework designed to estimate the prevalence of depression (and depression-related symptoms and psycho-social stressors) over millions of United States-geocoded tweets  Identifying the most discriminating feature sets and natural language processing classifiers for each depression symptom is vital for this goal 
Conclusions
In summary, we conducted two feature study experiments to assess the contribution of feature groups and to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy  From these experiments, we conclude that simple lexical features and reduced feature sets can produce comparable results to the much larger feature dataset 
Acknowledgments
Research reported in this publication was supported by the National Library of Medicine of the [United States] National Institutes of Health under award numbers K99LM011393 and R00LM011393  This study was granted an exemption from review by the University of Utah Institutional Review Board (IRB 00076188)  Note that in order to protect tweeter anonymity, we have not reproduced tweets verbatim  Example tweets shown were generated by the researchers as exemplars only  Finally, we would like to thank the anonymous reviewers of this paper for their valuable comments 

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

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

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. Is WordNet useful for taxonomic reasoning for this task?
Context 2: Introduction
Automatically answering questions, especially in the open-domain setting (i e , where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities  For example, knowing the answers to the two questions in Figure FIGREF1 requires identifying a specific ISA relation (i e , that cooking is a type of learned behavior) as well as recalling the definition of a concept (i e , that global warming is defined as a worldwide increase in temperature)  In the multiple-choice setting, which is the variety of question-answering (QA) that we focus on in this paper, there is also pragmatic reasoning involved in selecting optimal answer choices (e g , while greenhouse effect might in some other context be a reasonable answer to the second question in Figure FIGREF1, global warming is a preferable candidate) 
Recent successes in QA, driven largely by the creation of new resources BIBREF2, BIBREF3, BIBREF4, BIBREF5 and advances in model pre-training BIBREF6, BIBREF7, raise a natural question: do state-of-the-art multiple-choice QA (MCQA) models that excel at standard tasks really have basic knowledge and reasoning skills?
Most existing MCQA datasets are constructed through either expensive crowd-sourcing BIBREF8 or hand engineering effort, in the former case making it possible to collect large amounts of data at the cost of losing systematic control over the semantics of the target questions  Hence, doing a controlled experiment to answer such a question for QA is difficult given a lack of targeted challenge datasets 
Having definitive empirical evidence of model competence on any given phenomenon requires constructing a wide range of systematic tests  For example, in measuring competence of definitions, not only do we want to see that the model can handle individual questions such as Figure FIGREF1 1 inside of benchmark tasks, but that it can answer a wider range of questions that exhaustively cover a broad set of concepts and question perturbations (i e , systematic adjustments to how the questions are constructed)  The same applies to ISA reasoning; not only is it important to recognize in the question in Figure FIGREF1 1 that cooking is a learned behavior, but also that cooking is a general type of behavior or, through a few more inferential steps, a type of human activity 
In this paper, we look at systematically constructing such tests by exploiting the vast amounts of structured information contained in various types of expert knowledge such as knowledge graphs and lexical taxonomies  Our general methodology works as illustrated in Figure FIGREF1: given any MCQA model trained on a set of benchmark tasks, we systematically generate a set of synthetic dataset probes (i e , MCQA renderings of the target information) from information in expert knowledge sources  We then use these probes to ask two empirical questions: 1) how well do models trained on benchmark tasks perform on these probing tasks and; 2) can such models be re-trained to master new challenges with minimal performance loss on their original tasks?
While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA  For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary resources  We devise probes that measure model competence in definition and taxonomic knowledge in different settings (including hypernymy, hyponymy, and synonymy detection, and word sense disambiguation)  This choice is motivated by fact that the science domain is considered particularly challenging for QA BIBREF10, BIBREF11, BIBREF12, and existing science benchmarks are known to involve widespread use of such knowledge (see BIBREF1, BIBREF13 for analysis), which is also arguably fundamental to more complex forms of reasoning 
We show that accurately probing QA models via synthetic datasets is not straightforward, as unexpected artifacts can easily arise in such data  This motivates our carefully constructed baselines and close data inspection to ensure probe quality 
Our results confirm that transformer-based QA models have a remarkable ability to recognize certain types of knowledge captured in our probes—even without additional fine-tuning  Such models can even outperform strong task-specific models trained directly on our probing tasks (e g , on definitions, our best model achieves 77% test accuracy without specialized training, as opposed to 51% for a task-specific LSTM-based model)  We also show that the same models can be effectively re-fine-tuned on small samples (even 100 examples) of probe data, and that high performance on the probes tends to correlate with a smaller drop in the model's performance on the original QA task 
Our comprehensive assessment reveals several interesting nuances to the overall positive trend  For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops  Further, the accuracy of even our best models on the WordNetQA probe drops by 14-44% under our cluster-based analysis, which assesses whether a model knows several facts about each individual concept, rather than just being good at answering isolated questions  State-of-the-art QA models thus have much room to improve even in some fundamental building blocks, namely definitions and taxonomic hierarchies, of more complex forms of reasoning 
Related Work
We follow recent work on constructing challenge datasets for probing neural models, which has primarily focused on the task of natural language inference (NLI) BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18  Most of this work looks at constructing data through adversarial generation methods, which have also been found useful for creating stronger models BIBREF19  There has also been work on using synthetic data of the type we consider in this paper BIBREF20, BIBREF21, BIBREF22  We closely follow the methodology of BIBREF22, who use hand-constructed linguistic fragments to probe NLI models and study model re-training using a variant of the inoculation by fine-tuning strategy of BIBREF23  In contrast, we focus on probing open-domain MCQA models (see BIBREF24 for a related study in the reading comprehension setting) as well as constructing data from much larger sources of structured knowledge 
Our main study focuses on probing the BERT model and fine-tuning approach of BIBREF7, and other variants thereof, which are all based on the transformer architecture of BIBREF25  Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models BIBREF26, BIBREF27, BIBREF28, which, similar to our work, probe models using structured knowledge sources  This prior work, however, primarily focuses on unearthing the knowledge contained in the underlying language models as is without further training, using simple (single token) cloze-style probing tasks and templates (similar to what we propose in Section SECREF3)  In contrast, we focus on understanding the knowledge contained in language models after they have been trained for a QA end-task using benchmark datasets in which such knowledge is expected to be widespread  Further, our evaluation is done before and after these models are fine-tuned on our probe QA tasks, using a more complex set of QA templates and target inferences 
The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31  In the area of MCQA, there is related work on constructing questions from tuples BIBREF32, BIBREF3, both of which involve standard crowd annotation to elicit question-answer pairs (see also BIBREF33, BIBREF34)  In contrast to this work, we focus on generating data in an entirely automatic fashion, which obviates the need for expensive annotation and gives us the flexibility to construct much larger datasets that control a rich set of semantic aspects of the target questions 
Dataset Probes and Construction
Our probing methodology starts by constructing challenge datasets (Figure FIGREF1, yellow box) from a target set of knowledge resources  Each of our probing datasets consists of multiple-choice questions that include a question $\textbf {q}$ and a set of answer choices or candidates $\lbrace a_{1},   a_{N}\rbrace $  This section describes in detail the 5 different datasets we build, which are drawn from two sources of expert knowledge, namely WordNet BIBREF35 and the GNU Collaborative International Dictionary of English (GCIDE)  We describe each resource in turn, and explain how the resulting dataset probes, which we call WordNetQA and DictionaryQA, are constructed 
For convenience, we will describe each source of expert knowledge as a directed, edge-labeled graph $G$  The nodes of this graph are $\mathcal {V} = \mathcal {C} \cup \mathcal {W} \cup \mathcal {S} \cup \mathcal {D}$, where $\mathcal {C}$ is a set of atomic concepts, $\mathcal {W}$ a set of words, $\mathcal {S}$ a set of sentences, and $\mathcal {D}$ a set of definitions (see Table TABREF4 for details for WordNet and GCIDE)  Each edge of $G$ is directed from an atomic concept in $\mathcal {C}$ to another node in $V$, and is labeled with a relation, such as hypernym or isa$^\uparrow $, from a set of relations $\mathcal {R}$ (see Table TABREF4) 
When defining our probe question templates, it will be useful to view $G$ as a set of (relation, source, target) triples $\mathcal {T} \subseteq \mathcal {R} \times \mathcal {C} \times \mathcal {V}$  Due to their origin in an expert knowledge source, such triples preserve semantic consistency  For instance, when the relation in a triple is def, the corresponding edge maps a concept in $\mathcal {C}$ to a definition in $\mathcal {D}$ 
To construct probe datasets, we rely on two heuristic functions, defined below for each individual probe: $\textsc {gen}_{\mathcal {Q}}(\tau )$, which generates gold question-answer pairs $(\textbf {q},\textbf {a})$ from a set of triples $\tau \subseteq \mathcal {T}$ and question templates $\mathcal {Q}$, and $\textsc {distr}(\tau ^{\prime })$, which generates distractor answers choices $\lbrace a^{\prime }_{1},   a^{\prime }_{N-1} \rbrace $ based on another set of triples $\tau ^{\prime }$ (where usually $\tau \subset \tau ^{\prime }$)  For brevity, we will use $\textsc {gen}(\tau )$ to denote $\textsc {gen}_{\mathcal {Q}}(\tau )$, leaving question templates $\mathcal {Q}$ implicit 
Dataset Probes and Construction ::: WordNetQA
WordNet is an English lexical database consisting of around 117k concepts, which are organized into groups of synsets that each contain a gloss (i e , a definition of the target concept), a set of representative English words (called lemmas), and, in around 33k synsets, example sentences  In addition, many synsets have ISA links to other synsets that express complex taxonomic relations  Figure FIGREF6 shows an example and Table TABREF4 summarizes how we formulate WordNet as a set of triples $\mathcal {T}$ of various types  These triples together represent a directed, edge-labeled graph $G$  Our main motivation for using WordNet, as opposed to a resource such as ConceptNet BIBREF36, is the availability of glosses ($\mathcal {D}$) and example sentences ($\mathcal {S}$), which allows us to construct natural language questions that contextualize the types of concepts we want to probe 
Dataset Probes and Construction ::: WordNetQA ::: Example Generation @!START@$\textsc {gen}(\tau )$@!END@ 
We build 4 individual datasets based on semantic relations native to WordNet (see BIBREF37): hypernymy (i e , generalization or ISA reasoning up a taxonomy, ISA$^\uparrow $), hyponymy (ISA$^{\downarrow }$), synonymy, and definitions  To generate a set of questions in each case, we employ a number of rule templates $\mathcal {Q}$ that operate over tuples  A subset of such templates is shown in Table TABREF8  The templates were designed to mimic naturalistic questions we observed in our science benchmarks 
For example, suppose we wish to create a question $\textbf {q}$ about the definition of a target concept $c \in \mathcal {C}$  We first select a question template from $\mathcal {Q}$ that first introduces the concept $c$ and its lemma $l \in \mathcal {W}$ in context using the example sentence $s \in \mathcal {S}$, and then asks to identify the corresponding WordNet gloss $d \in \mathcal {D}$, which serves as the gold answer $\textbf {a}$  The same is done for ISA reasoning; each question about a hypernym/hyponym relation between two concepts $c \rightarrow ^{\uparrow /\downarrow } c^{\prime } \in \mathcal {T}_{i}$ (e g , $\texttt {dog} \rightarrow ^{\uparrow /\downarrow } \texttt {animal/terrier}$) first introduces a context for $c$ and then asks for an answer that identifies $c^{\prime }$ (which is also provided with a gloss so as to contain all available context) 
In the latter case, the rules $(\texttt {isa}^{r},c,c^{\prime }) \in \mathcal {T}_i$ in Table TABREF8 cover only direct ISA links from $c$ in direction $r \in \lbrace \uparrow ,\downarrow \rbrace $  In practice, for each $c$ and direction $r$, we construct tests that cover the set HOPS$(c,r)$ of all direct as well as derived ISA relations of $c$:
This allows us to evaluate the extent to which models are able to handle complex forms of reasoning that require several inferential steps or hops 
Dataset Probes and Construction ::: WordNetQA ::: Distractor Generation: @!START@$\textsc {distr}(\tau ^{\prime })$@!END@ 
An example of how distractors are generated is shown in Figure FIGREF6, which relies on similar principles as above  For each concept $c$, we choose 4 distractor answers that are close in the WordNet semantic space  For example, when constructing hypernymy tests for $c$ from the set hops$(c,\uparrow )$, we build distractors by drawing from $\textsc {hops}(c,\downarrow )$ (and vice versa), as well as from the $\ell $-deep sister family of $c$, defined as follows  The 1-deep sister family is simply $c$'s siblings or sisters, i e , the other children $\tilde{c} \ne c$ of the parent node $c^{\prime }$ of $c$  For $\ell > 1$, the $\ell $-deep sister family also includes all descendants of each $\tilde{c}$ up to $\ell -1$ levels deep, denoted $\textsc {hops}_{\ell -1}(\tilde{c},\downarrow )$  Formally:
For definitions and synonyms we build distractors from all of these sets (with a similar restriction on the depth of sister distractors as noted above)  In doing this, we can systematically investigate model performance on a wide range of distractor sets 
Dataset Probes and Construction ::: WordNetQA ::: Perturbations and Semantic Clusters
Based on how we generate data, for each concept $c$ (i e , atomic WordNet synset) and probe type (i e , definitions, hypernymy, etc ), we have a wide variety of questions related to $c$ that manipulate 1) the complexity of reasoning that is involved (e g , the number of inferential hops) and; 2) the types of distractors (or distractor perturbations) that are employed  We call such sets semantic clusters  As we describe in the next section, semantic clusters allow us to devise new types of evaluation that reveal whether models have comprehensive and consistent knowledge of target concepts (e g , evaluating whether a model can correctly answer several questions associated with a concept, as opposed to a few disjoint instances) 
Details of the individual datasets are shown in Table TABREF12  From these sets, we follow BIBREF22 in allocating a maximum of 3k examples for training and reserve the rest for development and testing  Since we are interested in probing, having large held-out sets allows us to do detailed analysis and cluster-based evaluation 
Dataset Probes and Construction ::: DictionaryQA
The DictionaryQA dataset is created from the GCIDE dictionary, which is a comprehensive open-source English dictionary built largely from the Webster's Revised Unabridged Dictionary BIBREF38  Each entry consists of a word, its part-of-speech, its definition, and an optional example sentence (see Table TABREF14)  Overall, 33k entries (out of a total of 155k) contain example sentences/usages  As with the WordNet probes, we focus on this subset so as to contextualize each word being probed  In contrast to WordNet, GCIDE does not have ISA relations or explicit synsets, so we take each unique entry to be a distinct sense  We then use the dictionary entries to create a probe that centers around word-sense disambiguation, as described below 
Dataset Probes and Construction ::: DictionaryQA ::: Example and Distractor Generation 
To generate gold questions and answers, we use the same generation templates for definitions exemplified in Figure TABREF8 for WordNetQA  To generate distractors, we simply take alternative definitions for the target words that represent a different word sense (e g , the alternative definitions of gift shown in Table TABREF14), as well as randomly chosen definitions if needed to create a 5-way multiple choice question  As above, we reserve a maximum of 3k examples for training  Since we have only 9k examples in total in this dataset (see WordSense in Table TABREF12), we also reserve 3k each for development and testing 
We note that initial attempts to build this dataset through standard random splitting gave rise to certain systematic biases that were exploited by the choice-only baseline models described in the next section, and hence inflated overall model scores  After several efforts at filtering we found that, among other factors, using definitions from entries without example sentences as distractors (e g , the first two entries in Table TABREF14) had a surprising correlation with such biases  This suggests that possible biases involving differences between dictionary entries with and without examples can taint the resulting automatically generated MCQA dataset (for more discussion on the pitfalls involved with automatic dataset construction, see Section SECREF5) 
Probing Methodology and Modeling
Given the probes above, we now can start to answer the empirical questions posed at the beginning  Our main focus is on looking at transformer-based MCQA models trained in the science domain (using the benchmarks shown in Table TABREF21)  In this section, we provide details of MCQA and the target models, as well as several baselines that we use to sanity check our new datasets  To evaluate model competence, we look at a combination of model performance after science pre-training and after additional model fine-tuning using the lossless inoculation strategy of BIBREF22 (Section SECREF22)  In Section SECREF24, we also discuss a cluster-level accuracy metric for measuring performance over semantic clusters 
Probing Methodology and Modeling ::: Task Definition and Modeling
Given a dataset $D =\lbrace (\textbf {q}^{(d)}, \lbrace a_{1}^{(d)},   , a_{N}^{(d)}\rbrace ) \rbrace _{d}^{\mid D \mid }$ consisting of pairs of questions stems $\textbf {q}$ and answer choices $a_{i}$, the goal is to find the correct answer $a_{i^{*}}$ that correctly answers each $\textbf {q}$  Throughout this paper, we look at 5-way multiple-choice problems (i e , where each $N=5$) 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Question+Answer Encoder 
To model this, our investigation centers around the use of the transformer-based BIBREF25 BERT encoder and fine-tuning approach of BIBREF7 (see also BIBREF6)  For each question and individual answer pair $q^{(j)}_{a_{i}}$, we assume the following rendering of this input:
which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i e , the classifier token) $\textbf {c}_{i}$:
The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j)}_{i}}$, which uses an additional set of classification parameters $\textbf {v} \in \mathbb {R}^{H}$ that are optimized (along with the full transformer network) by taking the final loss of the probability of each correct answer $p_{i^{*}}$ over all answer choices:
We specifically use BERT-large uncased with whole-word masking, as well as the RoBERTa-large model from BIBREF9, which is a more robustly trained version of the original BERT model  Our system uses the implementations provided in AllenNLP BIBREF39 and Huggingface BIBREF40 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks 
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42)  To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models 
Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},   ,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$)  A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows:
With these contextual representations, different baseline models can be constructed  For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way:
for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$ 
A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper)  In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process 
A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above  Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$  In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models 
Probing Methodology and Modeling ::: Inoculation and Pre-training
Using the various models introduced above, we train these models on benchmark tasks in the science domain and look at model performance on our probes with and without additional training on samples of probe data, building on the idea of inoculation from BIBREF23  Model inoculation is the idea of continuing to train models on new challenge tasks (in our cases, separately for each probe) using only a small amount of examples  Unlike in ordinary fine-tuning, the goal is not to learn an entirely re-purposed model, but to improve on (or vaccinate against) particular phenomena (e g , our synthetic probes) that potentially deviate from a model's original training distribution (but that nonetheless might involve knowledge already contained in the model) 
In the variant proposed in BIBREF22, for each pre-trained (science) model and architecture $M_{a}$ we continue training the model on $k$ new probe examples (with a maximum of $k=$ 3k) under a set of different hyper-parameter configurations $j \in \lbrace 1,    , J\rbrace $ and identify, for each $k$, the model $M_{*}^{a,k}$ with the best aggregate performance $S$ on the original (orig) and new task:
As in BIBREF22, we found all models to be especially sensitive to different learning rates, and performed comprehensive hyper-parameters searches that also manipulate the number of iterations and random seeds used 
Using this methodology, we can see how much exposure to new data it takes for a given model to master a new task, and whether there are phenomena that stress particular models (e g , lead to catastrophic forgetting of the original task)  Given the restrictions on the number of fine-tuning examples, our assumption is that when models are able to maintain good performance on their original task during inoculation, the quickness with which they are able to learn the inoculated task provides evidence of prior competence, which is precisely what we aim to probe  To measure past performance, we define a model's inoculation cost as the difference in the performance of this model on its original task before and after inoculation 
We pre-train on an aggregated training set of the benchmark science exams detailed in Table TABREF21, and created an aggregate development set of around 4k science questions for evaluating overall science performance and inoculation costs  To handle the mismatch between number of answer choices in these sets, we made all sets 5-way by adding empty answers as needed  We also experimented with a slight variant of inoculation, called add-some inoculation, which involves balancing the inoculation training sets with naturalistic science questions  We reserve the MCQL dataset in Table TABREF21 for this purpose, and experiment with balancing each probe example with a science example (x1 matching) and adding twice as many science questions (x2 matching, up to 3k) for each new example 
Probing Methodology and Modeling ::: Evaluating Model Competence
The standard way to evaluate our MCQA models is by looking at the overall accuracy of the correct answer prediction, or what we call instance-level accuracy (as in Table TABREF25)  Given the nature of our data and the existence of semantic clusters as detailed in Section SECREF11 (i e , sets of questions and answers under different distractor choices and inference complexity), we also measure a model's cluster-level (or strict cluster) accuracy, which requires correctly answering all questions in a cluster  Example semantic clusters are shown in Table TABREF30; in the first case, there are 6 ISA$^\uparrow $ questions (including perturbations) about the concept trouser n 01 (e g , involving knowing that trousers are a type of consumer good and garment/clothing), which a model must answer in order to receive full credit 
Our cluster-based analysis is motivated by the idea that if a model truly knows the meaning of a given concept, such as the concept of trousers, then it should be able to answer arbitrary questions about this concept without sensitivity to varied distractors  While our strict cluster metric is simplistic, it takes inspiration from work on visual QA BIBREF53, and allows us to evaluate how consistent and robust models are across our different probes, and to get insight into whether errors are concentrated on a small set of concepts or widespread across clusters 
Results and Findings
In this section, we provide the results of the empirical questions first introduced in Figure FIGREF1, starting with the results of our baseline models 
Results and Findings ::: Are our Probes Sufficiently Challenging?
As shown in Table TABREF25, most of our partial-input baselines (i e , Choice-Only and Choice-to-Choice models) failed to perform well on our dataset probes across a wide range of models, showing that such probes are generally immune from biases relating to how distractors were generated  As already discussed in Section SECREF13, however, initial versions of the DictionaryQA dataset had unforeseen biases partly related to whether distractors were sampled from entries without example sentences, which resulted in high Choice-Only-GloVe scores ranging around 56% accuracy before a filtering step was applied to remove these distractors 
We had similar issues with the hypernymy probe which, even after a filtering step that used our Choice-to-Choice-GloVe model, still leads to high results on the BERT and RoBERTa choice-only models  Given that several attempts were made to entirely de-duplicate the different splits (both in terms of gold answers and distractor types), the source of these biases is not at all obvious, which shows how easy it is for unintended biases in expert knowledge to appear in the resulting datasets and the importance of having rigorous baselines  We also note the large gap in some cases between the BERT and RoBERTa versus GloVe choice-only models, which highlights the need for having partial-input baselines that use the best available models 
Using a more conventional set of Task-Specific QA models (i e , the LSTM-based Question-to-Choice models trained directly on the probes), we can see that results are not particularly strong on any of the datasets, suggesting that our probes are indeed sufficiently challenging and largely immune from overt artifacts  The poor performance of the VecSimilarity (which uses pre-trained Word2Vec embeddings without additional training) provides additional evidence that elementary lexical matching strategies are insufficient for solving any of the probing tasks 
Results and Findings ::: How well do pre-trained MCQA models do?
Science models that use non-transformer based encoders, such as the ESIM model with GloVe and ELMO, perform poorly across all probes, in many cases scoring near random chance, showing limits to how well they generalize from science to other tasks even with pre-trained GloVe and ELMO embeddings  In sharp contrast, the transformer models have mixed results, the most striking result being the RoBERTa models on the definitions and synonymy probes (achieving a test accuracy of 77% and 61%, respectively), which outperform several of the task-specific LSTM models trained directly on the probes  At first glance, this suggests that RoBERTa, which generally far outpaces even BERT across most probes, has high competence of definitions and synonyms even without explicit training on our new tasks 
Given the controlled nature of our probes, we can get a more detailed view of how well the science models are performing across different reasoning and distractor types, as shown in the first column of Figure FIGREF28 for ESIM and RoBERTa  The ESIM science model without training has uniformly poor performance across all categories, whereas the performance of RoBERTa is more varied  Across all datasets and number of hops (i e , the rows in the heat maps), model performance for RoBERTa is consistently highest among examples with random distractors (i e , the first column), and lowest in cases involving distractors that are closest in WordNet space (e g , sister and ISA, or up/down, distractors of distance $k^{\prime }=1$)  This is not surprising, given that, in the first case, random distractors are likely to be the easiest category (and the opposite for distractors close in space), but suggests that RoBERTa might only be getting the easiest cases correct 
Model performance also clearly degrades for hypernymy and hyponymy across all models as the number of hops $k$ increases (see red dashed boxes)  For example, problems that involve hyponym reasoning with sister distractors of distance $k^{\prime }=1$ (i e , the second column) degrades from 47% to 15% when the number of hops $k$ increases from 1 to 4  This general tendency persists even after additional fine-tuning, as we discuss next, and gives evidence that models are limited in their capacity for certain types of multi-hop inferences 
As discussed by BIBREF26, the choice of generation templates can have a significant effect on model performance  The results so far should therefore be regarded as a lower bound on model competence  It is possible that model performance is high for definitions, for example, because the associated templates best align with the science training distribution (which we know little about)  For this reason, the subsequent inoculation step is important—it gives the model an opportunity to learn about our target templates and couple this learned knowledge with its general knowledge acquired during pre-training and science training (which is, again, what we aim to probe) 
Results and Findings ::: Can Models Be Effectively Inoculated?
Model performance after additional fine-tuning, or inoculation, is shown in the last 3 rows of Table TABREF25, along with learning curves shown in Figure FIGREF29 for a selection of probes and models  In the former case, the performance represents the model (and inoculation amount) with the highest aggregate performance over the old task and new probe  Here we again see the transformer-based models outperform non-transformer models, and that better models correlate with lower inoculation costs  For example, when inoculating on synonymy, the cost for ESIM is around 7% reduced accuracy on its original task, as opposed to $< 1$% and around 1% for BERT and RoBERTa, respectively  This shows the high capacity for transformer models to absorb new tasks with minimal costs, as also observed in BIBREF22 for NLI 
As shown in Figure FIGREF29, transformer models tend to learn most tasks fairly quickly while keeping constant scores on their original tasks (i e , the flat dashed lines observed in plots 1-4), which gives evidence of high competence  In both cases, add-some inoculation proves to be a cheap and easy way to 1) improve scores on the probing tasks (i e , the solid black and blue lines in plot 1) and; 2) minimize loss on science (e g , the blue and black dashed lines in plots 2-4)  The opposite is the case for ESIM (plots 5-6); models are generally unable to simultaneously learn individual probes without degrading on their original task, and adding more science data during inoculation confuses models on both tasks 
As shown in Figure FIGREF28, RoBERTa is able to significantly improve performance across most categories even after inoculation with a mere 100 examples (the middle plot), which again provides strong evidence of prior competence  As an example, RoBERTa improves on 2-hop hyponymy inference with random distractors by 18% (from 59% to 77%)  After 3k examples, the model has high performance on virtually all categories (the same score increases from 59% to 87%), however results still tends to degrade as a function of hop and distractor complexity, as discussed above 
Despite the high performance of our transformer models after inoculation, model performance on most probes (with the exception of Definitions) averages around 80% for our best models  This suggests that there is still considerable room for improvement, especially for synonymy and word sense, which is a topic that we discuss more in Section SECREF6 
Results and Findings ::: Are Models Consistent across Clusters?
Table TABREF32 shows cluster-level accuracies for the different WordNetQA probes  As with performance across the different inference/distractor categories, these results are mixed  For some probes, such as definitions, our best models appear to be rather robust; e g , our RoBERTa model has a cluster accuracy of $75\%$, meaning that it can answer all questions perfectly for 75% of the target concepts and that errors are concentrated on a small minority (25%) of concepts  On synonymy and hypernymy, both BERT and RoBERTa appear robust on the majority of concepts, showing that errors are similarly concentrated  In contrast, our best model on hyponymy has an accuracy of 36%, meaning that its errors are spread across many concepts, thus suggesting less robustness 
Table TABREF30 shows a selection of semantic clusters involving ISA reasoning, as well as the model performance over different answers (shown symbolically) and perturbations  For example, in the the second case, the cluster is based around the concept/synset oppose v 06 and involves 4 inferences and a total 24 questions (i e , inferences with perturbations)  Our weakest model, ESIM, answers only 5 out of 24 questions correctly, whereas RoBERTa gets 21/24  In the other cases, RoBERTa gets all clusters correct, whereas BERT and ESIM get none of them correct 
We emphasize that these results only provide a crude look into model consistency and robustness  Recalling again the details in Table TABREF12, probes differ in terms of average size of clusters  Hyponymy, in virtue of having many more questions per cluster, might simply be a much more difficult dataset  In addition, such a strict evaluation does not take into account potential errors inside of clusters, which is an important issue that we discuss in the next section  We leave addressing such issues and coming up with more insightful cluster-based metrics for future work 
Discussion and Conclusion
We presented several new challenge datasets and a novel methodology for automatically building such datasets from knowledge graphs and taxonomies  We used these to probe state-of-the-art open-domain QA models (centering around models based on variants of BERT)  While our general methodology is amendable to any target knowledge resource or QA model/domain, we focus on probing definitions and ISA knowledge using open-source dictionaries and MCQA models trained in the science domain 
We find, consistent with recent probing studies BIBREF26, that transformer-based models have a remarkable ability to answer questions that involve complex forms of relational knowledge, both with and without explicit exposure to our new target tasks  In the latter case, a newer RoBERTa model trained only on benchmark science tasks is able to outperform several task-specific LSTM-based models trained directly on our probing data  When re-trained on small samples (e g , 100 examples) of probing data using variations of the lossless inoculation strategy from BIBREF22, RoBERTa is able to master many aspects of our probes with virtually no performance loss on its original QA task 
These positive results suggest that transformer-based models, especially models additionally fine-tuned on small samples of synthetic data, can be used in place of task-specific models used for querying relational knowledge, as has already been done for targeted tasks such as word sense disambiguation BIBREF54  Since models seem to already contain considerable amounts of relational knowledge, our simple inoculation strategy, which tries to nudge models to bring out this knowledge explicitly, could serve as a cheaper alternative to recent attempts to build architectures that explicitly incorporate structured knowledge BIBREF55; we see many areas where our inoculation strategy could be improved for such purposes, including having more complex loss functions that manage old and new information, as well as using techniques that take into account network plasticity BIBREF56 
The main appeal of using automatically generate datasets is the ability to systematically manipulate and control the complexity of target questions, which allows for more controlled experimentation and new forms of evaluation  Despite the positive results described above, results that look directly at the effect of different types of distractors and the complexity of reasoning show that our best models, even after additional fine-tuning, struggle with certain categories of hard distractors and multi-hop inferences  For some probes, our cluster-based analysis also reveals that errors are widespread across concept clusters, suggesting that models are not always consistent and robust  These results, taken together with our findings about the vulnerability of synthetic datasets to systematic biases, suggest that there is much room for improvement and that the positive results should be taken with a grain of salt  Developing better ways to evaluate semantic clusters and model robustness would be a step in this direction 
We emphasize that using synthetic versus naturalistic QA data comes with important trade-offs  While we are able to generate large amounts of systematically controlled data at virtually no cost or need for manual annotation, it is much harder to validate the quality of such data at such a scale and such varying levels of complexity  Conversely, with benchmark QA datasets, it is much harder to perform the type of careful manipulations and cluster-based analyses we report here  While we assume that the expert knowledge we employ, in virtue of being hand-curated by human experts, is generally correct, we know that such resources are fallible and error-prone  Initial crowd-sourcing experiments that look at validating samples of our data show high agreement across probes and that human scores correlate with the model trends across the probe categories  More details of these studies are left for future work 

ground_truth: Unanswerable

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 baseline model is used?
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: 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