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 sentiment analysis dataset is used?
Context 1: Introduction
There have been many implementations of the word2vec model in either of the two architectures it provides: continuous skipgram and CBoW (BIBREF0)  Similar distributed models of word or subword embeddings (or vector representations) find usage in sota, deep neural networks like BERT and its successors (BIBREF1, BIBREF2, BIBREF3)  These deep networks generate contextual representations of words after been trained for extended periods on large corpora, unsupervised, using the attention mechanisms (BIBREF4) 
It has been observed that various hyper-parameter combinations have been used in different research involving word2vec with the possibility of many of them being sub-optimal (BIBREF5, BIBREF6, BIBREF7)  Therefore, the authors seek to address the research question: what is the optimal combination of word2vec hyper-parameters for intrinsic and extrinsic NLP purposes? There are astronomically high numbers of combinations of hyper-parameters possible for neural networks, even with just a few layers  Hence, the scope of our extensive work over three corpora is on dimension size, training epochs, window size and vocabulary size for the training algorithms (hierarchical softmax and negative sampling) of both skipgram and CBoW  The corpora used for word embeddings are English Wiki News Abstract by BIBREF8 of about 15MB, English Wiki Simple (SW) Articles by BIBREF9 of about 711MB and the Billion Word (BW) of 3 9GB by BIBREF10  The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples  The IMDb dataset used has a total of 25,000 sentences with half being positive sentiments and the other half being negative sentiments  The GMB dataset has 17 labels, with 9 main labels and 2 context tags  It is however unbalanced due to the high percentage of tokens with the label 'O'  This skew in the GMB dataset is typical with NER datasets 
The objective of this work is to determine the optimal combinations of word2vec hyper-parameters for intrinsic evaluation (semantic and syntactic analogies) and extrinsic evaluation tasks (BIBREF13, BIBREF14), like SA and NER  It is not our objective in this work to record sota results  Some of the main contributions of this research are the empirical establishment of optimal combinations of word2vec hyper-parameters for NLP tasks, discovering the behaviour of quality of vectors viz-a-viz increasing dimensions and the confirmation of embeddings being task-specific for the downstream  The rest of this paper is organised as follows: the literature review that briefly surveys distributed representation of words, particularly word2vec; the methodology employed in this research work; the results obtained and the conclusion 
Literature Review
Breaking away from the non-distributed (high-dimensional, sparse) representations of words, typical of traditional bag-of-words or one-hot-encoding (BIBREF15), BIBREF0 created word2vec  Word2Vec consists of two shallow neural network architectures: continuous skipgram and CBoW  It uses distributed (low-dimensional, dense) representations of words that group similar words  This new model traded the complexity of deep neural network architectures, by other researchers, for more efficient training over large corpora  Its architectures have two training algorithms: negative sampling and hierarchical softmax (BIBREF16)  The released model was trained on Google news dataset of 100 billion words  Implementations of the model have been undertaken by researchers in the programming languages Python and C++, though the original was done in C (BIBREF17) 
Continuous skipgram predicts (by maximizing classification of) words before and after the center word, for a given range  Since distant words are less connected to a center word in a sentence, less weight is assigned to such distant words in training  CBoW, on the other hand, uses words from the history and future in a sequence, with the objective of correctly classifying the target word in the middle  It works by projecting all history or future words within a chosen window into the same position, averaging their vectors  Hence, the order of words in the history or future does not influence the averaged vector  This is similar to the traditional bag-of-words, which is oblivious of the order of words in its sequence  A log-linear classifier is used in both architectures (BIBREF0)  In further work, they extended the model to be able to do phrase representations and subsample frequent words (BIBREF16)  Being a NNLM, word2vec assigns probabilities to words in a sequence, like other NNLMs such as feedforward networks or recurrent neural networks (BIBREF15)  Earlier models like latent dirichlet allocation (LDA) and latent semantic analysis (LSA) exist and effectively achieve low dimensional vectors by matrix factorization (BIBREF18, BIBREF19) 
It's been shown that word vectors are beneficial for NLP tasks (BIBREF15), such as sentiment analysis and named entity recognition  Besides, BIBREF0 showed with vector space algebra that relationships among words can be evaluated, expressing the quality of vectors produced from the model  The famous, semantic example: vector("King") - vector("Man") + vector("Woman") $\approx $ vector("Queen") can be verified using cosine distance  Another type of semantic meaning is the relationship between a capital city and its corresponding country  Syntactic relationship examples include plural verbs and past tense, among others  Combination of both syntactic and semantic analyses is possible and provided (totaling over 19,000 questions) as Google analogy test set by BIBREF0  WordSimilarity-353 test set is another analysis tool for word vectors (BIBREF20)  Unlike Google analogy score, which is based on vector space algebra, WordSimilarity is based on human expert-assigned semantic similarity on two sets of English word pairs  Both tools rank from 0 (totally dissimilar) to 1 (very much similar or exact, in Google analogy case) 
A typical artificial neural network (ANN) has very many hyper-parameters which may be tuned  Hyper-parameters are values which may be manually adjusted and include vector dimension size, type of algorithm and learning rate (BIBREF19)  BIBREF0 tried various hyper-parameters with both architectures of their model, ranging from 50 to 1,000 dimensions, 30,000 to 3,000,000 vocabulary sizes, 1 to 3 epochs, among others  In our work, we extended research to 3,000 dimensions  Different observations were noted from the many trials  They observed diminishing returns after a certain point, despite additional dimensions or larger, unstructured training data  However, quality increased when both dimensions and data size were increased together  Although BIBREF16 pointed out that choice of optimal hyper-parameter configurations depends on the NLP problem at hand, they identified the most important factors are architecture, dimension size, subsampling rate, and the window size  In addition, it has been observed that variables like size of datasets improve the quality of word vectors and, potentially, performance on downstream tasks (BIBREF21, BIBREF0) 
Methodology
The models were generated in a shared cluster running Ubuntu 16 with 32 CPUs of 32x Intel Xeon 4110 at 2 1GHz  Gensim (BIBREF17) python library implementation of word2vec was used with parallelization to utilize all 32 CPUs  The downstream experiments were run on a Tesla GPU on a shared DGX cluster running Ubuntu 18  Pytorch deep learning framework was used  Gensim was chosen because of its relative stability, popular support and to minimize the time required in writing and testing a new implementation in python from scratch 
To form the vocabulary, words occurring less than 5 times in the corpora were dropped, stop words removed using the natural language toolkit (NLTK) (BIBREF22) and data pre-processing carried out  Table TABREF2 describes most hyper-parameters explored for each dataset  In all, 80 runs (of about 160 minutes) were conducted for the 15MB Wiki Abstract dataset with 80 serialized models totaling 15 136GB while 80 runs (for over 320 hours) were conducted for the 711MB SW dataset, with 80 serialized models totaling over 145GB  Experiments for all combinations for 300 dimensions were conducted on the 3 9GB training set of the BW corpus and additional runs for other dimensions for the window 8 + skipgram + heirarchical softmax combination to verify the trend of quality of word vectors as dimensions are increased 
Google (semantic and syntactic) analogy tests and WordSimilarity-353 (with Spearman correlation) by BIBREF20 were chosen for intrinsic evaluations  They measure the quality of word vectors  The analogy scores are averages of both semantic and syntactic tests  NER and SA were chosen for extrinsic evaluations  The GMB dataset for NER was trained in an LSTM network, which had an embedding layer for input  The network diagram is shown in fig  FIGREF4  The IMDb dataset for SA was trained in a BiLSTM network, which also used an embedding layer for input  Its network diagram is given in fig  FIGREF4  It includes an additional hidden linear layer  Hyper-parameter details of the two networks for the downstream tasks are given in table TABREF3  The metrics for extrinsic evaluation include F1, precision, recall and accuracy scores  In both tasks, the default pytorch embedding was tested before being replaced by pre-trained embeddings released by BIBREF0 and ours  In each case, the dataset was shuffled before training and split in the ratio 70:15:15 for training, validation (dev) and test sets  Batch size of 64 was used  For each task, experiments for each embedding was conducted four times and an average value calculated and reported in the next section
Results and Discussion
Table TABREF5 summarizes key results from the intrinsic evaluations for 300 dimensions  Table TABREF6 reveals the training time (in hours) and average embedding loading time (in seconds) representative of the various models used  Tables TABREF11 and TABREF12 summarize key results for the extrinsic evaluations  Figures FIGREF7, FIGREF9, FIGREF10, FIGREF13 and FIGREF14 present line graph of the eight combinations for different dimension sizes for Simple Wiki, trend of Simple Wiki and Billion Word corpora over several dimension sizes, analogy score comparison for models across datasets, NER mean F1 scores on the GMB dataset and SA mean F1 scores on the IMDb dataset, respectively  Combination of the skipgram using hierarchical softmax and window size of 8 for 300 dimensions outperformed others in analogy scores for the Wiki Abstract  However, its results are so poor, because of the tiny file size, they're not worth reporting here  Hence, we'll focus on results from the Simple Wiki and Billion Word corpora 
Best combination changes when corpus size increases, as will be noticed from table TABREF5  In terms of analogy score, for 10 epochs, w8s0h0 performs best while w8s1h0 performs best in terms of WordSim and corresponding Spearman correlation  Meanwhile, increasing the corpus size to BW, w4s1h0 performs best in terms of analogy score while w8s1h0 maintains its position as the best in terms of WordSim and Spearman correlation  Besides considering quality metrics, it can be observed from table TABREF6 that comparative ratio of values between the models is not commensurate with the results in intrinsic or extrinsic values, especially when we consider the amount of time and energy spent, since more training time results in more energy consumption (BIBREF23) 
Information on the length of training time for the released Mikolov model is not readily available  However, it's interesting to note that their presumed best model, which was released is also s1h0  Its analogy score, which we tested and report, is confirmed in their paper  It beats our best models in only analogy score (even for Simple Wiki), performing worse in others  This is inspite of using a much bigger corpus of 3,000,000 vocabulary size and 100 billion words while Simple Wiki had vocabulary size of 367,811 and is 711MB  It is very likely our analogy scores will improve when we use a much larger corpus, as can be observed from table TABREF5, which involves just one billion words 
Although the two best combinations in analogy (w8s0h0 & w4s0h0) for SW, as shown in fig  FIGREF7, decreased only slightly compared to others with increasing dimensions, the increased training time and much larger serialized model size render any possible minimal score advantage over higher dimensions undesirable  As can be observed in fig  FIGREF9, from 100 dimensions, scores improve but start to drop after over 300 dimensions for SW and after over 400 dimensions for BW  More becomes worse! This trend is true for all combinations for all tests  Polynomial interpolation may be used to determine the optimal dimension in both corpora  Our models are available for confirmation and source codes are available on github 
With regards to NER, most pretrained embeddings outperformed the default pytorch embedding, with our BW w4s1h0 model (which is best in BW analogy score) performing best in F1 score and closely followed by BIBREF0 model  On the other hand, with regards to SA, pytorch embedding outperformed the pretrained embeddings but was closely followed by our SW w8s0h0 model (which also had the best SW analogy score)  BIBREF0 performed second worst of all, despite originating from a very huge corpus  The combinations w8s0h0 & w4s0h0 of SW performed reasonably well in both extrinsic tasks, just as the default pytorch embedding did 
Conclusion
This work analyses, empirically, optimal combinations of hyper-parameters for embeddings, specifically for word2vec  It further shows that for downstream tasks, like NER and SA, there's no silver bullet! However, some combinations show strong performance across tasks  Performance of embeddings is task-specific and high analogy scores do not necessarily correlate positively with performance on downstream tasks  This point on correlation is somewhat similar to results by BIBREF24 and BIBREF14  It was discovered that increasing dimension size depreciates performance after a point  If strong considerations of saving time, energy and the environment are made, then reasonably smaller corpora may suffice or even be better in some cases  The on-going drive by many researchers to use ever-growing data to train deep neural networks can benefit from the findings of this work  Indeed, hyper-parameter choices are very important in neural network systems (BIBREF19) 
Future work that may be investigated are performance of other architectures of word or sub-word embeddings, the performance and comparison of embeddings applied to languages other than English and how embeddings perform in other downstream tasks  In addition, since the actual reason for the changes in best model as corpus size increases is not clear, this will also be suitable for further research 
The work on this project is partially funded by Vinnova under the project number 2019-02996 "Språkmodeller för svenska myndigheter"
Acronyms

ground_truth: IMDb dataset of movie reviews

IMDb

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 embedding techniques are explored in the paper?
Context 2: Introduction
Measures of semantic similarity and relatedness quantify the degree to which two concepts are similar (e g , INLINEFORM0 – INLINEFORM1 ) or related (e g , INLINEFORM2 – INLINEFORM3 )  Semantic similarity can be viewed as a special case of semantic relatedness – to be similar is one of many ways that a pair of concepts may be related  The automated discovery of groups of semantically similar or related terms is critical to improving the retrieval BIBREF0 and clustering BIBREF1 of biomedical and clinical documents, and the development of biomedical terminologies and ontologies BIBREF2  
There is a long history in using distributional methods to discover semantic similarity and relatedness (e g , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 )  These methods are all based on the distributional hypothesis, which holds that two terms that are distributionally similar (i e , used in the same context) will also be semantically similar BIBREF7 , BIBREF8   Recently word embedding techniques such as word2vec BIBREF9 have become very popular  Despite the prominent role that neural networks play in many of these approaches, at their core they remain distributional techniques that typically start with a word by word co–occurrence matrix, much like many of the more traditional approaches 
However, despite these successes distributional methods do not perform well when data is very sparse (which is common)  One possible solution is to use second–order co–occurrence vectors BIBREF10 , BIBREF11   In this approach the similarity between two words is not strictly based on their co–occurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i e , second order co–occurrences)  This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13   However, while more robust in the face of sparsity, second–order methods can result in significant amounts of noise, where contextual information that is overly general is included and does not contribute to quantifying the semantic relatedness between the two concepts 
Our goal then is to discover methods that automatically reduce the amount of noise in a second–order co–occurrence vector  We achieve this by incorporating pairwise semantic similarity scores derived from a taxonomy into our second–order vectors, and then using these scores to select only the most semantically similar co–occurrences (thereby reducing noise) 
We evaluate our method on two datasets that have been annotated in multiple ways  One has been annotated for both similarity and relatedness, and the other has been annotated for relatedness by two different types of experts (medical doctors and medical coders)  Our results show that integrating second order co–occurrences with measures of semantic similarity increases correlation with our human reference standards  We also compare our result to a number of other studies which have applied various word embedding methods to the same reference standards we have used  We find that our method often performs at a comparable or higher level than these approaches  These results suggest that our methods of integrating semantic similarity and relatedness values have the potential to improve performance of purely distributional methods 
Similarity and Relatedness Measures
This section describes the similarity and relatedness measures we integrate in our second–order co–occurrence vectors  We use two taxonomies in this study, SNOMED–CT and MeSH  SNOMED–CT (Systematized Nomenclature of Medicine Clinical Terms) is a comprehensive clinical terminology created for the electronic representation of clinical health information  MeSH (Medical Subject Headings) is a taxonomy of biomedical terms developed for indexing biomedical journal articles 
We obtain SNOMED–CT and MeSH via the Unified Medical Language System (UMLS) Metathesaurus (version 2016AA)  The Metathesaurus contains approximately 2 million biomedical and clinical concepts from over 150 different terminologies that have been semi–automatically integrated into a single source  Concepts in the Metathesaurus are connected largely by two types of hierarchical relations: INLINEFORM0 / INLINEFORM1 (PAR/CHD) and INLINEFORM2 / INLINEFORM3 (RB/RN) 
Similarity Measures
Measures of semantic similarity can be classified into three broad categories : path–based, feature–based and information content (IC)  Path–based similarity measures use the structure of a taxonomy to measure similarity – concepts positioned close to each other are more similar than those further apart  Feature–based methods rely on set theoretic measures of overlap between features (union and intersection)  The information content measures quantify the amount of information that a concept provides – more specific concepts have a higher amount of information content 
RadaMBB89 introduce the Conceptual Distance measure  This measure is simply the length of the shortest path between two concepts ( INLINEFORM0 and INLINEFORM1 ) in the MeSH hierarchy  Paths are based on broader than (RB) and narrower than (RN) relations  CaviedesC04 extends this measure to use parent (PAR) and child (CHD) relations  Our INLINEFORM2 measure is simply the reciprocal of this shortest path value (Equation EQREF3 ), so that larger values (approaching 1) indicate a high degree of similarity  DISPLAYFORM0
While the simplicity of INLINEFORM0 is appealing, it can be misleading when concepts are at different levels of specificity  Two very general concepts may have the same path length as two very specific concepts  WuP94 introduce a correction to INLINEFORM1 that incorporates the depth of the concepts, and the depth of their Least Common Subsumer (LCS)  This is the most specific ancestor two concepts share  In this measure, similarity is twice the depth of the two concept's LCS divided by the product of the depths of the individual concepts (Equation EQREF4 )  Note that if there are multiple LCSs for a pair of concepts, the deepest of them is used in this measure  DISPLAYFORM0
ZhongZLY02 take a very similar approach and again scale the depth of the LCS by the sum of the depths of the two concepts (Equation EQREF5 ), where INLINEFORM0   The value of INLINEFORM1 was set to 2 based on their recommendations  DISPLAYFORM0
PekarS02 offer another variation on INLINEFORM0 , where the shortest path of the two concepts to the LCS is used, in addition to the shortest bath between the LCS and the root of the taxonomy (Equation EQREF6 )  DISPLAYFORM0
Feature–based methods represent each concept as a set of features and then measure the overlap or sharing of features to measure similarity  In particular, each concept is represented as the set of their ancestors, and similarity is a ratio of the intersection and union of these features 
MaedcheS01 quantify the similarity between two concepts as the ratio of the intersection over their union as shown in Equation EQREF8   DISPLAYFORM0
BatetSV11 extend this by excluding any shared features (in the numerator) as shown in Equation EQREF9   DISPLAYFORM0
Information content is formally defined as the negative log of the probability of a concept  The effect of this is to assign rare (low probability) concepts a high measure of information content, since the underlying assumption is that more specific concepts are less frequently used than more common ones 
Resnik95 modified this notion of information content in order to use it as a similarity measure  He defines the similarity of two concepts to be the information content of their LCS (Equation EQREF11 )  DISPLAYFORM0
JiangC97, Lin98, and PirroE10 extend INLINEFORM0 by incorporating the information content of the individual concepts in various different ways  Lin98 defines the similarity between two concepts as the ratio of information content of the LCS with the sum of the individual concept's information content (Equation EQREF12 )  Note that INLINEFORM1 has the same form as INLINEFORM2 and INLINEFORM3 , and is in effect using information content as a measure of specificity (rather than depth)  If there is more than one possible LCS, the LCS with the greatest IC is chosen  DISPLAYFORM0
JiangC97 define the distance between two concepts to be the sum of the information content of the two concepts minus twice the information content of the concepts' LCS  We modify this from a distance to a similarity measure by taking the reciprocal of the distance (Equation EQREF13 )  Note that the denominator of INLINEFORM0 is very similar to the numerator of INLINEFORM1   DISPLAYFORM0
PirroE10 define the similarity between two concepts as the information content of the two concept's LCS divided by the sum of their individual information content values minus the information content of their LCS (Equation EQREF14 )  Note that INLINEFORM0 can be viewed as a set–theoretic version of INLINEFORM1   DISPLAYFORM0
Information Content
The information content of a concept may be derived from a corpus (corpus–based) or directly from a taxonomy (intrinsic–based)  In this work we focus on corpus–based techniques 
For corpus–based information content, we estimate the probability of a concept INLINEFORM0 by taking the sum of the probability of the concept INLINEFORM1 and the probability its descendants INLINEFORM2 (Equation EQREF16 )  DISPLAYFORM0
The initial probabilities of a concept ( INLINEFORM0 ) and its descendants ( INLINEFORM1 ) are obtained by dividing the number of times each concept and descendant occurs in the corpus, and dividing that by the total numbers of concepts ( INLINEFORM2 ) 
Ideally the corpus from which we are estimating the probabilities of concepts will be sense–tagged  However, sense–tagging is a challenging problem in its own right, and it is not always possible to carry out reliably on larger amounts of text  In fact in this paper we did not use any sense–tagging of the corpus we derived information content from 
Instead, we estimated the probability of a concept by using the UMLSonMedline dataset  This was created by the National Library of Medicine and consists of concepts from the 2009AB UMLS and the counts of the number of times they occurred in a snapshot of Medline taken on 12 January, 2009  These counts were obtained by using the Essie Search Engine BIBREF14 which queried Medline with normalized strings from the 2009AB MRCONSO table in the UMLS  The frequency of a CUI was obtained by aggregating the frequency counts of the terms associated with the CUI to provide a rough estimate of its frequency  The information content measures then use this information to calculate the probability of a concept 
Another alternative is the use of Intrinsic Information Content  It assess the informativeness of concept based on its placement within a taxonomy by considering the number of incoming (ancestors) relative to outgoing (descendant) links BIBREF15 (Equation EQREF17 )  DISPLAYFORM0
where INLINEFORM0 are the number of descendants of concept INLINEFORM1 that are leaf nodes, INLINEFORM2 are the number of concept INLINEFORM3 's ancestors and INLINEFORM4 are the total number of leaf nodes in the taxonomy 
Relatedness Measures
Lesk86 observed that concepts that are related should share more words in their respective definitions than concepts that are less connected  He was able to perform word sense disambiguation by identifying the senses of words in a sentence with the largest number of overlaps between their definitions  An overlap is the longest sequence of one or more consecutive words that occur in both definitions  BanerjeeP03 extended this idea to WordNet, but observed that WordNet glosses are often very short, and did not contain enough information to distinguish between multiple concepts  Therefore, they created a super–gloss for each concept by adding the glosses of related concepts to the gloss of the concept itself (and then finding overlaps) 
PatwardhanP06 adapted this measure to second–order co–occurrence vectors  In this approach, a vector is created for each word in a concept's definition that shows which words co–occur with it in a corpus  These word vectors are averaged to create a single co-occurrence vector for the concept  The similarity between the concepts is calculated by taking the cosine between the concepts second–order vectors  LiuMPMP12 modified and extended this measure to be used to quantify the relatedness between biomedical and clinical terms in the UMLS  The work in this paper can be seen as a further extension of PatwardhanP06 and LiuMPMP12 
Method
In this section, we describe our second–order similarity vector measure  This incorporates both contextual information using the term pair's definition and their pairwise semantic similarity scores derived from a taxonomy  There are two stages to our approach  First, a co–occurrence matrix must be constructed  Second, this matrix is used to construct a second–order co–occurrence vector for each concept in a pair of concepts to be measured for relatedness 
Co–occurrence Matrix Construction
We build an INLINEFORM0 similarity matrix using an external corpus where the rows and columns represent words within the corpus and the element contains the similarity score between the row word and column word using the similarity measures discussed above  If a word maps to more than one possible sense, we use the sense that returns the highest similarity score 
For this paper our external corpus was the NLM 2015 Medline baseline  Medline is a bibliographic database containing over 23 million citations to journal articles in the biomedical domain and is maintained by National Library of Medicine  The 2015 Medline Baseline encompasses approximately 5,600 journals starting from 1948 and contains 23,343,329 citations, of which 2,579,239 contain abstracts  In this work, we use Medline titles and abstracts from 1975 to present day  Prior to 1975, only 2% of the citations contained an abstract  We then calculate the similarity for each bigram in this dataset and include those that have a similarity score greater than a specified threshold on these experiments 
Measure Term Pairs for Relatedness
We obtain definitions for each of the two terms we wish to measure  Due to the sparsity and inconsistencies of the definitions in the UMLS, we not only use the definition of the term (CUI) but also include the definition of its related concepts  This follows the method proposed by PatwardhanP06 for general English and WordNet, and which was adapted for the UMLS and the medical domain by LiuMPMP12  In particular we add the definitions of any concepts connected via a parent (PAR), child (CHD), RB (broader than), RN (narrower than) or TERM (terms associated with CUI) relation  All of the definitions for a term are combined into a single super–gloss  At the end of this process we should have two super–glosses, one for each term to be measured for relatedness 
Next, we process each super–gloss as follows:
We extract a first–order co–occurrence vector for each term in the super–gloss from the co–occurrence matrix created previously 
We take the average of the first order co–occurrence vectors associated with the terms in a super–gloss and use that to represent the meaning of the term  This is a second–order co–occurrence vector 
After a second–order co–occurrence vector has been constructed for each term, then we calculate the cosine between these two vectors to measure the relatedness of the terms 
Data
We use two reference standards to evaluate the semantic similarity and relatedness measures   UMNSRS was annotated for both similarity and relatedness by medical residents  MiniMayoSRS was annotated for relatedness by medical doctors (MD) and medical coders (coder)  In this section, we describe these data sets and describe a few of their differences 
MiniMayoSRS: The MayoSRS, developed by PakhomovPMMRC10, consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic  The relatedness of each term pair was assessed based on a four point scale: (4 0) practically synonymous, (3 0) related, (2 0) marginally related and (1 0) unrelated  MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter–annotator agreement was achieved  The average correlation between physicians is 0 68  The average correlation between medical coders is 0 78  We evaluate our method on the mean of the physician scores, and the mean of the coders scores in this subset in the same manner as reported by PedersenPPC07 
UMNSRS: The University of Minnesota Semantic Relatedness Set (UMNSRS) was developed by PakhomovMALPM10, and consists of 725 clinical term pairs whose semantic similarity and relatedness was determined independently by four medical residents from the University of Minnesota Medical School  The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness  The Intraclass Correlation Coefficient (ICC) for the reference standard tagged for similarity was 0 47, and 0 50 for relatedness  Therefore, as suggested by Pakhomov and colleagues,we use a subset of the ratings consisting of 401 pairs for the similarity set and 430 pairs for the relatedness set which each have an ICC of 0 73 
Experimental Framework
We conducted our experiments using the freely available open source software package UMLS::Similarity BIBREF16 version 1 47  This package takes as input two terms (or UMLS concepts) and returns their similarity or relatedness using the measures discussed in Section SECREF2  
Correlation between the similarity measures and human judgments were estimated using Spearman's Rank Correlation ( INLINEFORM0 )  Spearman's measures the statistical dependence between two variables to assess how well the relationship between the rankings of the variables can be described using a monotonic function  We used Fisher's r-to-z transformation BIBREF17 to calculate the significance between the correlation results 
Results and Discussion
Table TABREF26 shows the Spearman's Rank Correlation between the human scores from the four reference standards and the scores from the various measures of similarity introduced in Section SECREF2   Each class of measure is followed by the scores obtained when integrating our second order vector approach with these measures of semantic similarity 
Results Comparison
The results for UMNSRS tagged for similarity ( INLINEFORM0 ) and MiniMayoSRS tagged by coders show that all of the second-order similarity vector measures ( INLINEFORM1 ) except for INLINEFORM2 - INLINEFORM3 obtain a higher correlation than the original measures  We found that INLINEFORM4 - INLINEFORM5 and INLINEFORM6 - INLINEFORM7 obtain the highest correlations of all these results with human judgments 
For the UMNSRS dataset tagged for relatedness and MiniMayoSRS tagged by physicians (MD), the original INLINEFORM0 measure obtains a higher correlation than our measure ( INLINEFORM1 ) although the difference is not statistically significant ( INLINEFORM2 ) 
In order to analyze and better understand these results, we filtered the bigram pairs used to create the initial similarity matrix based on the strength of their similarity using the INLINEFORM0 and the INLINEFORM1 measures  Note that the INLINEFORM2 measure holds to a 0 to 1 scale, while INLINEFORM3 ranges from 0 to an unspecified upper bound that is dependent on the size of the corpus from which information content is estimated  As such we use a different range of threshold values for each measure  We discuss the results of this filtering below 
Thresholding Experiments
Table TABREF29 shows the results of applying the threshold parameter on each of the reference standards using the INLINEFORM0 measure  For example, a threshold of 0 indicates that all of the bigrams were included in the similarity matrix; and a threshold of 1 indicates that only the bigram pairs with a similarity score greater than one were included 
These results show that using a threshold cutoff of 2 obtains the highest correlation for the UMNSRS dataset, and that a threshold cutoff of 4 obtains the highest correlation for the MiniMayoSRS dataset  All of the results show an increase in correlation with human judgments when incorporating a threshold cutoff over all of the original measures  The increase in the correlation for the UMNSRS tagged for similarity is statistically significant ( INLINEFORM0 ), however this is not the case for the UMNSRS tagged for relatedness nor for the MiniMayoSRS data 
Similarly, Table TABREF30 shows the results of applying the threshold parameter (T) on each of the reference standards using the INLINEFORM0 measure  Although, unlike INLINEFORM1 whose scores are greater than or equal to 0 without an upper limit, the INLINEFORM2 measure returns scores between 0 and 1 (inclusive)  Therefore, here a threshold of 0 indicates that all of the bigrams were included in the similarity matrix; and a threshold of INLINEFORM3 indicates that only the bigram pairs with a similarity score greater than INLINEFORM4 were included  The results show an increase in accuracy for all of the datasets except for the MiniMayoSRS tagged for physicians  The increase in the results for the UMNSRS tagged for similarity and the MayoSRS is statistically significant ( INLINEFORM5 )  This is not the case for the UMNSRS tagged for relatedness nor the MiniMayoSRS 
Overall, these results indicate that including only those bigrams that have a sufficiently high similarity score increases the correlation results with human judgments, but what quantifies as sufficiently high varies depending on the dataset and measure 
Comparison with Previous Work
Recently, word embeddings BIBREF9 have become a popular method for measuring semantic relatedness in the biomedical domain  This is a neural network based approach that learns a representation of a word by word co–occurrence matrix  The basic idea is that the neural network learns a series of weights (the hidden layer within the neural network) that either maximizes the probability of a word given its context, referred to as the continuous bag of words (CBOW) approach, or that maximizes the probability of the context given a word, referred to as the Skip–gram approach  These approaches have been used in numerous recent papers 
muneeb2015evalutating trained both the Skip–gram and CBOW models over the PubMed Central Open Access (PMC) corpus of approximately 1 25 million articles  They evaluated the models on a subset of the UMNSRS data, removing word pairs that did not occur in their training corpus more than ten times  chiu2016how evaluated both the the Skip–gram and CBOW models over the PMC corpus and PubMed  They also evaluated the models on a subset of the UMNSRS ignoring those words that did not appear in their training corpus  Pakhomov2016corpus trained CBOW model over three different types of corpora: clinical (clinical notes from the Fairview Health System), biomedical (PMC corpus), and general English (Wikipedia)  They evaluated their method using a subset of the UMNSRS restricting to single word term pairs and removing those not found within their training corpus  sajad2015domain trained the Skip–gram model over CUIs identified by MetaMap on the OHSUMED corpus, a collection of 348,566 biomedical research articles  They evaluated the method on the complete UMNSRS, MiniMayoSRS and the MayoSRS datasets; any subset information about the dataset was not explicitly stated therefore we believe a direct comparison may be possible 
In addition, a previous work very closely related to ours is a retrofitting vector method proposed by YuCBJW16 that incorporates ontological information into a vector representation by including semantically related words  In their measure, they first map a biomedical term to MeSH terms, and second build a word vector based on the documents assigned to the respective MeSH term  They then retrofit the vector by including semantically related words found in the Unified Medical Language System  They evaluate their method on the MiniMayoSRS dataset 
Table TABREF31 shows a comparison to the top correlation scores reported by each of these works on the respective datasets (or subsets) they evaluated their methods on  N refers to the number of term pairs in the dataset the authors report they evaluated their method  The table also includes our top scoring results: the integrated vector-res and vector-faith  The results show that integrating semantic similarity measures into second–order co–occurrence vectors obtains a higher or on–par correlation with human judgments as the previous works reported results with the exception of the UMNSRS rel dataset  The results reported by Pakhomov2016corpus and chiu2016how obtain a higher correlation although the results can not be directly compared because both works used different subsets of the term pairs from the UMNSRS dataset 
Conclusion and Future Work
We have presented a method for quantifying the similarity and relatedness between two terms that integrates pair–wise similarity scores into second–order vectors  The goal of this approach is two–fold  First, we restrict the context used by the vector measure to words that exist in the biomedical domain, and second, we apply larger weights to those word pairs that are more similar to each other  Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments  We evaluated our method on datasets that have been manually annotated for relatedness and similarity and found evidence to support this hypothesis  In particular we discovered that guiding the creation of a second–order context vector by selecting term pairs from biomedical text based on their semantic similarity led to improved levels of correlation with human judgment 
We also explored using a threshold cutoff to include only those term pairs that obtained a sufficiently large level of similarity  We found that eliminating less similar pairs improved the overall results (to a point)  In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure  We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness  We are particularly interested in approaches that learn word, phrase, and sentence embeddings from structured corpora such as literature BIBREF23 and dictionary entries BIBREF24   Such embeddings could be integrated into a second–order vector or be used on their own 
Finally, we compared our proposed method to other distributional approaches, focusing on those that used word embeddings  Our results showed that integrating semantic similarity measures into second–order co–occurrence vectors obtains the same or higher correlation with human judgments as do various different word embedding approaches  However, a direct comparison was not possible due to variations in the subsets of the UMNSRS evaluation dataset used  In the future, we would not only like to conduct a direct comparison but also explore integrating semantic similarity into various kinds of word embeddings by training on pair–wise values of semantic similarity as well as co–occurrence statistics 
Context 3: Introduction
Massive Open Online Courses (MOOCs) have strived to bridge the social gap in higher education by bringing quality education from reputed universities to students at large  Such massive scaling through online classrooms, however, disrupt co-located, synchronous two-way communication between the students and the instructor 
MOOC platforms provide discussion forums for students to talk to their classmates about the lectures, homeworks, quizzes and provide a venue to socialise  Instructors (defined here as the course instructors, their teaching assistants and the MOOC platform's technical staff) monitor the discussion forum to post (reply to their message) in discussion threads among students  We refer to this posting as intervention, following prior work BIBREF0   However, due to large student enrolment, the student–instructor ratio in MOOCs is very high Therefore, instructors are not able to monitor and participate in all student discussions  To address this problem, a number of works have proposed systems e g , BIBREF0 , BIBREF1 to aid instructors to selectively intervene on student discussions where they are needed the most 
In this paper, we improve the state-of-the-art for instructor intervention in MOOC forums  We propose the first neural models for this prediction problem  We show that modelling the thread structure and the sequence of posts explicitly improves performance  Instructors in different MOOCs from different subject areas intervene differently  For example, on a Science, Technology, Engineering and Mathematics (STEM) MOOC, instructors may often intervene early as possible to resolve misunderstanding of the subject material and prevent confusion  However, in a Humanities MOOC, instructors allow for the students to explore open-ended discussions and debate among themselves  Such instructors may prefer to intervene later in the discussion to encourage further discussion or resolve conflicts among students  We therefore propose attention models to infer the latent context, i e , the series of posts that trigger an intervention  Earlier studies on MOOC forum intervention either model the entire context or require the context size to be specified explicitly 
Problem Statement
A thread INLINEFORM0 consists of a series of posts INLINEFORM1 through INLINEFORM2 where INLINEFORM3 is an instructor's post when INLINEFORM4 is intervened, if applicable  INLINEFORM5 is considered intervened if an instructor had posted at least once  The problem of predicting instructor intervention is cast as a binary classification problem  Intervened threads are predicted as 1 given while non-intervened threads are predicted as 0 given posts INLINEFORM6 through INLINEFORM7  
The primary problem leads to a secondary problem of inferring the appropriate amount of context to intervene  We define a context INLINEFORM0 of a post INLINEFORM1 as a series of linear contiguous posts INLINEFORM2 through INLINEFORM3 where INLINEFORM4   The problem of inferring context is to identify context INLINEFORM5 from a set of candidate contexts INLINEFORM6  
Modelling Context in Forums
Context has been used and modelled in various ways for different problems in discussion forums  In a work on a closely related problem of forum thread retrieval BIBREF2 models context using inter-post discourse e g , Question-Answer  BIBREF3 models the structural dependencies and relationships between forum posts using a conditional random field in their problem to infer the reply structure  Unlike BIBREF2 , BIBREF3 can be used to model any structural dependency and is, therefore, more general  In this paper, we seek to infer general dependencies between a reply and its previous context whereas BIBREF3 inference is limited to pairs of posts  More recently BIBREF4 proposed a context based model which factorises attention over threads of different lengths  Differently, we do not model length but the context before a post  However, our attention models cater to threads of all lengths 
BIBREF5 proposed graph structured LSTM to model the explicit reply structure in Reddit forums  Our work does not assume access to such a reply structure because 1) Coursera forums do not provide one and 2) forum participants often err by posting their reply to a different post than that they intended  At the other end of the spectrum are document classification models that do not assume structure in the document layout but try to infer inherent structure in the natural language, viz, words, sentences, paragraphs and documents  Hierarchical attention BIBREF6 is a well know recent work that classifies documents using a multi-level LSTMs with attention mechanism to select important units at each hierarchical level  Differently, we propose a hierarchical model that encodes layout hierarchy between a post and a thread but also infers reply structure using a attention mechanism since the layout does not reliably encode it 
Instructor Intervention in MOOC forums
The problem of predicting instructor intervention in MOOCs was proposed by BIBREF0   Later BIBREF7 evaluated baseline models by BIBREF0 over a larger corpus and found the results to vary widely across MOOCs  Since then subsequent works have used similar diverse evaluations on the same prediction problem BIBREF1 , BIBREF8   BIBREF1 proposed models with discourse features to enable better prediction over unseen MOOCs  BIBREF8 recently showed interventions on Coursera forums to be biased by the position at which a thread appears to an instructor viewing the forum interface and proposed methods for debiased prediction 
While all works since BIBREF0 address key limitations in this line of research, they have not investigated the role of structure and sequence in the threaded discussion in predicting instructor interventions  BIBREF0 proposed probabilistic graphical models to model structure and sequence  They inferred vocabulary dependent latent post categories to model the thread sequence and infer states that triggered intervention  Their model, however, requires a hyperparameter for the number of latent states  It is likely that their empirically reported setting will not generalise due to their weak evaluation BIBREF7   In this paper, we propose models to infer the context that triggers instructor intervention that does not require context lengths to be set apriori  All our proposed models generalise over modelling assumptions made by BIBREF0  
For the purpose of comparison against a state-of-the-art and competing baselines we choose BIBREF7 since BIBREF0 's system and data are not available for replication 
Data and Preprocessing
We evaluate our proposed models over a corpus of 12 MOOC iterations (offerings) on Coursera org In partnership with Coursera and in line with its Terms of Service, we obtained the data for use in our academic research  Following prior work BIBREF7 we evaluate over a diverse dataset to represent MOOCs of varying sizes, instructor styles, instructor team sizes and number of threads intervened  We only include threads from sub-forums on Lecture, Homework, Quiz and Exam  We also normalise and label sub-forums with other non-standard names (e g , Assignments instead of Homework) into of the four said sub-forums  Threads on general discussion, meet and greet and other custom sub-forums for social chitchat are omitted as our focus is to aid instructors on intervening on discussion on the subject matter  We also exclude announcement threads and other threads started by instructors since they are not interventions  We preprocess each thread by replacing URLs, equations and other mathematical formulae and references to timestamps in lecture videos by tokens INLINEFORM0 URL INLINEFORM1 , INLINEFORM2 MATH INLINEFORM3 , INLINEFORM4 TIMEREF INLINEFORM5 respectively  We also truncate intervened threads to only include posts before the first instructor post since the instructor's and subsequent posts will bias the prediction due to the instructor's post 
Model
The key innovation of our work is to decompose the intervention prediction problem into a two-stage model that first explicitly tries to discover the proper context to which a potential intervention could be replying to, and then, predict the intervention status  This model implicitly assesses the importance (or urgency) of the existing thread's context to decide whether an intervention is necessary  For example in Figure SECREF1 , prior to the instructor's intervention, the ultimate post (Post #6) by Student 2 already acknowledged the OP's gratitude for his answer  In this regard, the instructor may have decided to use this point to summarize the entire thread to consolidate all the pertinent positions  Here, we might assume that the instructor's reply takes the entire thread (Posts #1–6) as the context for her reply 
This subproblem of inferring the context scope is where our innovation centers on  To be clear, in order to make the prediction that a instruction intervention is now necessary on a thread, the instructor's reply is not yet available — the model predicts whether a reply is necessary — so in the example, only Posts #1–6 are available in the problem setting  To infer the context, we have to decide which subsequence of posts are the most plausible motivation for an intervention 
Recent work in deep neural modeling has used an attention mechanism as a focusing query to highlight specific items within the input history that significantly influence the current decision point  Our work employs this mechanism – but with a twist: due to the fact that the actual instructor intervention is not (yet) available at the decision timing, we cannot use any actual intervention to decide the context  To employ attention, we must then employ a surrogate text as the query to train our prediction model  Our model variants model assess the suitability of such surrogate texts for the attention mechanism basis 
Congruent with the representation of the input forums, in all our proposed models, we encode the discussion thread hierarchically  We first build representations for each post by passing pre-trained word vector representations from GloVe BIBREF9 for each word through an LSTM BIBREF10 , INLINEFORM0   We use the last layer output of the LSTM as a representation of the post  We refer this as the post vector INLINEFORM1  
Then each post INLINEFORM0 is passed through another LSTM, INLINEFORM1 , whose last layer output forms the encoding of the entire thread  Hidden unit outputs of INLINEFORM2 represent the contexts INLINEFORM3 ; that is, snapshots of the threads after each post, as shown in Figure FIGREF1  
The INLINEFORM0 and INLINEFORM1 together constitute the hierarchical LSTM (hLSTM) model  This general hLSTM model serves as the basis for our model exploration in the rest of this section 
Contextual Attention Models
When they intervene, instructors either pay attention to a specific post or a series of posts, which trigger their reply  However, instructors rarely explicitly indicate to which post(s) their intervention is in relation to  This is the case in our corpus, party due to Coursera's user interface which only allows for single level comments (see Figure FIGREF2 )  Based solely on the binary, thread-level intervention signal, our secondary objective seeks to infer the appropriate context – represented by a sequence of posts – as the basis for the intervention 
We only consider linear contiguous series of posts starting with the thread's original post to constitute to a context; e g , INLINEFORM0   This is a reasonable as MOOC forum posts always reply to the original post or to a subsequent post, which in turn replies to the original post  This is in contrast to forums such as Reddit that have a tree or graph-like structure that require forum structure to be modelled explicitly, such as in BIBREF5  
We propose three neural attention BIBREF11 variants based on how an instructor might attend and reply to a context in a thread: the ultimate, penultimate and any post attention models  We review each of these in turn

ground_truth: Skip–gram, CBOW

integrated vector-res, vector-faith, Skip–gram, CBOW

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 was the previous state of the art for this task?
Context 3: 
Ultimate Post Attention (UPA) Model  In this model we attend to the context represented by hidden state of the INLINEFORM0   We use the post prior to the instructor's reply as a query over the contexts INLINEFORM1 to compute attention weights INLINEFORM2 , which are then used to compute the attended context representation INLINEFORM3 (recall again that the intervention text itself is not available for this purpose)  This attention formulation makes an equivalence between the final INLINEFORM4 post and the prospective intervention, using Post INLINEFORM5 as the query for finding the appropriate context INLINEFORM6 , inclusive of itself INLINEFORM7   Said in another way, UPA uses the most recent content in the thread as the attentional query for context 
For example, if post INLINEFORM0 is the instructor's reply, post INLINEFORM1 will query over the contexts INLINEFORM2 and INLINEFORM3   The model schematic is shown in Figure FIGREF12  
The attended context representations are computed as: DISPLAYFORM0
The INLINEFORM0 representation is then passed through a fully connected softmax layer to yield the binary prediction 
Penultimate Post Attention (PPA) Model  While the UPA model uses the most recent text and makes the ultimate post itself available as potential context, our the ultimate post may be better modeled as having any of its prior posts as potential context  Penultimate Post Attention (PPA) variant does this  The schematic and the equations for the PPA model are obtained by summing over contexts INLINEFORM0 in Equation EQREF10 and Figure FIGREF12   While we could properly model such a context inference decision with any post INLINEFORM1 and prospective contexts INLINEFORM2 (where INLINEFORM3 is a random post), it makes sense to use the penultimate post, as we can make the most information available to the model for the context inference 
The attended context representations are computed as: DISPLAYFORM0
Any Post Attention (APA) Model  APA further relaxes both UPA and PPA, allowing APA to generalize and hypothesize that the prospective instructor intervention is based on the context that any previous post INLINEFORM0 replied to  In this model, each post INLINEFORM1 is set as a query to attend to its previous context INLINEFORM2   For example, INLINEFORM3 will attend to INLINEFORM4   Different from standard attention mechanisms, APA attention weights INLINEFORM5 are obtained by normalising interaction matrix over the different queries 
In APA, the attention context INLINEFORM0 is computed via: DISPLAYFORM0
Evaluation
The baseline and the models are evaluated on a corpus of 12 MOOC discussion forums  We train on 80% of the training data and report evaluation results on the held-out 20% of test data  We report INLINEFORM0 scores on the positive class (interventions), in line with prior work  We also argue that recall of the positive class is more important than precision, since it is costlier for instructors to miss intervening on a thread than spending irrelevant time intervening on a less critical threads due to false positives 
Model hyperpameter settings  All proposed and baseline neural models are trained using Adam optimizer with a learning rate of 0 001  We used cross-entropy as loss function  Importantly we updated the model parameters during training after each instance as in vanilla stochastic gradient descent; this setting was practical since data on most courses had only a few hundred instances enabling convergence within a reasonable training time of a few hours (see Table TABREF15 , column 2)  Models were trained for a single epoch as most of our courses with a few hundred thread converged after a single epoch  We used 300-dimensional GloVe vectors and permitted the embeddings to be updated during the model's end-to-end training  The hidden dimension size of both INLINEFORM0 and INLINEFORM1 are set to 128 for all the models 
Baselines  We compare our models against a neural baseline models, hierarchical LSTM (hLSTM), with the attention ablated but with access to the complete context, and a strong, open-sourced feature-rich baseline BIBREF7   We choose BIBREF7 over other prior works such as BIBREF0 since we do not have access to the dataset or the system used in their papers for replication  BIBREF7 is a logistic regression classifier with features inclusive of bag-of-words representation of the unigrams and thread length, normalised counts of agreements to previous posts, counts of non-lexical reference items such as URLs, and the Coursera forum type in which a thread appeared  We also report aggregated results from a hLSTM model with access only to the last post as context for comparison  Table TABREF17 compares the performance of these baselines against our proposed methods 
Results
Table TABREF15 shows performance of all our proposed models and the neural baseline over our 12 MOOC dataset  Our models of UPA, PPA individually better the baseline by 5 and 2% on INLINEFORM0 and 3 and 6% on recall respectively  UPA performs the best in terms of INLINEFORM1 on average while PPA performs the best in terms of recall on average  At the individual course level, however, the results are mixed  UPA performs the best on INLINEFORM2 on 5 out of 12 courses, PPA on 3 out 12 courses, APA 1 out of 12 courses and the baseline hLSTM on 1  PPA performs the best on recall on 7 out of the 12 courses  We also note that course level performance differences correlate with the course size and intervention ratio (hereafter, i ratio), which is the ratio of intervened to non-intervened threads  UPA performs better than PPA and APA on low intervention courses (i ratio INLINEFORM3 0 25) mainly because PPA and APA's performance drops steeply when i ratio drops (see col 2 parenthesis and INLINEFORM4 of PPA and APA)  While all the proposed models beat the baseline on every course except casebased-2  On medicalneuro-2 and compilers-4 which have the lowest i ratio among the 12 courses none of the neural models better the reported baseline BIBREF7 (course level not scores not shown in this paper)  The effect is pronounced in compilers-4 course where none of the neural models were able to predict any intervened threads  This is due to the inherent weakness of standard neural models, which are unable to learn features well enough when faced with sparse data 
The best performance of UPA indicates that the reply context of the instructor's post INLINEFORM0 correlates strongly with that of the previous post INLINEFORM1   This is not surprising since normal conversations are typically structured that way 
Discussion
In order to further understand the models' ability to infer the context and its effect on intervention prediction, we further investigate the following research questions 
RQ1  Does context inference help intervention prediction?
In order to understand if context inference is useful to intervention prediction, we ablate the attention components and experiment with the vanilla hierarchical LSTM model  Row 3 of Table TABREF17 shows the macro averaged result from this experiment  The UPA and PPA attention models better the vanilla hLSTM by 5% and 2% on average in INLINEFORM0 respectively  Recall that the vanilla hLSTM already has access to a context consisting of all posts (from INLINEFORM1 through INLINEFORM2 )  In contrast, the UPA and PPA models selectively infers a context for INLINEFORM3 and INLINEFORM4 posts, respectively, and use it to predict intervention  The improved performance of our attention models that actively select their optimal context, over a model with the complete thread as context, hLSTM, shows that the context inference improves intervention prediction over using the default full context 
RQ2  How well do the models perform across threads of different lengths? To understand the models' prediction performance across threads of different lengths, we bin threads by length and study the models' recall  We choose three courses, ml-5, rprog-3 and calc-1, from our corpus of 12 with the highest number of positive instances ( INLINEFORM0 100 threads)  We limit our analysis to these since binning renders courses with fewer positive instances sparse  Figure FIGREF18 shows performance across thread lengths from 1 through 7 posts and INLINEFORM1 posts  Clearly, the UPA model performs much better on shorter threads than on longer threads while PPA and APA works better on longer threads  Although, UPA is the best performing model in terms of overall INLINEFORM2 its performance drops steeply on threads of length INLINEFORM3   UPA's overall best performance is because most of the interventions in the corpus happen after one post  To highlight the performance of APA we show an example from smac-1 in Figure FIGREF22 with nine posts which was predicted correctly as intervened by APA but not by other models  Threads shows students confused over a missing figure in a homework  The instructor finally shows up, though late, to resolve the confusion 
RQ3  Do models trained with different context lengths perform better than when trained on a single context length?
We find that context length has a regularising effect on the model's performance at test time  This is not surprising since models trained with threads of single context length will not generalise to infer different context lengths  Row 4 of Table TABREF17 shows a steep performance drop in training by classifier with all threads truncated to a context of just one post, INLINEFORM0 , the post immediately preceding the intervened post  We also conducted an experiment with a multi-objective loss function with an additive cross-entropy term where each term computes loss from a model with context limited to a length of 3  We chose 3 since intervened threads in all the courses had a median length between 3 and 4  We achieved an INLINEFORM1 of 0 45 with a precision of 0 47 and recall of 0 43  This achieves a performance comparable to that of the BIBREF7 with context length set to only to 3  This approach of using infinitely many loss terms for each context length from 1 through the maximum thread length in a course is naive and not practical  We only use this model to show the importance of training the model with loss from threads of different lengths to prevent models overfitting to threads of specific context lengths 
Conclusion
We predict instructor intervention on student discussions by first inferring the optimal size of the context needed to decide on the intervention decision for the intervened post  We first show that a structured representation of the complete thread as the context is better than a bag-of-words, feature-rich representation  We then propose attention-based models to infer and select a context – defined as a contiguous subsequence of student posts – to improve over a model that always takes the complete thread as a context to prediction intervention  Our Any Post Attention (APA) model enables instructors to tune the model to predict intervention early or late  We posit our APA model will enable MOOC instructors employing varying pedagogical styles to use the model equally well  We introspect the attention models' performance across threads of varying lengths and show that APA predicts intervention on longer threads, which possesses more candidate contexts, better 
We note that the recall of the predictive models for longer threads (that is, threads of length greater 2) can still be improved  Models perform differently between shorter and longer length  An ensemble model or a multi-objective loss function is thus planned in our future work to better prediction on such longer threads 

ground_truth: hLSTM

hLSTM