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 features are used?
Context 1: Introduction
Cancer is one of the leading causes of death in the world, with over 80,000 deaths registered in Canada in 2017 (Canadian Cancer Statistics 2017)  A computer-aided system for cancer diagnosis usually involves a pathologist rendering a descriptive report after examining the tissue glass slides obtained from the biopsy of a patient  A pathology report contains specific analysis of cells and tissues, and other histopathological indicators that are crucial for diagnosing malignancies  An average sized laboratory may produces a large quantity of pathology reports annually (e g , in excess of 50,000), but these reports are written in mostly unstructured text and with no direct link to the tissue sample  Furthermore, the report for each patient is a personalized document and offers very high variability in terminology due to lack of standards and may even include misspellings and missing punctuation, clinical diagnoses interspersed with complex explanations, different terminology to label the same malignancy, and information about multiple carcinoma appearances included in a single report BIBREF0  
In Canada, each Provincial and Territorial Cancer Registry (PTCR) is responsible for collecting the data about cancer diseases and reporting them to Statistics Canada (StatCan)  Every year, Canadian Cancer Registry (CCR) uses the information sources of StatCan to compile an annual report on cancer and tumor diseases  Many countries have their own cancer registry programs  These programs rely on the acquisition of diagnostic, treatment, and outcome information through manual processing and interpretation from various unstructured sources (e g , pathology reports, autopsy/laboratory reports, medical billing summaries)  The manual classification of cancer pathology reports is a challenging, time-consuming task and requires extensive training BIBREF0  
With the continued growth in the number of cancer patients, and the increase in treatment complexity, cancer registries face a significant challenge in manually reviewing the large quantity of reports BIBREF1 , BIBREF0   In this situation, Natural Language Processing (NLP) systems can offer a unique opportunity to automatically encode the unstructured reports into structured data  Since, the registries already have access to the large quantity of historically labeled and encoded reports, a supervised machine learning approach of feature extraction and classification is a compelling direction for making their workflow more effective and streamlined  If successful, such a solution would enable processing reports in much lesser time allowing trained personnel to focus on their research and analysis  However, developing an automated solution with high accuracy and consistency across wide variety of reports is a challenging problem 
For cancer registries, an important piece of information in a pathology report is the associated ICD-O code which describes the patient's histological diagnosis, as described by the World Health Organization's (WHO) International Classification of Diseases for Oncology BIBREF2   Prediction of the primary diagnosis from a pathology report provides a valuable starting point for exploration of machine learning techniques for automated cancer surveillance  A major application for this purpose would be “auto-reporting” based on analysis of whole slide images, the digitization of the biopsy glass slides  Structured, summarized and categorized reports can be associated with the image content when searching in large archives  Such as system would be able to drastically increase the efficiency of diagnostic processes for the majority of cases where in spite of obvious primary diagnosis, still time and effort is required from the pathologists to write a descriptive report 
The primary objective of our study is to analyze the efficacy of existing machine learning approaches for the automated classification of pathology reports into different diagnosis categories  We demonstrate that TF-IDF feature vectors combined with linear SVM or XGBoost classifier can be an effective method for classification of the reports, achieving up to 83% accuracy  We also show that TF-IDF features are capable of identifying important keywords within a pathology report  Furthermore, we have created a new dataset consisting of 1,949 pathology reports across 37 primary diagnoses  Taken together, our exploratory experiments with a newly introduced dataset on pathology reports opens many new opportunities for researchers to develop a scalable and automatic information extraction from unstructured pathology reports 
Background
NLP approaches for information extraction within the biomedical research areas range from rule-based systems BIBREF3 , to domain-specific systems using feature-based classification BIBREF1 , to the recent deep networks for end-to-end feature extraction and classification BIBREF0   NLP has had varied degree of success with free-text pathology reports BIBREF4   Various studies have acknowledge the success of NLP in interpreting pathology reports, especially for classification tasks or extracting a single attribute from a report BIBREF4 , BIBREF5  
The Cancer Text Information Extraction System (caTIES) BIBREF6 is a framework developed in a caBIG project focuses on information extraction from pathology reports  Specifically, caTIES extracts information from surgical pathology reports (SPR) with good precision as well as recall 
Another system known as Open Registry BIBREF7 is capable of filtering the reports with disease codes containing cancer  In BIBREF8 , an approach called Automated Retrieval Console (ARC) is proposed which uses machine learning models to predict the degree of association of a given pathology or radiology with the cancer  The performance ranges from an F-measure of 0 75 for lung cancer to 0 94 for colorectal cancer  However, ARC uses domain-specific rules which hiders with the generalization of the approach to variety of pathology reports 
This research work is inspired by themes emerging in many of the above studies  Specifically, we are evaluating the task of predicting the primary diagnosis from the pathology report  Unlike previous approaches, the system does not rely on custom rule-based knowledge, domain specific features, balanced dataset with fewer number of classes 
Materials and Methods
We assembled a dataset of 1,949 cleaned pathology reports  Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes  The reports are collected from four different body parts or primary sites from multiple patients  The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution  The dataset was developed in three steps as follows 
Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9   Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney  The selection was primarily made based on the quality of PDF files 
Cleaning reports: The next step was to extract the text content from these reports  Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset  We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files  Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system 
Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively  This split resulted in 1,364 training, and 585 testing reports 
Pre-Processing of Reports
We pre-processed the reports by setting their text content to lowercase and filtering out any non-alphanumeric characters  We used NLTK library to remove stopping words, e g , `the', `an', `was', `if' and so on BIBREF10   We then analyzed the reports to find common bigrams, such as “lung parenchyma”, “microscopic examination”, “lymph node” etc  We joined the biagrams with a hyphen, converting them into a single word  We further removed the words that occur less than 2% in each of the diagnostic category  As well, we removed the words that occur more than 90% across all the categories  We stored each pre-processed report in a separate text file 
TF-IDF features
TF-IDF stands for Term Frequency-Inverse Document Frequency, and it is a useful weighting scheme in information retrieval and text mining  TF-IDF signifies the importance of a term in a document within a corpus  It is important to note that a document here refers to a pathology report, a corpus refers to the collection of reports, and a term refers to a single word in a report  The TF-IDF weight for a term INLINEFORM0 in a document INLINEFORM1 is given by DISPLAYFORM0
We performed the following steps to transform a pathology report into a feature vector:
Create a set of vocabulary containing all unique words from all the pre-processed training reports 
Create a zero vector INLINEFORM0 of the same length as the vocabulary 
For each word INLINEFORM0 in a report INLINEFORM1 , set the corresponding index in INLINEFORM2 to INLINEFORM3  
The resultant INLINEFORM0 is a feature vector for the report INLINEFORM1 and it is a highly sparse vector 
Keyword extraction and topic modelling
The keyword extraction involves identifying important words within reports that summarizes its content  In contrast, the topic modelling allows grouping these keywords using an intelligent scheme, enabling users to further focus on certain aspects of a document  All the words in a pathology report are sorted according to their TF-IDF weights  The top INLINEFORM0 sorted words constitute the top INLINEFORM1 keywords for the report  The INLINEFORM2 is empirically set to 50 within this research  The extracted keywords are further grouped into different topics by using latent Dirichlet allocation (LDA) BIBREF11   The keywords in a report are highlighted using the color theme based on their topics 
Evaluation metrics
Each model is evaluated using two standard NLP metrics—micro and macro averaged F-scores, the harmonic mean of related metrics precision and recall  For each diagnostic category INLINEFORM0 from a set of 37 different classes INLINEFORM1 , the number of true positives INLINEFORM2 , false positives INLINEFORM3 , and false negatives INLINEFORM4 , the micro F-score is defined as DISPLAYFORM0
whereas macro F-score is given by DISPLAYFORM0
In summary, micro-averaged metrics have class representation roughly proportional to their test set representation (same as accuracy for classification problem with a single label per data point), whereas macro-averaged metrics are averaged by class without weighting by class prevalence BIBREF12  
Experimental setting
In this study, we performed two different series of experiments: i) evaluating the performance of TF-IDF features and various machine learning classifiers on the task of predicting primary diagnosis from the text content of a given report, and ii) using TF-IDF and LDA techniques to highlight the important keywords within a report  For the first experiment series, training reports are pre-processed, then their TF-IDF features are extracted  The TF-IDF features and the training labels are used to train different classification models  These different classification models and their hyper-parameters are reported in tab:classifier  The performance of classifiers is measured quantitatively on the test dataset using the evaluation metrics discussed in the previous section  For the second experiment series, a random report is selected and its top 50 keywords are extracted using TF-IDF weights  These 50 keywords are highlighted using different colors based on their associated topic, which are extracted through LDA  A non-expert based qualitative inspection is performed on the extracted keywords and their corresponding topics 
Experiment Series 1
A classification model is trained to predict the primary diagnosis given the content of the cancer pathology report  The performance results on this task are reported in tab:results  We can observe that the XGBoost classifier outperformed all other models for both the micro F-score metric, with a score of 0 92, and the macro F-score metric, with a score of 0 31  This was an improvement of 7% for the micro F-score over the next best model, SVM-L, and a marginal improvement of 5% for macro F-score  It is interesting to note that SVM with linear kernels performs much better than SVM with RBF kernel, scoring 9% on the macro F-score and 12% more on the micro F-score  It is suspected that since words used in primary diagnosis itself occur in some reports, thus enabling the linear models to outperform complex models 
Experiment Series 2
fig:keywords shows the top 50 keywords highlighted using TF-IDF and LDA  The proposed approach has performed well in highlighting the important regions, for example the topic highlighted with a red color containing “presence range tumor necrosis” provides useful biomarker information to readers 
Conclusions
We proposed a simple yet efficient TF-IDF method to extract and corroborate useful keywords from pathology cancer reports  Encoding a pathology report for cancer and tumor surveillance is a laborious task, and sometimes it is subjected to human errors and variability in the interpretation  One of the most important aspects of encoding a pathology report involves extracting the primary diagnosis  This may be very useful for content-based image retrieval to combine with visual information  We used existing classification model and TF-IDF features to predict the primary diagnosis  We achieved up to 92% accuracy using XGBoost classifier  The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports 
Context 2: Introduction
Twitter is a widely used microblogging platform, where users post and interact with messages, “tweets”  Understanding the semantic representation of tweets can benefit a plethora of applications such as sentiment analysis BIBREF0 , BIBREF1 , hashtag prediction BIBREF2 , paraphrase detection BIBREF3 and microblog ranking BIBREF4 , BIBREF5   However, tweets are difficult to model as they pose several challenges such as short length, informal words, unusual grammar and misspellings  Recently, researchers are focusing on leveraging unsupervised representation learning methods based on neural networks to solve this problem  Once these representations are learned, we can use off-the-shelf predictors taking the representation as input to solve the downstream task BIBREF6 , BIBREF7   These methods enjoy several advantages: (1) they are cheaper to train, as they work with unlabelled data, (2) they reduce the dependence on domain level experts, and (3) they are highly effective across multiple applications, in practice 
Despite this, there is a lack of prior work which surveys the tweet-specific unsupervised representation learning models  In this work, we attempt to fill this gap by investigating the models in an organized fashion  Specifically, we group the models based on the objective function it optimizes  We believe this work can aid the understanding of the existing literature  We conclude the paper by presenting interesting future research directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models 
Unsupervised Tweet Representation Models
There are various models spanning across different model architectures and objective functions in the literature to compute tweet representation in an unsupervised fashion  These models work in a semi-supervised way - the representations generated by the model is fed to an off-the-shelf predictor like Support Vector Machines (SVM) to solve a particular downstream task  These models span across a wide variety of neural network based architectures including average of word vectors, convolutional-based, recurrent-based and so on  We believe that the performance of these models is highly dependent on the objective function it optimizes – predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision  In this section, we provide the first of its kind survey of the recent tweet-specific unsupervised models in an organized fashion to understand the literature  Specifically, we categorize each model based on the optimized objective function as shown in Figure FIGREF1   Next, we study each category one by one 
Modeling within-tweet relationships
Motivation: Every tweet is assumed to have a latent topic vector, which influences the distribution of the words in the tweet  For example, though the appearance of the phrase catch the ball is frequent in the corpus, if we know that the topic of a tweet is about “technology”, we can expect words such as bug or exception after the word catch (ignoring the) instead of the word ball since catch the bug/exception is more plausible under the topic “technology”  On the other hand, if the topic of the tweet is about “sports”, then we can expect ball after catch  These intuitions indicate that the prediction of neighboring words for a given word strongly relies on the tweet also 
Models: BIBREF8 's work is the first to exploit this idea to compute distributed document representations that are good at predicting words in the document  They propose two models: PV-DM and PV-DBOW, that are extensions of Continuous Bag Of Words (CBOW) and Skip-gram model variants of the popular Word2Vec model BIBREF9 respectively – PV-DM inserts an additional document token (which can be thought of as another word) which is shared across all contexts generated from the same document; PV-DBOW attempts to predict the sampled words from the document given the document representation  Although originally employed for paragraphs and documents, these models work better than the traditional models: BOW BIBREF10 and LDA BIBREF11 for tweet classification and microblog retrieval tasks BIBREF12   The authors in BIBREF12 make the PV-DM and PV-DBOW models concept-aware (a rich semantic signal from a tweet) by augmenting two features: attention over contextual words and conceptual tweet embedding, which jointly exploit concept-level senses of tweets to compute better representations  Both the discussed works have the following characteristics: (1) they use a shallow architecture, which enables fast training, (2) computing representations for test tweets requires computing gradients, which is time-consuming for real-time Twitter applications, and (3) most importantly, they fail to exploit textual information from related tweets that can bear salient semantic signals 
Modeling inter-tweet relationships
Motivation: To capture rich tweet semantics, researchers are attempting to exploit a type of sentence-level Distributional Hypothesis BIBREF10 , BIBREF13   The idea is to infer the tweet representation from the content of adjacent tweets in a related stream like users' Twitter timeline, topical, retweet and conversational stream  This approach significantly alleviates the context insufficiency problem caused due to the ambiguous and short nature of tweets BIBREF0 , BIBREF14  
Models: Skip-thought vectors BIBREF15 (STV) is a widely popular sentence encoder, which is trained to predict adjacent sentences in the book corpus BIBREF16   Although the testing is cheap as it involves a cheap forward propagation of the test sentence, STV is very slow to train thanks to its complicated model architecture  To combat this computational inefficiency, FastSent BIBREF17 propose a simple additive (log-linear) sentence model, which predicts adjacent sentences (represented as BOW) taking the BOW representation of some sentence in context  This model can exploit the same signal, but at a much lower computational expense  Parallel to this work, Siamase CBOW BIBREF18 develop a model which directly compares the BOW representation of two sentence to bring the embeddings of a sentence closer to its adjacent sentence, away from a randomly occurring sentence in the corpus  For FastSent and Siamese CBOW, the test sentence representation is a simple average of word vectors obtained after training  Both of these models are general purpose sentence representation models trained on book corpus, yet give a competitive performance over previous models on the tweet semantic similarity computation task  BIBREF14 's model attempt to exploit these signals directly from Twitter

ground_truth: Unanswerable

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

Please answer the questions based on the text provided below. Which dataset do they use?
Context 2:  With the help of attention technique and learned user representation, this log-linear model is able to capture salient semantic information from chronologically adjacent tweets of a target tweet in users' Twitter timeline 
Modeling from structured resources
Motivation: In recent times, building representation models based on supervision from richly structured resources such as Paraphrase Database (PPDB) BIBREF19 (containing noisy phrase pairs) has yielded high quality sentence representations  These methods work by maximizing the similarity of the sentences in the learned semantic space 
Models: CHARAGRAM BIBREF20 embeds textual sequences by learning a character-based compositional model that involves addition of the vectors of its character n-grams followed by an elementwise nonlinearity  This simpler architecture trained on PPDB is able to beat models with complex architectures like CNN, LSTM on SemEval 2015 Twitter textual similarity task by a large margin  This result emphasizes the importance of character-level models that address differences due to spelling variation and word choice  The authors in their subsequent work BIBREF21 conduct a comprehensive analysis of models spanning the range of complexity from word averaging to LSTMs for its ability to do transfer and supervised learning after optimizing a margin based loss on PPDB  For transfer learning, they find models based on word averaging perform well on both the in-domain and out-of-domain textual similarity tasks, beating LSTM model by a large margin  On the other hand, the word averaging models perform well for both sentence similarity and textual entailment tasks, outperforming the LSTM  However, for sentiment classification task, they find LSTM (trained on PPDB) to beat the averaging models to establish a new state of the art  The above results suggest that structured resources play a vital role in computing general-purpose embeddings useful in downstream applications 
Modeling as an autoencoder
Motivation: The autoencoder based approach learns latent (or compressed) representation by reconstructing its own input  Since textual data like tweets contain discrete input signals, sequence-to-sequence models BIBREF22 like STV can be used to build the solution  The encoder model which encodes the input tweet can typically be a CNN BIBREF23 , recurrent models like RNN, GRU, LSTM BIBREF24 or memory networks BIBREF25   The decoder model which generates the output tweet can typically be a recurrent model that predicts a output token at every time step 
Models: Sequential Denoising Autoencoders (SDAE) BIBREF17 is a LSTM-based sequence-to-sequence model, which is trained to recover the original data from the corrupted version  SDAE produces robust representations by learning to represent the data in terms of features that explain its important factors of variation  Tweet2Vec BIBREF3 is a recent model which uses a character-level CNN-LSTM encoder-decoder architecture trained to construct the input tweet directly  This model outperforms competitive models that work on word-level like PV-DM, PV-DBOW on semantic similarity computation and sentiment classification tasks, thereby showing that the character-level nature of Tweet2Vec is best-suited to deal with the noise and idiosyncrasies of tweets  Tweet2Vec controls the generalization error by using a data augmentation technique, wherein tweets are replicated and some of the words in the replicated tweets are replaced with their synonyms  Both SDAE and Tweet2Vec has the advantage that they don't need a coherent inter-sentence narrative (like STV), which is hard to obtain in Twitter 
Modeling using weak supervision
Motivation: In a weakly supervised setup, we create labels for a tweet automatically and predict them to learn potentially sophisticated models than those obtained by unsupervised learning alone  Examples of labels include sentiment of the overall tweet, words like hashtag present in the tweet and so on  This technique can create a huge labeled dataset especially for building data-hungry, sophisticated deep learning models 
Models: BIBREF26 learns sentiment-specific word embedding (SSWE), which encodes the polarity information in the word representations so that words with contrasting polarities and similar syntactic context (like good and bad) are pushed away from each other in the semantic space that it learns  SSWE utilizes the massive distant-supervised tweets collected by positive and negative emoticons to build a powerful tweet representation, which are shown to be useful in tasks such as sentiment classification and word similarity computation in sentiment lexicon  BIBREF2 observes that hashtags in tweets can be considered as topics and hence tweets with similar hashtags must come closer to each other  Their model predicts the hashtags by using a Bi-GRU layer to embed the tweets from its characters  Due to subword modeling, such character-level models can approximate the representations for rare words and new words (words not seen during training) in the test tweets really well  This model outperforms the word-level baselines for hashtag prediction task, thereby concluding that exploring character-level models for tweets is a worthy research direction to pursue  Both these works fail to study the model's generality BIBREF27 , i e , the ability of the model to transfer the learned representations to diverse tasks 
Future Directions
In this section we present the future research directions which we believe can be worth pursuing to generate high quality tweet embeddings 
Conclusion
In this work we study the problem of learning unsupervised tweet representations  We believe our survey of the existing works based on the objective function can give vital perspectives to researchers and aid their understanding of the field  We also believe the future research directions studied in this work can help in breaking the barriers in building high quality, general purpose tweet representation models 
Context 3: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International Licence  Licence details: http://creativecommons org/licenses/by/4 0/
In the spirit of the brevity of social media's messages and reactions, people have got used to express feelings minimally and symbolically, as with hashtags on Twitter and Instagram  On Facebook, people tend to be more wordy, but posts normally receive more simple “likes” than longer comments  Since February 2016, Facebook users can express specific emotions in response to a post thanks to the newly introduced reaction feature (see Section SECREF2 ), so that now a post can be wordlessly marked with an expression of say “joy" or “surprise" rather than a generic “like” 
It has been observed that this new feature helps Facebook to know much more about their users and exploit this information for targeted advertising BIBREF0 , but interest in people's opinions and how they feel isn't limited to commercial reasons, as it invests social monitoring, too, including health care and education BIBREF1   However, emotions and opinions are not always expressed this explicitly, so that there is high interest in developing systems towards their automatic detection  Creating manually annotated datasets large enough to train supervised models is not only costly, but also—especially in the case of opinions and emotions—difficult, due to the intrinsic subjectivity of the task BIBREF2 , BIBREF3   Therefore, research has focused on unsupervised methods enriched with information derived from lexica, which are manually created BIBREF3 , BIBREF4   Since go2009twitter have shown that happy and sad emoticons can be successfully used as signals for sentiment labels, distant supervision, i e  using some reasonably safe signals as proxies for automatically labelling training data BIBREF5 , has been used also for emotion recognition, for example exploiting both emoticons and Twitter hashtags BIBREF6 , but mainly towards creating emotion lexica  mohammad2015using use hashtags, experimenting also with highly fine-grained emotion sets (up to almost 600 emotion labels), to create the large Hashtag Emotion Lexicon  Emoticons are used as proxies also by hallsmarmulti, who use distributed vector representations to find which words are interchangeable with emoticons but also which emoticons are used in a similar context 
We take advantage of distant supervision by using Facebook reactions as proxies for emotion labels, which to the best of our knowledge hasn't been done yet, and we train a set of Support Vector Machine models for emotion recognition  Our models, differently from existing ones, exploit information which is acquired entirely automatically, and achieve competitive or even state-of-the-art results for some of the emotion labels on existing, standard evaluation datasets  For explanatory purposes, related work is discussed further and more in detail when we describe the benchmarks for evaluation (Section SECREF3 ) and when we compare our models to existing ones (Section SECREF5 )  We also explore and discuss how choosing different sets of Facebook pages as training data provides an intrinsic domain-adaptation method 
Facebook reactions as labels
For years, on Facebook people could leave comments to posts, and also “like” them, by using a thumbs-up feature to explicitly express a generic, rather underspecified, approval  A “like” could thus mean “I like what you said", but also “I like that you bring up such topic (though I find the content of the article you linked annoying)" 
In February 2016, after a short trial, Facebook made a more explicit reaction feature available world-wide  Rather than allowing for the underspecified “like” as the only wordless response to a post, a set of six more specific reactions was introduced, as shown in Figure FIGREF1 : Like, Love, Haha, Wow, Sad and Angry  We use such reactions as proxies for emotion labels associated to posts 
We collected Facebook posts and their corresponding reactions from public pages using the Facebook API, which we accessed via the Facebook-sdk python library  We chose different pages (and therefore domains and stances), aiming at a balanced and varied dataset, but we did so mainly based on intuition (see Section SECREF4 ) and with an eye to the nature of the datasets available for evaluation (see Section SECREF5 )  The choice of which pages to select posts from is far from trivial, and we believe this is actually an interesting aspect of our approach, as by using different Facebook pages one can intrinsically tackle the domain-adaptation problem (See Section SECREF6 for further discussion on this)  The final collection of Facebook pages for the experiments described in this paper is as follows: FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney 
Note that thankful was only available during specific time spans related to certain events, as Mother's Day in May 2016 
For each page, we downloaded the latest 1000 posts, or the maximum available if there are fewer, from February 2016, retrieving the counts of reactions for each post  The output is a JSON file containing a list of dictionaries with a timestamp, the post and a reaction vector with frequency values, which indicate how many users used that reaction in response to the post (Figure FIGREF3 )  The resulting emotion vectors must then be turned into an emotion label 
In the context of this experiment, we made the simple decision of associating to each post the emotion with the highest count, ignoring like as it is the default and most generic reaction people tend to use  Therefore, for example, to the first post in Figure FIGREF3 , we would associate the label sad, as it has the highest score (284) among the meaningful emotions we consider, though it also has non-zero scores for other emotions  At this stage, we didn't perform any other entropy-based selection of posts, to be investigated in future work 
Emotion datasets
Three datasets annotated with emotions are commonly used for the development and evaluation of emotion detection systems, namely the Affective Text dataset, the Fairy Tales dataset, and the ISEAR dataset  In order to compare our performance to state-of-the-art results, we have used them as well  In this Section, in addition to a description of each dataset, we provide an overview of the emotions used, their distribution, and how we mapped them to those we obtained from Facebook posts in Section SECREF7   A summary is provided in Table TABREF8 , which also shows, in the bottom row, what role each dataset has in our experiments: apart from the development portion of the Affective Text, which we used to develop our models (Section SECREF4 ), all three have been used as benchmarks for our evaluation 
Affective Text dataset
Task 14 at SemEval 2007 BIBREF7 was concerned with the classification of emotions and valence in news headlines  The headlines where collected from several news websites including Google news, The New York Times, BBC News and CNN  The used emotion labels were Anger, Disgust, Fear, Joy, Sadness, Surprise, in line with the six basic emotions of Ekman's standard model BIBREF8   Valence was to be determined as positive or negative  Classification of emotion and valence were treated as separate tasks  Emotion labels were not considered as mututally exclusive, and each emotion was assigned a score from 0 to 100  Training/developing data amounted to 250 annotated headlines (Affective development), while systems were evaluated on another 1000 (Affective test)  Evaluation was done using two different methods: a fine-grained evaluation using Pearson's r to measure the correlation between the system scores and the gold standard; and a coarse-grained method where each emotion score was converted to a binary label, and precision, recall, and f-score were computed to assess performance  As it is done in most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , we also treat this as a classification problem (coarse-grained)  This dataset has been extensively used for the evaluation of various unsupervised methods BIBREF2 , but also for testing different supervised learning techniques and feature portability BIBREF10  
Fairy Tales dataset
This is a dataset collected by alm2008affect, where about 1,000 sentences from fairy tales (by B  Potter, H C  Andersen and Grimm) were annotated with the same six emotions of the Affective Text dataset, though with different names: Angry, Disgusted, Fearful, Happy, Sad, and Surprised  In most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , only sentences where all annotators agreed are used, and the labels angry and disgusted are merged  We adopt the same choices 
ISEAR
The ISEAR (International Survey on Emotion Antecedents and Reactions BIBREF11 , BIBREF12 ) is a dataset created in the context of a psychology project of the 1990s, by collecting questionnaires answered by people with different cultural backgrounds  The main aim of this project was to gather insights in cross-cultural aspects of emotional reactions  Student respondents, both psychologists and non-psychologists, were asked to report situations in which they had experienced all of seven major emotions (joy, fear, anger, sadness, disgust, shame and guilt)  In each case, the questions covered the way they had appraised a given situation and how they reacted  The final dataset contains reports by approximately 3000 respondents from all over the world, for a total of 7665 sentences labelled with an emotion, making this the largest dataset out of the three we use 
Overview of datasets and emotions
We summarise datasets and emotion distribution from two viewpoints  First, because there are different sets of emotions labels in the datasets and Facebook data, we need to provide a mapping and derive a subset of emotions that we are going to use for the experiments  This is shown in Table TABREF8 , where in the “Mapped” column we report the final emotions we use in this paper: anger, joy, sadness, surprise  All labels in each dataset are mapped to these final emotions, which are therefore the labels we use for training and testing our models 
Second, the distribution of the emotions for each dataset is different, as can be seen in Figure FIGREF9  
In Figure FIGREF9 we also provide the distribution of the emotions anger, joy, sadness, surprise per Facebook page, in terms of number of posts (recall that we assign to a post the label corresponding to the majority emotion associated to it, see Section SECREF2 )  We can observe that for example pages about news tend to have more sadness and anger posts, while pages about cooking and tv-shows have a high percentage of joy posts  We will use this information to find the best set of pages for a given target domain (see Section SECREF5 ) 
Model
There are two main decisions to be taken in developing our model: (i) which Facebook pages to select as training data, and (ii) which features to use to train the model, which we discuss below  Specifically, we first set on a subset of pages and then experiment with features  Further exploration of the interaction between choice of pages and choice of features is left to future work, and partly discussed in Section SECREF6   For development, we use a small portion of the Affective data set described in Section SECREF4 , that is the portion that had been released as development set for SemEval's 2007 Task 14 BIBREF7 , which contains 250 annotated sentences (Affective development, Section SECREF4 )  All results reported in this section are on this dataset  The test set of Task 14 as well as the other two datasets described in Section SECREF3 will be used to evaluate the final models (Section SECREF4 ) 
Selecting Facebook pages
Although page selection is a crucial ingredient of this approach, which we believe calls for further and deeper, dedicated investigation, for the experiments described here we took a rather simple approach  First, we selected the pages that would provide training data based on intuition and availability, then chose different combinations according to results of a basic model run on development data, and eventually tested feature combinations, still on the development set 
For the sake of simplicity and transparency, we first trained an SVM with a simple bag-of-words model and default parameters as per the Scikit-learn implementation BIBREF13 on different combinations of pages  Based on results of the attempted combinations as well as on the distribution of emotions in the development dataset (Figure FIGREF9 ), we selected a best model (B-M), namely the combined set of Time, The Guardian and Disney, which yields the highest results on development data  Time and The Guardian perform well on most emotions but Disney helps to boost the performance for the Joy class 
Features
In selecting appropriate features, we mainly relied on previous work and intuition  We experimented with different combinations, and all tests were still done on Affective development, using the pages for the best model (B-M) described above as training data  Results are in Table TABREF20   Future work will further explore the simultaneous selection of features and page combinations 
We use a set of basic text-based features to capture the emotion class  These include a tf-idf bag-of-words feature, word (2-3) and character (2-5) ngrams, and features related to the presence of negation words, and to the usage of punctuation 
This feature is used in all unsupervised models as a source of information, and we mainly include it to assess its contribution, but eventually do not use it in our final model 
We used the NRC10 Lexicon because it performed best in the experiments by BIBREF10 , which is built around the emotions anger, anticipation, disgust, fear, joy, sadness, and surprise, and the valence values positive and negative  For each word in the lexicon, a boolean value indicating presence or absence is associated to each emotion  For a whole sentence, a global score per emotion can be obtained by summing the vectors for all content words of that sentence included in the lexicon, and used as feature 
As additional feature, we also included Word Embeddings, namely distributed representations of words in a vector space, which have been exceptionally successful in boosting performance in a plethora of NLP tasks  We use three different embeddings:
Google embeddings: pre-trained embeddings trained on Google News and obtained with the skip-gram architecture described in BIBREF14   This model contains 300-dimensional vectors for 3 million words and phrases 
Facebook embeddings: embeddings that we trained on our scraped Facebook pages for a total of 20,000 sentences  Using the gensim library BIBREF15 , we trained the embeddings with the following parameters: window size of 5, learning rate of 0 01 and dimensionality of 100  We filtered out words with frequency lower than 2 occurrences 
Retrofitted embeddings: Retrofitting BIBREF16 has been shown as a simple but efficient way of informing trained embeddings with additional information derived from some lexical resource, rather than including it directly at the training stage, as it's done for example to create sense-aware BIBREF17 or sentiment-aware BIBREF18 embeddings  In this work, we retrofit general embeddings to include information about emotions, so that emotion-similar words can get closer in space  Both the Google as well as our Facebook embeddings were retrofitted with lexical information obtained from the NRC10 Lexicon mentioned above, which provides emotion-similarity for each token  Note that differently from the previous two types of embeddings, the retrofitted ones do rely on handcrafted information in the form of a lexical resource 
Results on development set
We report precision, recall, and f-score on the development set  The average f-score is reported as micro-average, to better account for the skewed distribution of the classes as well as in accordance to what is usually reported for this task BIBREF19  
From Table TABREF20 we draw three main observations  First, a simple tf-idf bag-of-word mode works already very well, to the point that the other textual and lexicon-based features don't seem to contribute to the overall f-score (0 368), although there is a rather substantial variation of scores per class  Second, Google embeddings perform a lot better than Facebook embeddings, and this is likely due to the size of the corpus used for training  Retrofitting doesn't seem to help at all for the Google embeddings, but it does boost the Facebook embeddings, leading to think that with little data, more accurate task-related information is helping, but corpus size matters most  Third, in combination with embeddings, all features work better than just using tf-idf, but removing the Lexicon feature, which is the only one based on hand-crafted resources, yields even better results  Then our best model (B-M) on development data relies entirely on automatically obtained information, both in terms of training data as well as features 
Results
In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3  
Our B-M model relies on subsets of Facebook pages for training, which were chosen according to their performance on the development set as well as on the observation of emotions distribution on different pages and in the different datasets, as described in Section SECREF4   The feature set we use is our best on the development set, namely all the features plus Google-based embeddings, but excluding the lexicon  This makes our approach completely independent of any manual annotation or handcrafted resource  Our model's performance is compared to the following systems, for which results are reported in the referred literature  Please note that no other existing model was re-implemented, and results are those reported in the respective papers 
Discussion, conclusions and future work
We have explored the potential of using Facebook reactions in a distant supervised setting to perform emotion classification  The evaluation on standard benchmarks shows that models trained as such, especially when enhanced with continuous vector representations, can achieve competitive results without relying on any handcrafted resource  An interesting aspect of our approach is the view to domain adaptation via the selection of Facebook pages to be used as training data 
We believe that this approach has a lot of potential, and we see the following directions for improvement  Feature-wise, we want to train emotion-aware embeddings, in the vein of work by tang:14, and iacobacci2015sensembed  Retrofitting FB-embeddings trained on a larger corpus might also be successful, but would rely on an external lexicon 
The largest room for yielding not only better results but also interesting insights on extensions of this approach lies in the choice of training instances, both in terms of Facebook pages to get posts from, as well as in which posts to select from the given pages  For the latter, one could for example only select posts that have a certain length, ignore posts that are only quotes or captions to images, or expand posts by including content from linked html pages, which might provide larger and better contexts BIBREF23   Additionally, and most importantly, one could use an entropy-based measure to select only posts that have a strong emotion rather than just considering the majority emotion as training label  For the former, namely the choice of Facebook pages, which we believe deserves the most investigation, one could explore several avenues, especially in relation to stance-based issues BIBREF24   In our dataset, for example, a post about Chile beating Colombia in a football match during the Copa America had very contradictory reactions, depending on which side readers would cheer for  Similarly, the very same political event, for example, would get very different reactions from readers if it was posted on Fox News or The Late Night Show, as the target audience is likely to feel very differently about the same issue  This also brings up theoretical issues related more generally to the definition of the emotion detection task, as it's strongly dependent on personal traits of the audience  Also, in this work, pages initially selected on availability and intuition were further grouped into sets to make training data according to performance on development data, and label distribution  Another criterion to be exploited would be vocabulary overlap between the pages and the datasets 
Lastly, we could develop single models for each emotion, treating the problem as a multi-label task  This would even better reflect the ambiguity and subjectivity intrinsic to assigning emotions to text, where content could be at same time joyful or sad, depending on the reader 
Acknowledgements
In addition to the anonymous reviewers, we want to thank Lucia Passaro and Barbara Plank for insightful discussions, and for providing comments on draft versions of this paper 

ground_truth: Unanswerable

 Paraphrase Database (PPDB) ,  book corpus

Unanswerable

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

Please answer the questions based on the text provided below. What was their performance on emotion detection?
Context 4:  We train the improved projected gradient model proposed in BIBREF20 to obtain word vectors (called OIWE-IPG) using the same corpus we use to train GloVe and our proposed method  Using the methods proposed in BIBREF23 , BIBREF26 , BIBREF24 on our baseline GloVe embeddings, we obtain SOV, SPINE and Parsimax (orthogonal) word representations, respectively  We train all the models with the proposed parameters  However, in BIBREF26 , the authors show results for a relatively small vocabulary of 15,000 words  When we trained their model on our baseline GloVe embeddings with a large vocabulary of size 287,847, the resulting vectors performed significantly poor on word similarity tasks compared to the results presented in their paper  In addition, Parsimax (orthogonal) word vectors obtained using method in BIBREF26 are nearly identical to the baseline vectors (i e  learned orthogonal transformation matrix is very close to identity)  Therefore, Parsimax (orthogonal) yields almost same results with baseline vectors in all evaluations  We evaluate the interpretability of the resulting embeddings qualitatively and quantitatively  We also test the performance of the embeddings on word similarity and word analogy tests 
In our experiments, vocabulary size is close to 300,000 while only 16,242 unique words of the vocabulary are present in the concept groups  Furthermore, only dimensions that correspond to the concept group of the word will be updated due to the additional cost term  Given that these concept words can belong to multiple concept groups (2 on average), only 33,319 parameters are updated  There are 90 million individual parameters present for the 300,000 word vectors of size 300  Of these parameters, only approximately 33,000 are updated by the additional cost term 
Qualitative Evaluation for Interpretability
In Fig  FIGREF13 , we demonstrate the particular way in which the proposed algorithm ( SECREF4 ) influences the vector representation distributions  Specifically, we consider, for illustration, the 32nd dimension values for the original GloVe algorithm and our modified version, restricting the plots to the top-1000 words with respect to their frequency ranks for clarity of presentation  In Fig  FIGREF13 , the words in the horizontal axis are sorted in descending order with respect to the values at the 32nd dimension of their word embedding vectors coming from the original GloVe algorithm  The dimension values are denoted with blue and red/green markers for the original and the proposed algorithms, respectively  Additionally, the top-50 words that achieve the greatest 32nd dimension values among the considered 1000 words are emphasized with enlarged markers, along with text annotations  In the presented simulation of the proposed algorithm, the 32nd dimension values are encoded with the concept JUDGMENT, which is reflected as an increase in the dimension values for words such as committee, academy, and article  We note that these words (red) are not part of the pre-determined word-group for the concept JUDGMENT, in contrast to words such as award, review and account (green) which are  This implies that the increase in the corresponding dimension values seen for these words is attributable to the joint effect of the first term in ( SECREF4 ) which is inherited from the original GloVe algorithm, in conjunction with the remaining terms in the proposed objective expression ( SECREF4 )  This experiment illustrates that the proposed algorithm is able to impart the concept of JUDGMENT on its designated vector dimension above and beyond the supplied list of words belonging to the concept word-group for that dimension  We also present the list of words with the greatest dimension value for the dimensions 11, 13, 16, 31, 36, 39, 41, 43 and 79 in Table TABREF11   These dimensions are aligned/imparted with the concepts that are given in the column headers  In Table TABREF11 , the words that are highlighted with green denote the words that exist in the corresponding word-group obtained from Roget's Thesaurus (and are thus explicitly forced to achieve increased dimension values), while the red words denote the words that achieve increased dimension values by virtue of their cooccurrence statistics with the thesaurus-based words (indirectly, without being explicitly forced)  This again illustrates that a semantic concept can indeed be coded to a vector dimension provided that a sensible lexical resource is used to guide semantically related words to the desired vector dimension via the proposed objective function in ( SECREF4 )  Even the words that do not appear in, but are semantically related to, the word-groups that we formed using Roget's Thesaurus, are indirectly affected by the proposed algorithm  They also reflect the associated concepts at their respective dimensions even though the objective functions for their particular vectors are not modified  This point cannot be overemphasized  Although the word-groups extracted from Roget's Thesaurus impose a degree of supervision to the process, the fact that the remaining words in the entire vocabulary are also indirectly affected makes the proposed method a semi-supervised approach that can handle words that are not in these chosen word-groups  A qualitative example of this result can be seen in the last column of Table TABREF11   It is interesting to note the appearance of words such as guerilla, insurgency, mujahideen, Wehrmacht and Luftwaffe in addition to the more obvious and straightforward army, soldiers and troops, all of which are not present in the associated word-group WARFARE 
Most of the dimensions we investigated exhibit similar behaviour to the ones presented in Table TABREF11   Thus generally speaking, we can say that the entries in Table TABREF11 are representative of the great majority  However, we have also specifically looked for dimensions that make less sense and determined a few such dimensions which are relatively less satisfactory  These less satisfactory examples are given in Table TABREF14   These examples are also interesting in that they shed insight into the limitations posed by polysemy and existence of very rare outlier words 
Quantitative Evaluation for Interpretability
One of the main goals of this study is to improve the interpretability of dense word embeddings by aligning the dimensions with predefined concepts from a suitable lexicon  A quantitative measure is required to reliably evaluate the achieved improvement  One of the methods proposed to measure the interpretability is the word intrusion test BIBREF41   But, this method is expensive to apply since it requires evaluations from multiple human evaluators for each embedding dimension  In this study, we use a semantic category-based approach based on the method and category dataset (SEMCAT) introduced in BIBREF27 to quantify interpretability  Specifically, we apply a modified version of the approach presented in BIBREF40 in order to consider possible sub-groupings within the categories in SEMCAT  Interpretability scores are calculated using Interpretability Score (IS) as given below:
DISPLAYFORM0
In ( EQREF17 ), INLINEFORM0 and INLINEFORM1 represents the interpretability scores in the positive and negative directions of the INLINEFORM2 dimension ( INLINEFORM3 , INLINEFORM4 number of dimensions in the embedding space) of word embedding space for the INLINEFORM5 category ( INLINEFORM6 , INLINEFORM7 is number of categories in SEMCAT, INLINEFORM8 ) in SEMCAT respectively  INLINEFORM9 is the set of words in the INLINEFORM10 category in SEMCAT and INLINEFORM11 is the number of words in INLINEFORM12   INLINEFORM13 corresponds to the minimum number of words required to construct a semantic category (i e  represent a concept)  INLINEFORM14 represents the set of INLINEFORM15 words that have the highest ( INLINEFORM16 ) and lowest ( INLINEFORM17 ) values in INLINEFORM18 dimension of the embedding space  INLINEFORM19 is the intersection operator and INLINEFORM20 is the cardinality operator (number of elements) for the intersecting set  In ( EQREF17 ), INLINEFORM21 gives the interpretability score for the INLINEFORM22 dimension and INLINEFORM23 gives the average interpretability score of the embedding space 
Fig  FIGREF18 presents the measured average interpretability scores across dimensions for original GloVe embeddings, for the proposed method and for the other four methods we compare, along with a randomly generated embedding  Results are calculated for the parameters INLINEFORM0 and INLINEFORM1   Our proposed method significantly improves the interpretability for all INLINEFORM2 compared to the original GloVe approach  Our proposed method is second to only SPINE in increasing interpretability  However, as we will experimentally demonstrate in the next subsection, in doing this, SPINE almost entirely destroys the underlying semantic structure of the word embeddings, which is the primary function of a word embedding 
The proposed method and interpretability measurements are both based on utilizing concepts represented by word-groups  Therefore it is expected that there will be higher interpretability scores for some of the dimensions for which the imparted concepts are also contained in SEMCAT  However, by design, word groups that they use are formed by using different sources and are independent  Interpretability measurements use SEMCAT while our proposed method utilizes Roget's Thesaurus 
Intrinsic Evaluation of the Embeddings
It is necessary to show that the semantic structure of the original embedding has not been damaged or distorted as a result of aligning the dimensions with given concepts, and that there is no substantial sacrifice involved from the performance that can be obtained with the original GloVe  To check this, we evaluate performances of the proposed embeddings on word similarity BIBREF42 and word analogy BIBREF0 tests  We compare the results with the original embeddings and the three alternatives excluding Parsimax BIBREF26 since orthogonal transformations will not affect the performance of the original embeddings on these tests 
Word similarity test measures the correlation between word similarity scores obtained from human evaluation (i e  true similarities) and from word embeddings (usually using cosine similarity)  In other words, this test quantifies how well the embedding space reflects human judgements in terms of similarities between different words  The correlation scores for 13 different similarity test sets are reported in Table TABREF20   We observe that, let alone a reduction in performance, the obtained scores indicate an almost uniform improvement in the correlation values for the proposed algorithm, outperforming all the alternatives in almost all test sets  Categories from Roget's thesaurus are groupings of words that are similar in some sense which the original embedding algorithm may fail to capture  These test results signify that the semantic information injected into the algorithm by the additional cost term is significant enough to result in a measurable improvement  It should also be noted that scores obtained by SPINE is unacceptably low on almost all tests indicating that it has achieved its interpretability performance at the cost of losing its semantic functions 
Word analogy test is introduced in BIBREF1 and looks for the answers of the questions that are in the form of "X is to Y, what Z is to ?" by applying simple arithmetic operations to vectors of words X, Y and Z  We present precision scores for the word analogy tests in Table TABREF21   It can be seen that the alternative approaches that aim to improve interpretability, have poor performance on the word analogy tests  However, our proposed method has comparable performance with the original GloVe embeddings  Our method outperforms GloVe in semantic analogy test set and in overall results, while GloVe performs slightly better in syntactic test set  This comparable performance is mainly due to the cost function of our proposed method that includes the original objective of the GloVe 
To investigate the effect of the additional cost term on the performance improvement in the semantic analogy test, we present Table TABREF22   In particular, we present results for the cases where i) all questions in the dataset are considered, ii) only the questions that contains at least one concept word are considered, iii) only the questions that consist entirely of concept words are considered  We note specifically that for the last case, only a subset of the questions under the semantic category family txt ended up being included  We observe that for all three scenarios, our proposed algorithm results in an improvement in the precision scores  However, the greatest performance increase is seen for the last scenario, which underscores the extent to which the semantic features captured by embeddings can be improved with a reasonable selection of the lexical resource from which the concept word-groups were derived 
Conclusion
We presented a novel approach to impart interpretability into word embeddings  We achieved this by encouraging different dimensions of the vector representation to align with predefined concepts, through the addition of an additional cost term in the optimization objective of the GloVe algorithm that favors a selective increase for a pre-specified input of concept words along each dimension 
We demonstrated the efficacy of this approach by applying qualitative and quantitative evaluations for interpretability  We also showed via standard word-analogy and word-similarity tests that the semantic coherence of the original vector space is preserved, even slightly improved  We have also performed and reported quantitative comparisons with several other methods for both interpretabilty increase and preservation of semantic coherence  Upon inspection of Fig  FIGREF18 and Tables TABREF20 , TABREF21 , and TABREF22 altogether, it should be noted that our proposed method achieves both of the objectives simultaneously, increased interpretability and preservation of the intrinsic semantic structure 
An important point was that, while it is expected for words that are already included in the concept word-groups to be aligned together since their dimensions are directly updated with the proposed cost term, it was also observed that words not in these groups also aligned in a meaningful manner without any direct modification to their cost function  This indicates that the cost term we added works productively with the original cost function of GloVe to handle words that are not included in the original concept word-groups, but are semantically related to those word-groups  The underlying mechanism can be explained as follows  While the outside lexical resource we introduce contains a relatively small number of words compared to the total number of words, these words and the categories they represent have been carefully chosen and in a sense, "densely span" all the words in the language  By saying "span", we mean they cover most of the concepts and ideas in the language without leaving too many uncovered areas  With "densely" we mean all areas are covered with sufficient strength  In other words, this subset of words is able to constitute a sufficiently strong skeleton, or scaffold  Now remember that GloVe works to align or bring closer related groups of words, which will include words from the lexical source  So the joint action of aligning the words with the predefined categories (introduced by us) and aligning related words (handled by GloVe) allows words not in the lexical groups to also be aligned meaningfully  We may say that the non-included words are "pulled along" with the included words by virtue of the "strings" or "glue" that is provided by GloVe  In numbers, the desired effect is achieved by manipulating less than only 0 05% of parameters of the entire word vectors  Thus, while there is a degree of supervision coming from the external lexical resource, the rest of the vocabulary is also aligned indirectly in an unsupervised way  This may be the reason why, unlike earlier proposed approaches, our method is able to achieve increasing interpretability without destroying underlying semantic structure, and consequently without sacrificing performance in benchmark tests 
Upon inspecting the 2nd column of Table TABREF14 , where qualitative results for concept TASTE are presented, another insight regarding the learning mechanism of our proposed approach can be made  Here it seems understandable that our proposed approach, along with GloVe, brought together the words taste and polish, and then the words Polish and, for instance, Warsaw are brought together by GloVe  These examples are interesting in that they shed insight into how GloVe works and the limitations posed by polysemy  It should be underlined that the present approach is not totally incapable of handling polysemy, but cannot do so perfectly  Since related words are being clustered, sufficiently well-connected words that do not meaningfully belong along with others will be appropriately "pulled away" from that group by several words, against the less effective, inappropriate pull of a particular word  Even though polish with lowercase "p" belongs where it is, it is attracting Warsaw to itself through polysemy and this is not meaningful  Perhaps because Warsaw is not a sufficiently well-connected word, it ends being dragged along, although words with greater connectedness to a concept group might have better resisted such inappropriate attractions 
In this study, we used the GloVe algorithm as the underlying dense word embedding scheme to demonstrate our approach  However, we stress that it is possible for our approach to be extended to other word embedding algorithms which have a learning routine consisting of iterations over cooccurrence records, by making suitable adjustments in the objective function  Since word2vec model is also based on the coocurrences of words in a sliding window through a large corpus, we expect that our approach can also be applied to word2vec after making suitable adjustments, which can be considered as an immediate future work for our approach  Although the semantic concepts are encoded in only one direction (positive) within the embedding dimensions, it might be beneficial to pursue future work that also encodes opposite concepts, such as good and bad, in two opposite directions of the same dimension 
The proposed methodology can also be helpful in computational cross-lingual studies, where the similarities are explored across the vector spaces of different languages BIBREF43 , BIBREF44  

ground_truth: Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively.