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

Please answer the questions based on the text provided below. Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?
Context 1: Introduction
Question answering (QA) has been a blooming research field for the last decade  Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language  Three tasks have been proposed for selection-based QA  Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks  Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e g , IBM Watson, MIT Start) 
Several datasets have been released for selection-based QA  wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task  feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts  yang:15a introduced WikiQA for answer selection and triggering  jurczyk:16 created SelQA for large real-scale answer triggering  rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension  Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection 
These corpora make it possible to evaluate the robustness of statistical question answering learning  Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made  In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA  We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 )  We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 )  Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ) 
Intrinsic Analysis
Four publicly available corpora are selected for our analysis  These corpora are based on Wikipedia, so more comparable than the others, and have already been used for the evaluation of several QA systems 
WikiQA BIBREF6 comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles  The abstracts of these articles are then extracted to create answer candidates  The assumption is made that if many queries lead to the same article, it must contain the answer context; however, this assumption fails for some occasions, which makes this dataset more challenging  Since the existence of answer contexts is not guaranteed in this task, it is called answer triggering instead of answer selection 
SelQA BIBREF7 is a product of five annotation tasks through crowdsourcing  It consists of about 8K questions where a half of the questions are paraphrased from the other half, aiming to reduce contextual similarities between questions and answers  Each question is associated with a section in Wikipedia where the answer context is guaranteed, and also with five sections selected from the entire Wikipedia where the selection is made by the Lucene search engine  This second dataset does not assume the existence of the answer context, so can be used for the evaluation of answer triggering 
SQuAD BIBREF12 presents 107K+ crowdsourced questions on 536 Wikipedia articles, where the answer contexts are guaranteed to exist within the provided paragraph  It contains annotation of answer phrases as well as the pointers to the sentences including the answer phrases; thus, it can be used for both answer extraction and selection  This corpus also provides human accuracy on those questions, setting up a reasonable upper bound for machines  To avoid overfitting, the evaluation set is not publicly available although system outputs can be evaluated by their provided script 
InfoboxQA BIBREF13 gives 15K+ questions based on the infoboxes from 150 articles in Wikipedia  Each question is crowdsourced and associated with an infobox, where each line of the infobox is considered an answer candidate  This corpus emphasizes the gravity of infoboxes, which summary arguably the most commonly asked information about those articles  Although the nature of this corpus is different from the others, it can also be used to evaluate answer selection 
Analysis
All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively  SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times  On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts  Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes 
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate  SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes  It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries  InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes  InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively  In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close 
Fig  FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons  Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on 
Fig  FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a  Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models 
Answer Retrieval
This section describes another selection-based QA task, called answer retrieval, that finds the answer context from a larger dataset, the entire Wikipedia  SQuAD provides no mapping of the answer contexts to Wikipedia, whereas WikiQA and SelQA provide mappings; however, their data do not come from the same version of Wikipedia  We propose an automatic way of mapping the answer contexts from all corpora to the same version of Wikipeda so they can be coherently used for answer retrieval 
Each paragraph in Wikipedia is first indexed by Lucene using {1,2,3}-grams, where the paragraphs are separated by WikiExtractor and segmented by NLP4J (28 7M+ paragraphs are indexed)  Each answer sentence from the corpora in Table TABREF3 is then queried to Lucene, and the top-5 ranked paragraphs are retrieved  The cosine similarity between each sentence in these paragraphs and the answer sentence is measured for INLINEFORM0 -grams, say INLINEFORM1   A weight is assigned to each INLINEFORM2 -gram score, say INLINEFORM3 , and the weighted sum is measured: INLINEFORM4   The fixed weights of INLINEFORM5 are used for our experiments, which can be improved 
If there exists a sentence whose INLINEFORM0 , the paragraph consisting of that sentence is considered the silver-standard answer passage  Table TABREF3 shows how robust these silver-standard passages are based on human judgement ( INLINEFORM1 ) and how many passages are collected ( INLINEFORM2 ) for INLINEFORM3 , where the human judgement is performed on 50 random samples for each case  For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively  Finally, each question is queried to Lucene and the top- INLINEFORM7 paragraphs are retrieved from the entire Wikipedia  If the answer sentence exists within those retrieved paragraphs according to the silver-standard, it is considered correct 
Finding a paragraph that includes the answer context out of the entire Wikipedia is an extremely difficult task (128 7M)  The last row of Table TABREF3 shows results from answer retrieval  Given INLINEFORM0 , SelQA and SQuAD show about 34% and 35% accuracy, which are reasonable  However, WikiQA shows a significantly lower accuracy of 12 47%; this is because the questions in WikiQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search 
Answer Selection
Answer selection is evaluated by two metrics, mean average precision (MAP) and mean reciprocal rank (MRR)  The bigram CNN introduced by yu:14a is used to generate all the results in Table TABREF11 , where models are trained on either single or combined datasets  Clearly, the questions in WikiQA are the most challenging, and adding more training data from the other corpora hurts accuracy due to the uniqueness of query-based questions in this corpus  The best model is achieved by training on W+S+Q for SelQA; adding InfoboxQA hurts accuracy for SelQA although it gives a marginal gain for SQuAD  Just like WikiQA, InfoboxQA performs the best when it is trained on only itself  From our analysis, we suggest that to use models trained on WikiQA and InfoboxQA for short query-like questions, whereas to use ones trained on SelQA and SQuAD for long natural questions 
Answer Triggering
The results of INLINEFORM0 from the answer retrieval task in Section SECREF13 are used to create the datasets for answer triggering, where about 65% of the questions are not expected to find their answer contexts from the provided paragraphs for SelQA and SQuAD and 87 5% are not expected for WikiQA  Answer triggering is evaluated by the F1 scores as presented in Table TABREF11 , where three corpora are cross validated  The results on WikiQA are pretty low as expected from the poor accuracy on the answer retrieval task  Training on SelQA gives the best models for both WikiQA and SelQA  Training on SQuAD gives the best model for SQuAD although the model trained on SelQA is comparable  Since the answer triggering datasets are about 5 times larger than the answer selection datasets, it is computationally too expensive to combine all data for training  We plan to find a strong machine to perform this experiment in near future 
Related work
Lately, several deep learning approaches have been proposed for question answering  yu:14a presented a CNN model that recognizes the semantic similarity between two sentences  wang-nyberg:2015:ACL-IJCNLP presented a stacked bidirectional LSTM approach to read words in sequence, then outputs their similarity scores  feng:15a applied a general deep learning framework to non-factoid question answering  santos:16a introduced an attentive pooling mechanism that led to further improvements in selection-based QA 
Conclusion
We present a comprehensive comparison study of the existing corpora for selection-based question answering  Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora  Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning  Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available  In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters 
Context 2: Introduction
Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0  Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods 
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge  The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4  Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6 
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma 
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10  Recent neural-based methods are devoted to dealing with this problem  BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer  BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words  However, neither of them can totally beat the best word expert supervised methods 
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network  Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss  Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge 
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system  Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering  Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI)  We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task  In particular, our contribution is two-fold:
1  We construct context-gloss pairs and propose three BERT-based models for WSD 
2  We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems 
Methodology
In this section, we describe our method in detail 
Methodology ::: Task Definition
In WSD, a sentence $s$ usually consists of a series of words: $\lbrace w_1,\cdots ,w_m\rbrace $, and some of the words $\lbrace w_{i_1},\cdots ,w_{i_k}\rbrace $ are targets $\lbrace t_1,\cdots ,t_k\rbrace $ need to be disambiguated  For each target $t$, its candidate senses $\lbrace c_1,\cdots ,c_n\rbrace $ come from entries of its lemma in a pre-defined sense inventory (usually WordNet)  Therefore, WSD task aims to find the most suitable entry (symbolized as unique sense key) for each target in a sentence  See a sentence example in Table TABREF1 
Methodology ::: BERT
BERT BIBREF15 is a new language representation model, and its architecture is a multi-layer bidirectional Transformer encoder  BERT model is pre-trained on a large corpus and two novel unsupervised prediction tasks, i e , masked language model and next sentence prediction tasks are used in pre-training  When incorporating BERT into downstream tasks, the fine-tuning procedure is recommended  We fine-tune the pre-trained BERT model on WSD task 
Methodology ::: BERT ::: BERT(Token-CLS)
Since every target in a sentence needs to be disambiguated to find its exact sense, WSD task can be regarded as a token-level classification task  To incorporate BERT to WSD task, we take the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer for every target lemma, which is the same as the last layer of the Bi-LSTM model BIBREF11 
Methodology ::: GlossBERT
BERT can explicitly model the relationship of a pair of texts, which has shown to be beneficial to many pair-wise natural language understanding tasks  In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem 
We describe our construction method with an example (See Table TABREF1)  There are four targets in this sentence, and here we take target word research as an example:
Methodology ::: GlossBERT ::: Context-Gloss Pairs
The sentence containing target words is denoted as context sentence  For each target word, we extract glosses of all $N$ possible senses (here $N=4$) of the target word (research) in WordNet to obtain the gloss sentence  [CLS] and [SEP] marks are added to the context-gloss pairs to make it suitable for the input of BERT model  A similar idea is also used in aspect-based sentiment analysis BIBREF16 
Methodology ::: GlossBERT ::: Context-Gloss Pairs with Weak Supervision
Based on the previous construction method, we add weak supervised signals to the context-gloss pairs (see the highlighted part in Table TABREF1)  The signal in the gloss sentence aims to point out the target word, and the signal in the context sentence aims to emphasize the target word considering the situation that a target word may occur more than one time in the same sentence 
Therefore, each target word has $N$ context-gloss pair training instances ($label\in \lbrace yes, no\rbrace $)  When testing, we output the probability of $label=yes$ of each context-gloss pair and choose the sense corresponding to the highest probability as the prediction label of the target word  We experiment with three GlossBERT models:
Methodology ::: GlossBERT ::: GlossBERT(Token-CLS)
We use context-gloss pairs as input  We highlight the target word by taking the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer ($label\in \lbrace yes, no\rbrace $) 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS)
We use context-gloss pairs as input  We take the final hidden state of the first token [CLS] as the representation of the whole sequence and add a classification layer ($label\in \lbrace yes, no\rbrace $), which does not highlight the target word 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS-WS)
We use context-gloss pairs with weak supervision as input  We take the final hidden state of the first token [CLS] and add a classification layer ($label\in \lbrace yes, no\rbrace $), which weekly highlight the target word by the weak supervision

ground_truth: Yes

No

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

Please answer the questions based on the text provided below. Is SemCor3.0 reflective of English language data in general?
Context 2: 
Experiments ::: Datasets
The statistics of the WSD datasets are shown in Table TABREF12 
Experiments ::: Datasets ::: Training Dataset
Following previous work BIBREF13, BIBREF12, BIBREF10, BIBREF17, BIBREF9, BIBREF7, we choose SemCor3 0 as training corpus, which is the largest corpus manually annotated with WordNet sense for WSD 
Experiments ::: Datasets ::: Evaluation Datasets
We evaluate our method on several English all-words WSD datasets  For a fair comparison, we use the benchmark datasets proposed by BIBREF17 which include five standard all-words fine-grained WSD datasets from the Senseval and SemEval competitions: Senseval-2 (SE2), Senseval-3 (SE3), SemEval-2007 (SE07), SemEval-2013 (SE13) and SemEval-2015 (SE15)  Following BIBREF13, BIBREF12 and BIBREF10, we choose SE07, the smallest among these test sets, as the development set 
Experiments ::: Datasets ::: WordNet
Since BIBREF17 map all the sense annotations in these datasets from their original versions to WordNet 3 0, we extract word sense glosses from WordNet 3 0 
Experiments ::: Settings
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task  The number of Transformer blocks is 12, the number of the hidden layer is 768, the number of self-attention heads is 12, and the total number of parameters of the pre-trained model is 110M  When fine-tuning, we use the development set (SE07) to find the optimal settings for our experiments  We keep the dropout probability at 0 1, set the number of epochs to 4  The initial learning rate is 2e-5, and the batch size is 64 
Experiments ::: Results
Table TABREF19 shows the performance of our method on the English all-words WSD benchmark datasets  We compare our approach with previous methods 
The first block shows the MFS baseline, which selects the most frequent sense in the training corpus for each target word 
The second block shows two knowledge-based systems  Lesk$_{ext+emb}$ BIBREF4 is a variant of Lesk algorithm BIBREF2 by calculating the gloss-context overlap of the target word  Babelfy BIBREF6 is a unified graph-based approach which exploits the semantic network structure from BabelNet 
The third block shows two word expert traditional supervised systems  IMS BIBREF7 is a flexible framework which trains SVM classifiers and uses local features  And IMS$_{+emb}$ BIBREF9 is the best configuration of the IMS framework, which also integrates word embeddings as features 
The fourth block shows several recent neural-based methods  Bi-LSTM BIBREF11 is a baseline for neural models  Bi-LSTM$_{+ att  + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task  GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge  CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge 
In the last block, we report the performance of our method  BERT(Token-CLS) is our baseline, which does not incorporate gloss information, and it performs slightly worse than previous traditional supervised methods and recent neural-based methods  It proves that directly using BERT cannot obtain performance growth  The other three methods outperform other models by a substantial margin, which proves that the improvements come from leveraging BERT to better exploit gloss information  It is worth noting that our method achieves significant improvements in SE07 and Verb over previous methods, which have the highest ambiguity level among all datasets and all POS tags respectively according to BIBREF17 
Moreover, GlossBERT(Token-CLS) performs better than GlossBERT(Sent-CLS), which proves that highlighting the target word in the sentence is important  However, the weakly highlighting method GlossBERT(Sent-CLS-WS) performs best in most circumstances, which may result from its combination of the advantages of the other two methods 
Experiments ::: Discussion
There are two main reasons for the great improvements of our experimental results  First, we construct context-gloss pairs and convert WSD problem to a sentence-pair classification task which is similar to NLI tasks and train only one classifier, which is equivalent to expanding the corpus  Second, we leverage BERT BIBREF15 to better exploit the gloss information  BERT model shows its advantage in dealing with sentence-pair classification tasks by its amazing improvement on QA and NLI tasks  This advantage comes from both of its two novel unsupervised prediction tasks 
Compared with traditional word expert supervised methods, our GlossBERT shows its effectiveness to alleviate the effort of feature engineering and does not require training a dedicated classifier for every target lemma  Up to now, it can be said that the neural network method can totally beat the traditional word expert method  Compared with recent neural-based methods, our solution is more intuitive and can make better use of gloss knowledge  Besides, our approach demonstrates that when we fine-tune BERT on a downstream task, converting it into a sentence-pair classification task may be a good choice 
Conclusion
In this paper, we seek to better leverage gloss knowledge in a supervised neural WSD system  We propose a new solution to WSD by constructing context-gloss pairs and then converting WSD to a sentence-pair classification task  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task 
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments  The research work is supported by National Natural Science Foundation of China (No  61751201 and 61672162), Shanghai Municipal Science and Technology Commission (16JC1420401 and 17JC1404100), Shanghai Municipal Science and Technology Major Project (No 2018SHZDZX01) and ZJLab 
Context 3: Introduction
This work is licensed under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/
Entity linking (EL), mapping entity mentions in texts to a given knowledge base (KB), serves as a fundamental role in many fields, such as question answering BIBREF0 , semantic search BIBREF1 , and information extraction BIBREF2 , BIBREF3   However, this task is non-trivial because entity mentions are usually ambiguous  As shown in Figure FIGREF1 , the mention England refers to three entities in KB, and an entity linking system should be capable of identifying the correct entity as England cricket team rather than England and England national football team 
Entity linking is typically broken down into two main phases: (i) candidate generation obtains a set of referent entities in KB for each mention, and (ii) named entity disambiguation selects the possible candidate entity by solving a ranking problem  The key challenge lies in the ranking model that computes the relevance between candidates and the corresponding mentions based on the information both in texts and KBs BIBREF4   In terms of the features used for ranking, we classify existing EL models into two groups: local models to resolve mentions independently relying on textual context information from the surrounding words BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , and global (collective) models, which are the main focus of this paper, that encourage the target entities of all mentions in a document to be topically coherent BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14  
Global models usually build an entity graph based on KBs to capture coherent entities for all identified mentions in a document, where the nodes are entities, and edges denote their relations  The graph provides highly discriminative semantic signals (e g , entity relatedness) that are unavailable to local model BIBREF15   For example (Figure FIGREF1 ), an EL model seemly cannot find sufficient disambiguation clues for the mention England from its surrounding words, unless it utilizes the coherence information of consistent topic “cricket" among adjacent mentions England, Hussain, and Essex  Although the global model has achieved significant improvements, its limitation is threefold:
To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability  In such models, words/entities are represented by low dimensional vectors in a continuous space, and features for mention as well as candidate entities are automatically learned from data BIBREF4   However, existing NN-based methods for EL are either local models BIBREF16 , BIBREF17 or merely use word/entity embeddings for feature extraction and rely on another modules for collective disambiguation, which thus cannot fully utilize the power of NN models for collective EL BIBREF18 , BIBREF19 , BIBREF20  
The second drawback of the global approach has been alleviated through approximate optimization techniques, such as PageRank/random walks BIBREF21 , graph pruning BIBREF22 , ranking SVMs BIBREF23 , or loopy belief propagation (LBP) BIBREF18 , BIBREF24   However, these methods are not differentiable and thus difficult to be integrated into neural network models (the solution for the first limitation) 
To overcome the third issue of inadequate training data, BIBREF17 has explored a massive amount of hyperlinks in Wikipedia, but these potential annotations for EL contain much noise, which may distract a naive disambiguation model BIBREF6  
In this paper, we propose a novel Neural Collective Entity Linking model (NCEL), which performs global EL combining deep neural networks with Graph Convolutional Network (GCN) BIBREF25 , BIBREF26 that allows flexible encoding of entity graphs  It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion  Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise  On the other hand, we apply GCNs to improve discriminative signals of candidate entities by exploiting the rich structure underlying the correct entities  To alleviate the global computations, we propose to convolute on the subgraph of adjacent mentions  Thus, the overall coherence shall be achieved in a chain-like way via a sliding window over the document  To the best of our knowledge, this is the first effort to develop a unified model for neural collective entity linking 
In experiments, we first verify the efficiency of NCEL via theoretically comparing its time complexity with other collective alternatives  Afterwards, we train our neural model using collected Wikipedia hyperlinks instead of dataset-specific annotations, and perform evaluations on five public available benchmarks  The results show that NCEL consistently outperforms various baselines with a favorable generalization ability  Finally, we further present the performance on a challenging dataset WW BIBREF19 as well as qualitative results, investigating the effectiveness of each key module 
Preliminaries and Framework
We denote INLINEFORM0 as a set of entity mentions in a document INLINEFORM1 , where INLINEFORM2 is either a word INLINEFORM3 or a mention INLINEFORM4   INLINEFORM5 is the entity graph for document INLINEFORM6 derived from the given knowledge base, where INLINEFORM7 is a set of entities, INLINEFORM8 denotes the relatedness between INLINEFORM9 and higher values indicate stronger relations  Based on INLINEFORM10 , we extract a subgraph INLINEFORM11 for INLINEFORM12 , where INLINEFORM13 denotes the set of candidate entities for INLINEFORM14   Note that we don't include the relations among candidates of the same mention in INLINEFORM15 because these candidates are mutually exclusive in disambiguation 
Formally, we define the entity linking problem as follows: Given a set of mentions INLINEFORM0 in a document INLINEFORM1 , and an entity graph INLINEFORM2 , the goal is to find an assignment INLINEFORM3  
To collectively find the best assignment, NCEL aims to improve the discriminability of candidates' local features by using entity relatedness within a document via GCN, which is capable of learning a function of features on the graph through shared parameters over all nodes  Figure FIGREF10 shows the framework of NCEL including three main components:
Example As shown in Figure FIGREF10 , for the current mention England, we utilize its surrounding words as local contexts (e g , surplus), and adjacent mentions (e g , Hussian) as global information  Collectively, we utilize the candidates of England INLINEFORM0 as well as those entities of its adjacencies INLINEFORM1 to construct feature vectors for INLINEFORM2 and the subgraph of relatedness as inputs of our neural model  Let darker blue indicate higher probability of being predicted, the correct candidate INLINEFORM3 becomes bluer due to its bluer neighbor nodes of other mentions INLINEFORM4   The dashed lines denote entity relations that have indirect impacts through the sliding adjacent window , and the overall structure shall be achieved via multiple sub-graphs by traversing all mentions 
Before introducing our model, we first describe the component of candidate generation 
Candidate Generation
Similar to previous work BIBREF24 , we use the prior probability INLINEFORM0 of entity INLINEFORM1 conditioned on mention INLINEFORM2 both as a local feature and to generate candidate entities: INLINEFORM3   We compute INLINEFORM4 based on statistics of mention-entity pairs from: (i) Wikipedia page titles, redirect titles and hyperlinks, (ii) the dictionary derived from a large Web Corpus BIBREF27 , and (iii) the YAGO dictionary with a uniform distribution BIBREF22   We pick up the maximal prior if a mention-entity pair occurs in different resources  In experiments, to optimize for memory and run time, we keep only top INLINEFORM5 entities based on INLINEFORM6   In the following two sections, we will present the key components of NECL, namely feature extraction and neural network for collective entity linking 
Feature Extraction
The main goal of NCEL is to find a solution for collective entity linking using an end-to-end neural model, rather than to improve the measurements of local textual similarity or global mention/entity relatedness  Therefore, we use joint embeddings of words and entities at sense level BIBREF28 to represent mentions and its contexts for feature extraction  In this section, we give a brief description of our embeddings followed by our features used in the neural model 
Learning Joint Embeddings of Word and Entity
Following BIBREF28 , we use Wikipedia articles, hyperlinks, and entity outlinks to jointly learn word/mention and entity embeddings in a unified vector space, so that similar words/mentions and entities have similar vectors  To address the ambiguity of words/mentions, BIBREF28 represents each word/mention with multiple vectors, and each vector denotes a sense referring to an entity in KB  The quality of the embeddings is verified on both textual similarity and entity relatedness tasks 
Formally, each word/mention has a global embedding INLINEFORM0 , and multiple sense embeddings INLINEFORM1   Each sense embedding INLINEFORM2 refers to an entity embedding INLINEFORM3 , while the difference between INLINEFORM4 and INLINEFORM5 is that INLINEFORM6 models the co-occurrence information of an entity in texts (via hyperlinks) and INLINEFORM7 encodes the structured entity relations in KBs  More details can be found in the original paper 
Local Features
Local features focus on how compatible the entity is mentioned in a piece of text (i e , the mention and the context words)  Except for the prior probability (Section SECREF9 ), we define two types of local features for each candidate entity INLINEFORM0 :
String Similarity Similar to BIBREF16 , we define string based features as follows: the edit distance between mention's surface form and entity title, and boolean features indicating whether they are equivalent, whether the mention is inside, starts with or ends with entity title and vice versa 
Compatibility We also measure the compatibility of INLINEFORM0 with the mention's context words INLINEFORM1 by computing their similarities based on joint embeddings: INLINEFORM2 and INLINEFORM3 , where INLINEFORM4 is the context embedding of INLINEFORM5 conditioned on candidate INLINEFORM6 and is defined as the average sum of word global vectors weighted by attentions: INLINEFORM7
where INLINEFORM0 is the INLINEFORM1 -th word's attention from INLINEFORM2   In this way, we automatically select informative words by assigning higher attention weights, and filter out irrelevant noise through small weights  The attention INLINEFORM3 is computed as follows: INLINEFORM4
where INLINEFORM0 is the similarity measurement, and we use cosine similarity in the presented work  We concatenate the prior probability, string based similarities, compatibility similarities and the embeddings of contexts as well as the entity as the local feature vectors 
Global Features
The key idea of collective EL is to utilize the topical coherence throughout the entire document  The consistency assumption behind it is that: all mentions in a document shall be on the same topic  However, this leads to exhaustive computations if the number of mentions is large  Based on the observation that the consistency attenuates along with the distance between two mentions, we argue that the adjacent mentions might be sufficient for supporting the assumption efficiently 
Formally, we define neighbor mentions as INLINEFORM0 adjacent mentions before and after current mention INLINEFORM1 : INLINEFORM2 , where INLINEFORM3 is the pre-defined window size  Thus, the topical coherence at document level shall be achieved in a chain-like way  As shown in Figure FIGREF10 ( INLINEFORM4 ), mentions Hussain and Essex, a cricket player and the cricket club, provide adequate disambiguation clues to induce the underlying topic “cricket" for the current mention England, which impacts positively on identifying the mention surrey as another cricket club via the common neighbor mention Essex 
A degraded case happens if INLINEFORM0 is large enough to cover the entire document, and the mentions used for global features become the same as the previous work, such as BIBREF21   In experiments, we heuristically found a suitable INLINEFORM1 which is much smaller than the total number of mentions  The benefits of efficiency are in two ways: (i) to decrease time complexity, and (ii) to trim the entity graph into a fixed size of subgraph that facilitates computation acceleration through GPUs and batch techniques, which will be discussed in Section SECREF24  
Given neighbor mentions INLINEFORM0 , we extract two types of vectorial global features and structured global features for each candidate INLINEFORM1 :
Neighbor Mention Compatibility Suppose neighbor mentions are topical coherent, a candidate entity shall also be compatible with neighbor mentions if it has a high compatibility score with the current mention, otherwise not  That is, we extract the vectorial global features by computing the similarities between INLINEFORM0 and all neighbor mentions: INLINEFORM1 , where INLINEFORM2 is the mention embedding by averaging the global vectors of words in its surface form: INLINEFORM3 , where INLINEFORM4 are tokenized words of mention INLINEFORM5  
Subgraph Structure The above features reflect the consistent semantics in texts (i e , mentions)  We now extract structured global features using the relations in KB, which facilitates the inference among candidates to find the most topical coherent subset

ground_truth: Yes

Unanswerable

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

Please answer the questions based on the text provided below. How effective is their NCEL approach overall?
Context 3:  For each document, we obtain the entity graph INLINEFORM0 by taking candidate entities of all mentions INLINEFORM1 as nodes, and using entity embeddings to compute their similarities as edges INLINEFORM2   Then, we extract the subgraph structured features INLINEFORM3 for each entity INLINEFORM4 for efficiency 
Formally, we define the subgraph as: INLINEFORM0 , where INLINEFORM1   For example (Figure FIGREF1 ), for entity England cricket team, the subgraph contains the relation from it to all candidates of neighbor mentions: England cricket team, Nasser Hussain (rugby union), Nasser Hussain, Essex, Essex County Cricket Club and Essex, New York  To support batch-wise acceleration, we represent INLINEFORM2 in the form of adjacency table based vectors: INLINEFORM3 , where INLINEFORM4 is the number of candidates per mention 
Finally, for each candidate INLINEFORM0 , we concatenate local features and neighbor mention compatibility scores as the feature vector INLINEFORM1 , and construct the subgraph structure representation INLINEFORM2 as the inputs of NCEL 
Neural Collective Entity Linking
NCEL incorporates GCN into a deep neural network to utilize structured graph information for collectively feature abstraction, while differs from conventional GCN in the way of applying the graph  Instead of the entire graph, only a subset of nodes is “visible" to each node in our proposed method, and then the overall structured information shall be reached in a chain-like way  Fixing the size of the subset, NCEL is further speeded up by batch techniques and GPUs, and is efficient to large-scale data 
Graph Convolutional Network
GCNs are a type of neural network model that deals with structured data  It takes a graph as an input and output labels for each node  As a simplification of spectral graph convolutions, the main idea of BIBREF26 is similar to a propagation model: to enhance the features of a node according to its neighbor nodes  The formulation is as follows: INLINEFORM0
where INLINEFORM0 is a normalized adjacent matrix of the input graph with self-connection, INLINEFORM1 and INLINEFORM2 are the hidden states and weights in the INLINEFORM3 -th layer, and INLINEFORM4 is a non-linear activation, such as ReLu 
Model Architecture
As shown in Figure FIGREF10 , NCEL identifies the correct candidate INLINEFORM0 for the mention INLINEFORM1 by using vectorial features as well as structured relatedness with candidates of neighbor mentions INLINEFORM2   Given feature vector INLINEFORM3 and subgraph representation INLINEFORM4 of each candidate INLINEFORM5 , we stack them as inputs for mention INLINEFORM6 : INLINEFORM7 , and the adjacent matrix INLINEFORM8 , where INLINEFORM9 denotes the subgraph with self-connection  We normalize INLINEFORM10 such that all rows sum to one, denoted as INLINEFORM11 , avoiding the change in the scale of the feature vectors 
Given INLINEFORM0 and INLINEFORM1 , the goal of NCEL is to find the best assignment: INLINEFORM2
where INLINEFORM0 is the output variable of candidates, and INLINEFORM1 is a probability function as follows: INLINEFORM2
where INLINEFORM0 is the score function parameters by INLINEFORM1   NCEL learns the mapping INLINEFORM2 through a neural network including three main modules: encoder, sub-graph convolution network (sub-GCN) and decoder  Next, we introduce them in turn 
Encoder The function of this module is to integrate different features by a multi-layer perceptron (MLP): INLINEFORM0
where INLINEFORM0 is the hidden states of the current mention, INLINEFORM1 and INLINEFORM2 are trainable parameters and bias  We use ReLu as the non-linear activation INLINEFORM3  
Sub-Graph Convolution Network Similar to GCN, this module learns to abstract features from the hidden state of the mention itself as well as its neighbors  Suppose INLINEFORM0 is the hidden states of the neighbor INLINEFORM1 , we stack them to expand the current hidden states of INLINEFORM2 as INLINEFORM3 , such that each row corresponds to that in the subgraph adjacent matrix INLINEFORM4   We define sub-graph convolution as: INLINEFORM5
where INLINEFORM0 is a trainable parameter 
Decoder After INLINEFORM0 iterations of sub-graph convolution, the hidden states integrate both features of INLINEFORM1 and its neighbors  A fully connected decoder maps INLINEFORM2 to the number of candidates as follows: INLINEFORM3
where INLINEFORM0  
Training
The parameters of network are trained to minimize cross-entropy of the predicted and ground truth INLINEFORM0 : INLINEFORM1
Suppose there are INLINEFORM0 documents in training corpus, each document has a set of mentions INLINEFORM1 , leading to totally INLINEFORM2 mention sets  The overall objective function is as follows: INLINEFORM3
Experiments
To avoid overfitting with some dataset, we train NCEL using collected Wikipedia hyperlinks instead of specific annotated data  We then evaluate the trained model on five different benchmarks to verify the linking precision as well as the generalization ability  Furthermore, we investigate the effectiveness of key modules in NCEL and give qualitative results for comprehensive analysis 
Baselines and Datasets
We compare NCEL with the following state-of-the-art EL methods including three local models and three types of global models:
Local models: He BIBREF29 and Chisholm BIBREF6 beat many global models by using auto-encoders and web links, respectively, and NTEE BIBREF16 achieves the best performance based on joint embeddings of words and entities 
Iterative model: AIDA BIBREF22 links entities by iteratively finding a dense subgraph 
Loopy Belief Propagation: Globerson BIBREF18 and PBoH BIBREF30 introduce LBP BIBREF31 techniques for collective inference, and Ganea BIBREF24 solves the global training problem via truncated fitting LBP 
PageRank/Random Walk: Boosting BIBREF32 , AGDISTISG BIBREF33 , Babelfy BIBREF34 , WAT BIBREF35 , xLisa BIBREF36 and WNED BIBREF19 performs PageRank BIBREF37 or random walk BIBREF38 on the mention-entity graph and use the convergence score for disambiguation 
For fairly comparison, we report the original scores of the baselines in the papers  Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents  (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing  (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk  (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies  (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation  It has 6374 mentions in 310 documents automatically extracted from Wikipedia 
Training Details and Running Time Analysis
Training We collect 50,000 Wikipedia articles according to the number of its hyperlinks as our training data  For efficiency, we trim the articles to the first three paragraphs leading to 1,035,665 mentions in total  Using CoNLL-Test A as the development set, we evaluate the trained NCEL on the above benchmarks  We set context window to 20, neighbor mention window to 6, and top INLINEFORM0 candidates for each mention  We use two layers with 2000 and 1 hidden units in MLP encoder, and 3 layers in sub-GCN  We use early stop and fine tune the embeddings  With a batch size of 16, nearly 3 epochs cost less than 15 minutes on the server with 20 core CPU and the GeForce GTX 1080Ti GPU with 12Gb memory  We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements 
Complexity Analysis Compared with local methods, the main disadvantage of collective methods is high complexity and expensive costs  Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence  AIDA has the highest time complexity INLINEFORM3 in worst case due to exhaustive iteratively finding and sorting the graph  The LBP and PageRank/random walk based methods achieve similar high time complexity of INLINEFORM4 mainly because of the inference on the entire graph 
Results on GERBIL
GERBIL BIBREF41 is a benchmark entity annotation framework that aims to provide a unified comparison among different EL methods across datasets including ACE2004, AQUAINT and CoNLL  We compare NCEL with the global models that report the performance on GERBIL 
As shown in Table TABREF26 , NCEL achieves the best performance in most cases with an average gain of 2% on Micro F1 and 3% Macro F1  The baseline methods also achieve competitive results on some datasets but fail to adapt to the others  For example, AIDA and xLisa perform quite well on ACE2004 but poorly on other datasets, or WAT, PBoH, and WNED have a favorable performance on CoNLL but lower values on ACE2004 and AQUAINT  Our proposed method performs consistently well on all datasets that demonstrates the good generalization ability 
Results on TAC2010 and WW
In this section, we investigate the effectiveness of NCEL in the “easy" and “hard" datasets, respectively  Particularly, TAC2010, which has two mentions per document on average (Section SECREF19 ) and high prior probabilities of correct candidates (Figure FIGREF28 ), is regarded as the “easy" case for EL, and WW is the “hard" case since it has the most mentions with balanced prior probabilities BIBREF19   Besides, we further compare the impact of key modules by removing the following part from NCEL: global features (NCEL-local), attention (NCEL-noatt), embedding features (NCEL-noemb), and the impact of the prior probability (prior) 
The results are shown in Table FIGREF28 and Table FIGREF28   We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases  In the “easy" case, local models have similar performance with global models since only little global information is available (2 mentions per document)  Besides, NN-based models, NTEE and NCEL-local, perform significantly better than others including most global models, demonstrating that the effectiveness of neural models deals with the first limitation in the introduction 
Impact of NCEL Modules
As shown in Figure FIGREF28 , the prior probability performs quite well in TAC2010 but poorly in WW  Compared with NCEL-local, the global module in NCEL brings more improvements in the “hard" case than that for “easy" dataset, because local features are discriminative enough in most cases of TAC2010, and global information becomes quite helpful when local features cannot handle  That is, our propose collective model is robust and shows a good generalization ability to difficult EL  The improvements by each main module are relatively small in TAC2010, while the modules of attention and embedding features show non-negligible impacts in WW (even worse than local model), mainly because WW contains much noise, and these two modules are effective in improving the robustness to noise and the ability of generalization by selecting informative words and providing more accurate semantics, respectively 
Qualitative Analysis
The results of example in Figure FIGREF1 are shown in Table TABREF30 , which is from CoNLL testa dataset  For mention Essex, although both NCEL and NCEL-local correctly identify entity Essex County Cricket Club, NCEL outputs higher probability due to the enhancement of neighbor mentions  Moreover, for mention England, NCEL-local cannot find enough disambiguation clues from its context words, such as surplus and requirements, and thus assigns a higher probability of 0 42 to the country England according to the prior probability  Collectively, NCEL correctly identifies England cricket team with a probability of 0 72 as compared with 0 20 in NCEL-local with the help of its neighbor mention Essex 
Conclusion
In this paper, we propose a neural model for collective entity linking that is end-to-end trainable  It applies GCN on subgraphs instead of the entire entity graph to efficiently learn features from both local and global information  We design an attention mechanism that endows NCEL robust to noisy data  Trained on collected Wikipedia hyperlinks, NCEL outperforms the state-of-the-art collective methods across five different datasets  Besides, further analysis of the impacts of main modules as well as qualitative results demonstrates its effectiveness 
In the future, we will extend our method into cross-lingual settings to help link entities in low-resourced languages by exploiting rich knowledge from high-resourced languages, and deal with NIL entities to facilitate specific applications 
Acknowledgments
The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007), and THUNUS NExT Co-Lab 

ground_truth: NCEL consistently outperforms various baselines with a favorable generalization ability