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. Where does the ancient Chinese dataset come from?
Context 1: Introduction
Ancient Chinese is the writing language in ancient China  It is a treasure of Chinese culture which brings together the wisdom and ideas of the Chinese nation and chronicles the ancient cultural heritage of China  Learning ancient Chinese not only helps people to understand and inherit the wisdom of the ancients, but also promotes people to absorb and develop Chinese culture 
However, it is difficult for modern people to read ancient Chinese  Firstly, compared with modern Chinese, ancient Chinese is more concise and shorter  The grammatical order of modern Chinese is also quite different from that of ancient Chinese  Secondly, most modern Chinese words are double syllables, while the most of the ancient Chinese words are monosyllabic  Thirdly, there is more than one polysemous phenomenon in ancient Chinese  In addition, manual translation has a high cost  Therefore, it is meaningful and useful to study the automatic translation from ancient Chinese to modern Chinese  Through ancient-modern Chinese translation, the wisdom, talent and accumulated experience of the predecessors can be passed on to more people 
Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 has achieved remarkable performance on many bilingual translation tasks  It is an end-to-end learning approach for machine translation, with the potential to show great advantages over the statistic machine translation (SMT) systems  However, NMT approach has not been widely applied to the ancient-modern Chinese translation task  One of the main reasons is the limited high-quality parallel data resource 
The most popular method of acquiring translation examples is bilingual text alignment BIBREF5   This kind of method can be classified into two types: lexical-based and statistical-based  The lexical-based approaches BIBREF6 , BIBREF7 focus on lexical information, which utilize the bilingual dictionary BIBREF8 , BIBREF9 or lexical features  Meanwhile, the statistical-based approaches BIBREF10 , BIBREF11 rely on statistical information, such as sentence length ratio in two languages and align mode probability 
However, these methods are designed for other bilingual language pairs that are written in different language characters (e g  English-French, Chinese-Japanese)  The ancient-modern Chinese has some characteristics that are quite different from other language pairs  For example, ancient and modern Chinese are both written in Chinese characters, but ancient Chinese is highly concise and its syntactical structure is different from modern Chinese  The traditional methods do not take these characteristics into account  In this paper, we propose an effective ancient-modern Chinese text alignment method at the level of clause based on the characteristics of these two languages  The proposed method combines both lexical-based information and statistical-based information, which achieves 94 2 F1-score on Test set  Recently, a simple longest common subsequence based approach for ancient-modern Chinese sentence alignment is proposed in BIBREF12   Our experiments showed that our proposed alignment approach performs much better than their method 
We apply the proposed method to create a large translation parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  Furthermore, we test SMT models and various NMT models on the created dataset and provide a strong baseline for this task 
Overview
There are four steps to build the ancient-modern Chinese translation dataset: (i) The parallel corpus crawling and cleaning  (ii) The paragraph alignment  (iii) The clause alignment based on aligned paragraphs  (iv) Augmenting data by merging aligned adjacent clauses  The most critical step is the third step 
Clause Alignment
In the clause alignment step, we combine both statistical-based and lexical-based information to measure the score for each possible clause alignment between ancient and modern Chinese strings  The dynamic programming is employed to further find overall optimal alignment paragraph by paragraph  According to the characteristics of the ancient and modern Chinese languages, we consider the following factors to measure the alignment score INLINEFORM0 between a bilingual clause pair:
Lexical Matching  The lexical matching score is used to calculate the matching coverage of the ancient clause INLINEFORM0   It contains two parts: exact matching and dictionary matching  An ancient Chinese character usually corresponds to one or more modern Chinese words  In the first part, we carry out Chinese Word segmentation to the modern Chinese clause INLINEFORM1   Then we match the ancient characters and modern words in the order from left to right  In further matching, the words that have been matched will be deleted from the original clauses 
However, some ancient characters do not appear in its corresponding modern Chinese words  An ancient Chinese dictionary is employed to address this issue  We preprocess the ancient Chinese dictionary and remove the stop words  In this dictionary matching step, we retrieve the dictionary definition of each unmatched ancient character and use it to match the remaining modern Chinese words  To reduce the impact of universal word matching, we use Inverse Document Frequency (IDF) to weight the matching words  The lexical matching score is calculated as: DISPLAYFORM0
The above equation is used to calculate the matching coverage of the ancient clause INLINEFORM0   The first term of equation ( EQREF8 ) represents exact matching score  INLINEFORM1 denotes the length of INLINEFORM2 , INLINEFORM3 denotes each ancient character in INLINEFORM4 , and the indicator function INLINEFORM5 indicates whether the character INLINEFORM6 can match the words in the clause INLINEFORM7   The second term is dictionary matching score  Here INLINEFORM8 and INLINEFORM9 represent the remaining unmatched strings of INLINEFORM10 and INLINEFORM11 , respectively  INLINEFORM12 denotes the INLINEFORM13 -th character in the dictionary definition of the INLINEFORM14 and its IDF score is denoted as INLINEFORM15   The INLINEFORM16 is a predefined parameter which is used to normalize the IDF score  We tuned the value of this parameter on the Dev set 
Statistical Information  Similar to BIBREF11 and BIBREF6 , the statistical information contains alignment mode and length information  There are many alignment modes between ancient and modern Chinese languages  If one ancient Chinese clause aligns two adjacent modern Chinese clauses, we call this alignment as 1-2 alignment mode  We show some examples of different alignment modes in Figure FIGREF9   In this paper, we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes which account for INLINEFORM0 of the Dev set  We estimate the probability Pr INLINEFORM1 n-m INLINEFORM2 of each alignment mode n-m on the Dev set  To utilize length information, we make an investigation on length correlation between these two languages  Based on the assumption of BIBREF11 that each character in one language gives rise to a random number of characters in the other language and those random variables INLINEFORM3 are independent and identically distributed with a normal distribution, we estimate the mean INLINEFORM4 and standard deviation INLINEFORM5 from the paragraph aligned parallel corpus  Given a clause pair INLINEFORM6 , the statistical information score can be calculated by: DISPLAYFORM0
where INLINEFORM0 denotes the normal distribution probability density function 
Edit Distance  Because ancient and modern Chinese are both written in Chinese characters, we also consider using the edit distance  It is a way of quantifying the dissimilarity between two strings by counting the minimum number of operations (insertion, deletion, and substitution) required to transform one string into the other  Here we define the edit distance score as: DISPLAYFORM0
Dynamic Programming  The overall alignment score for each possible clause alignment is as follows: DISPLAYFORM0
Here INLINEFORM0 and INLINEFORM1 are pre-defined interpolation factors  We use dynamic programming to find the overall optimal alignment paragraph by paragraph  Let INLINEFORM2 be total alignment scores of aligning the first to INLINEFORM3 -th ancient Chinese clauses with the first to to INLINEFORM4 -th modern Chinese clauses, and the recurrence then can be described as follows: DISPLAYFORM0
Where INLINEFORM0 denotes concatenate clause INLINEFORM1 to clause INLINEFORM2   As we discussed above, here we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes 
Ancient-Modern Chinese Dataset
Data Collection  To build the large ancient-modern Chinese dataset, we collected 1 7K bilingual ancient-modern Chinese articles from the internet  More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era  They used plain and accurate words to express what happened at that time, and thus ensure the generality of the translated materials 
Paragraph Alignment  To further ensure the quality of the new dataset, the work of paragraph alignment is manually completed  After data cleaning and manual paragraph alignment, we obtained 35K aligned bilingual paragraphs 
Clause Alignment  We applied our clause alignment algorithm on the 35K aligned bilingual paragraphs and obtained 517K aligned bilingual clauses  The reason we use clause alignment algorithm instead of sentence alignment is because we can construct more aligned sentences more flexibly and conveniently  To be specific, we can get multiple additional sentence level bilingual pairs by “data augmentation” 
Data Augmentation  We augmented the data in the following way: Given an aligned clause pair, we merged its adjacent clause pairs as a new sample pair  For example, suppose we have three adjacent clause level bilingual pairs: ( INLINEFORM0 , INLINEFORM1 ), ( INLINEFORM2 , INLINEFORM3 ), and ( INLINEFORM4 , INLINEFORM5 )  We can get some additional sentence level bilingual pairs, such as: ( INLINEFORM6 , INLINEFORM7 ) and ( INLINEFORM8 , INLINEFORM9 )  Here INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 are adjacent clauses in the original paragraph, and INLINEFORM13 denotes concatenate clause INLINEFORM14 to clause INLINEFORM15   The advantage of using this data augmentation method is that compared with only using ( INLINEFORM16 , INLINEFORM17 ) as the training data, we can also use ( INLINEFORM18 , INLINEFORM19 ) and ( INLINEFORM20 , INLINEFORM21 ) as the training data, which can provide richer supervision information for the model and make the model learn the align information between the source language and the target language better  After the data augmentation, we filtered the sentences which are longer than 50 or contain more than four clause pairs 
Dataset Creation  Finally, we split the dataset into three sets: training (Train), development (Dev) and testing (Test)  Note that the unaugmented dataset contains 517K aligned bilingual clause pairs from 35K aligned bilingual paragraphs  To keep all the sentences in different sets come from different articles, we split the 35K aligned bilingual paragraphs into Train, Dev and Test sets following these ratios respectively: 80%, 10%, 10%  Before data augmentation, the unaugmented Train set contains INLINEFORM0 aligned bilingual clause pairs from 28K aligned bilingual paragraphs  Then we augmented the Train, Dev and Test sets respectively  Note that the augmented Train, Dev and Test sets also contain the unaugmented data  The statistical information of the three data sets is shown in Table TABREF17   We show some examples of data in Figure FIGREF14  
RNN-based NMT model
We first briefly introduce the RNN based Neural Machine Translation (RNN-based NMT) model  The RNN-based NMT with attention mechanism BIBREF0 has achieved remarkable performance on many translation tasks  It consists of encoder and decoder part 
We firstly introduce the encoder part  The input word sequence of source language are individually mapped into a INLINEFORM0 -dimensional vector space INLINEFORM1   Then a bi-directional RNN BIBREF15 with GRU BIBREF16 or LSTM BIBREF17 cell converts these vectors into a sequences of hidden states INLINEFORM2  
For the decoder part, another RNN is used to generate target sequence INLINEFORM0   The attention mechanism BIBREF0 , BIBREF18 is employed to allow the decoder to refer back to the hidden state sequence and focus on a particular segment  The INLINEFORM1 -th hidden state INLINEFORM2 of decoder part is calculated as: DISPLAYFORM0
Here g INLINEFORM0 is a linear combination of attended context vector c INLINEFORM1 and INLINEFORM2 is the word embedding of (i-1)-th target word: DISPLAYFORM0
The attended context vector c INLINEFORM0 is computed as a weighted sum of the hidden states of the encoder: DISPLAYFORM0
The probability distribution vector of the next word INLINEFORM0 is generated according to the following: DISPLAYFORM0
We take this model as the basic RNN-based NMT model in the following experiments 
Transformer-NMT
Recently, the Transformer model BIBREF4 has made remarkable progress in machine translation  This model contains a multi-head self-attention encoder and a multi-head self-attention decoder 
As proposed by BIBREF4 , an attention function maps a query and a set of key-value pairs to an output, where the queries INLINEFORM0 , keys INLINEFORM1 , and values INLINEFORM2 are all vectors  The input consists of queries and keys of dimension INLINEFORM3 , and values of dimension INLINEFORM4   The attention function is given by: DISPLAYFORM0
Multi-head attention mechanism projects queries, keys and values to INLINEFORM0 different representation subspaces and calculates corresponding attention  The attention function outputs are concatenated and projected again before giving the final output  Multi-head attention allows the model to attend to multiple features at different positions 
The encoder is composed of a stack of INLINEFORM0 identical layers  Each layer has two sub-layers: multi-head self-attention mechanism and position-wise fully connected feed-forward network  Similarly, the decoder is also composed of a stack of INLINEFORM1 identical layers  In addition to the two sub-layers in each encoder layer, the decoder contains a third sub-layer which performs multi-head attention over the output of the encoder stack (see more details in BIBREF4 ) 
Experiments
Our experiments revolve around the following questions: Q1: As we consider three factors for clause alignment, do all these factors help? How does our method compare with previous methods? Q2: How does the NMT and SMT models perform on this new dataset we build?
Clause Alignment Results (Q1)
In order to evaluate our clause alignment algorithm, we manually aligned bilingual clauses from 37 bilingual ancient-modern Chinese articles, and finally got 4K aligned bilingual clauses as the Test set and 2K clauses as the Dev set 
Metrics  We used F1-score and precision score as the evaluation metrics  Suppose that we get INLINEFORM0 bilingual clause pairs after running the algorithm on the Test set, and there are INLINEFORM1 bilingual clause pairs of these INLINEFORM2 pairs are in the ground truth of the Test set, the precision score is defined as INLINEFORM3 (the algorithm gives INLINEFORM4 outputs, INLINEFORM5 of which are correct)  And suppose that the ground truth of the Test set contains INLINEFORM6 bilingual clause pairs, the recall score is INLINEFORM7 (there are INLINEFORM8 ground truth samples, INLINEFORM9 of which are output by the algorithm), then the F1-score is INLINEFORM10  
Baselines  Since the related work BIBREF10 , BIBREF11 can be seen as the ablation cases of our method (only statistical score INLINEFORM0 with dynamic programming), we compared the full proposed method with its variants on the Test set for ablation study  In addition, we also compared our method with the longest common subsequence (LCS) based approach proposed by BIBREF12   To the best of our knowledge, BIBREF12 is the latest related work which are designed for Ancient-Modern Chinese alignment 
Hyper-parameters  For the proposed method, we estimated INLINEFORM0 and INLINEFORM1 on all aligned paragraphs  The probability Pr INLINEFORM2 n-m INLINEFORM3 of each alignment mode n-m was estimated on the Dev set  For the hyper-parameters INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , the grid search was applied to tune them on the Dev set  In order to show the effect of hyper-parameters INLINEFORM7 , INLINEFORM8 , and INLINEFORM9 , we reported the results of various hyper-parameters on the Dev set in Table TABREF26   Based on the results of grid search on the Dev set, we set INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 in the following experiment  The Jieba Chinese text segmentation is employed for modern Chinese word segmentation 
Results  The results on the Test set are shown in Table TABREF28 , the abbreviation w/o means removing a particular part from the setting  From the results, we can see that the lexical matching score is the most important among these three factors, and statistical information score is more important than edit distance score  Moreover, the dictionary term in lexical matching score significantly improves the performance  From these results, we obtain the best setting that involves all these three factors  We used this setting for dataset creation  Furthermore, the proposed method performs much better than LCS BIBREF12  
Translation Results (Q2)
In this experiment, we analyzed and compared the performance of the SMT and various NMT models on our built dataset  To verify the effectiveness of our data augmented method  We trained the NMT and SMT models on both unaugmented dataset (including 0 46M training pairs) and augmented dataset, and test all the models on the same Test set which is augmented  The models to be tested and their configurations are as follows:
SMT  The state-of-art Moses toolkit BIBREF19 was used to train SMT model  We used KenLM BIBREF20 to train a 5-gram language model, and the GIZA++ toolkit to align the data 
RNN-based NMT  The basic RNN-based NMT model is based on BIBREF0 which is introduced above  Both the encoder and decoder used 2-layer RNN with 1024 LSTM cells, and the encoder is a bi-directional RNN  The batch size, threshold of element-wise gradient clipping and initial learning rate of Adam optimizer BIBREF21 were set to 128, 5 0 and 0 001  When trained the model on augmented dataset, we used 4-layer RNN  Several techniques were investigated to train the model, including layer-normalization BIBREF22 , RNN-dropout BIBREF23 , and learning rate decay BIBREF1   The hyper-parameters were chosen empirically and adjusted in the Dev set  Furthermore, we tested the basic NMT model with several techniques, such as target language reversal BIBREF24 (reversing the order of the words in all target sentences, but not source sentences), residual connection BIBREF25 and pre-trained word2vec BIBREF26   For word embedding pre-training, we collected an external ancient corpus which contains INLINEFORM0 134M tokens 
Transformer-NMT  We also trained the Transformer model BIBREF4 which is a strong baseline of NMT on both augmented and unaugmented parallel corpus  The training configuration of the Transformer model is shown in Table TABREF32   The hyper-parameters are set based on the settings in the paper BIBREF4 and the sizes of our training sets 
For the evaluation, we used the average of 1 to 4 gram BLEUs multiplied by a brevity penalty BIBREF27 which computed by multi-bleu perl in Moses as metrics  The results are reported in Table TABREF34   For RNN-based NMT, we can see that target language reversal, residual connection, and word2vec can further improve the performance of the basic RNN-based NMT model  However, we find that word2vec and reversal tricks seem no obvious improvement when trained the RNN-based NMT and Transformer models on augmented parallel corpus  For SMT, it performs better than NMT models when they were trained on the unaugmented dataset  Nevertheless, when trained on the augmented dataset, both the RNN-based NMT model and Transformer based NMT model outperform the SMT model  In addition, as with other translation tasks BIBREF4 , the Transformer also performs better than RNN-based NMT 
Because the Test set contains both augmented and unaugmented data, it is not surprising that the RNN-based NMT model and Transformer based NMT model trained on unaugmented data would perform poorly  In order to further verify the effect of data augmentation, we report the test results of the models on only unaugmented test data (including 48K test pairs) in Table TABREF35   From the results, it can be seen that the data augmentation can still improve the models 
Analysis
The generated samples of various models are shown in Figure FIGREF36   Besides BLEU scores, we analyze these examples from a human perspective and draw some conclusions  At the same time, we design different metrics and evaluate on the whole Test set to support our conclusions as follows:
On the one hand, we further compare the translation results from the perspective of people  We find that although the original meaning can be basically translated by SMT, its translation results are less smooth when compared with the other two NMT models (RNN-based NMT and Transformer)  For example, the translations of SMT are usually lack of auxiliary words, conjunctions and function words, which is not consistent with human translation habits  To further confirm this conclusion, the average length of the translation results of the three models are measured (RNN-based NMT:17 12, SMT:15 50, Transformer:16 78, Reference:16 47)  We can see that the average length of the SMT outputs is shortest, and the length gaps between the SMT outputs and the references are largest  Meanwhile, the average length of the sentences translated by Transformer is closest to the average length of references  These results indirectly verify our point of view, and show that the NMT models perform better than SMT in this task 
On the other hand, there still exists some problems to be solved  We observe that translating proper nouns and personal pronouns (such as names, place names and ancient-specific appellations) is very difficult for all of these models  For instance, the ancient Chinese appellation `Zhen' should be translated into `Wo' in modern Chinese  Unfortunately, we calculate the accurate rate of some special words (such as `Zhen',`Chen' and `Gua'), and find that this rate is very low (the accurate rate of translating `Zhen' are: RNN-based NMT:0 14, SMT:0 16, Transformer:0 05)  We will focus on this issue in the future 
Conclusion and Future Work
We propose an effective ancient-modern Chinese clause alignment method which achieves 94 2 F1-score on Test set  Based on it, we build a large scale parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  In addition, we test the performance of the SMT and various NMT models on our built dataset and provide a strong NMT baseline for this task which achieves 27 16 BLEU score (4-gram)  We further analyze the performance of the SMT and various NMT models and summarize some specific problems that machine translation models will encounter when translating ancient Chinese 
For the future work, firstly, we are going to expand the dataset using the proposed method continually  Secondly, we will focus on solving the problem of proper noun translation and improve the translation system according to the features of ancient Chinese translation  Finally, we plan to introduce some techniques of statistical translation into neural machine translation to improve the performance 
This work is supported by National Natural Science Fund for Distinguished Young Scholar (Grant No  61625204) and partially supported by the State Key Program of National Science Foundation of China (Grant Nos  61836006 and 61432014) 
Context 2: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/ Deep neural networks have been widely used in text classification and have achieved promising results BIBREF0 , BIBREF1 , BIBREF2   Most focus on content information and use models such as convolutional neural networks (CNN) BIBREF3 or recursive neural networks BIBREF4   However, for user-generated posts on social media like Facebook or Twitter, there is more information that should not be ignored  On social media platforms, a user can act either as the author of a post or as a reader who expresses his or her comments about the post 
In this paper, we classify posts taking into account post authorship, likes, topics, and comments  In particular, users and their “likes” hold strong potential for text mining  For example, given a set of posts that are related to a specific topic, a user's likes and dislikes provide clues for stance labeling  From a user point of view, users with positive attitudes toward the issue leave positive comments on the posts with praise or even just the post's content; from a post point of view, positive posts attract users who hold positive stances  We also investigate the influence of topics: different topics are associated with different stance labeling tendencies and word usage  For example we discuss women's rights and unwanted babies on the topic of abortion, but we criticize medicine usage or crime when on the topic of marijuana BIBREF5   Even for posts on a specific topic like nuclear power, a variety of arguments are raised: green energy, radiation, air pollution, and so on  As for comments, we treat them as additional text information  The arguments in the comments and the commenters (the users who leave the comments) provide hints on the post's content and further facilitate stance classification 
In this paper, we propose the user-topic-comment neural network (UTCNN), a deep learning model that utilizes user, topic, and comment information  We attempt to learn user and topic representations which encode user interactions and topic influences to further enhance text classification, and we also incorporate comment information  We evaluate this model on a post stance classification task on forum-style social media platforms  The contributions of this paper are as follows: 1  We propose UTCNN, a neural network for text in modern social media channels as well as legacy social media, forums, and message boards — anywhere that reveals users, their tastes, as well as their replies to posts  2  When classifying social media post stances, we leverage users, including authors and likers  User embeddings can be generated even for users who have never posted anything  3  We incorporate a topic model to automatically assign topics to each post in a single topic dataset  4  We show that overall, the proposed method achieves the highest performance in all instances, and that all of the information extracted, whether users, topics, or comments, still has its contributions 
Extra-Linguistic Features for Stance Classification
In this paper we aim to use text as well as other features to see how they complement each other in a deep learning model  In the stance classification domain, previous work has showed that text features are limited, suggesting that adding extra-linguistic constraints could improve performance BIBREF6 , BIBREF7 , BIBREF8   For example, Hasan and Ng as well as Thomas et al  require that posts written by the same author have the same stance BIBREF9 , BIBREF10   The addition of this constraint yields accuracy improvements of 1–7% for some models and datasets  Hasan and Ng later added user-interaction constraints and ideology constraints BIBREF7 : the former models the relationship among posts in a sequence of replies and the latter models inter-topic relationships, e g , users who oppose abortion could be conservative and thus are likely to oppose gay rights 
For work focusing on online forum text, since posts are linked through user replies, sequential labeling methods have been used to model relationships between posts  For example, Hasan and Ng use hidden Markov models (HMMs) to model dependent relationships to the preceding post BIBREF9 ; Burfoot et al  use iterative classification to repeatedly generate new estimates based on the current state of knowledge BIBREF11 ; Sridhar et al  use probabilistic soft logic (PSL) to model reply links via collaborative filtering BIBREF12   In the Facebook dataset we study, we use comments instead of reply links  However, as the ultimate goal in this paper is predicting not comment stance but post stance, we treat comments as extra information for use in predicting post stance 
Deep Learning on Extra-Linguistic Features
In recent years neural network models have been applied to document sentiment classification BIBREF13 , BIBREF4 , BIBREF14 , BIBREF15 , BIBREF2   Text features can be used in deep networks to capture text semantics or sentiment  For example, Dong et al  use an adaptive layer in a recursive neural network for target-dependent Twitter sentiment analysis, where targets are topics such as windows 7 or taylor swift BIBREF16 , BIBREF17 ; recursive neural tensor networks (RNTNs) utilize sentence parse trees to capture sentence-level sentiment for movie reviews BIBREF4 ; Le and Mikolov predict sentiment by using paragraph vectors to model each paragraph as a continuous representation BIBREF18   They show that performance can thus be improved by more delicate text models 
Others have suggested using extra-linguistic features to improve the deep learning model  The user-word composition vector model (UWCVM) BIBREF19 is inspired by the possibility that the strength of sentiment words is user-specific; to capture this they add user embeddings in their model  In UPNN, a later extension, they further add a product-word composition as product embeddings, arguing that products can also show different tendencies of being rated or reviewed BIBREF20   Their addition of user information yielded 2–10% improvements in accuracy as compared to the above-mentioned RNTN and paragraph vector methods  We also seek to inject user information into the neural network model  In comparison to the research of Tang et al  on sentiment classification for product reviews, the difference is two-fold  First, we take into account multiple users (one author and potentially many likers) for one post, whereas only one user (the reviewer) is involved in a review  Second, we add comment information to provide more features for post stance classification  None of these two factors have been considered previously in a deep learning model for text stance classification  Therefore, we propose UTCNN, which generates and utilizes user embeddings for all users — even for those who have not authored any posts — and incorporates comments to further improve performance 
Method
In this section, we first describe CNN-based document composition, which captures user- and topic-dependent document-level semantic representation from word representations  Then we show how to add comment information to construct the user-topic-comment neural network (UTCNN) 
User- and Topic-dependent Document Composition
As shown in Figure FIGREF4 , we use a general CNN BIBREF3 and two semantic transformations for document composition   We are given a document with an engaged user INLINEFORM0 , a topic INLINEFORM1 , and its composite INLINEFORM2 words, each word INLINEFORM3 of which is associated with a word embedding INLINEFORM4 where INLINEFORM5 is the vector dimension  For each word embedding INLINEFORM6 , we apply two dot operations as shown in Equation EQREF6 : DISPLAYFORM0
where INLINEFORM0 models the user reading preference for certain semantics, and INLINEFORM1 models the topic semantics; INLINEFORM2 and INLINEFORM3 are the dimensions of transformed user and topic embeddings respectively  We use INLINEFORM4 to model semantically what each user prefers to read and/or write, and use INLINEFORM5 to model the semantics of each topic  The dot operation of INLINEFORM6 and INLINEFORM7 transforms the global representation INLINEFORM8 to a user-dependent representation  Likewise, the dot operation of INLINEFORM9 and INLINEFORM10 transforms INLINEFORM11 to a topic-dependent representation 
After the two dot operations on INLINEFORM0 , we have user-dependent and topic-dependent word vectors INLINEFORM1 and INLINEFORM2 , which are concatenated to form a user- and topic-dependent word vector INLINEFORM3   Then the transformed word embeddings INLINEFORM4 are used as the CNN input  Here we apply three convolutional layers on the concatenated transformed word embeddings INLINEFORM5 : DISPLAYFORM0
where INLINEFORM0 is the index of words; INLINEFORM1 is a non-linear activation function (we use INLINEFORM2 ); INLINEFORM5 is the convolutional filter with input length INLINEFORM6 and output length INLINEFORM7 , where INLINEFORM8 is the window size of the convolutional operation; and INLINEFORM9 and INLINEFORM10 are the output and bias of the convolution layer INLINEFORM11 , respectively  In our experiments, the three window sizes INLINEFORM12 in the three convolution layers are one, two, and three, encoding unigram, bigram, and trigram semantics accordingly 
After the convolutional layer, we add a maximum pooling layer among convolutional outputs to obtain the unigram, bigram, and trigram n-gram representations  This is succeeded by an average pooling layer for an element-wise average of the three maximized convolution outputs 
UTCNN Model Description
Figure FIGREF10 illustrates the UTCNN model  As more than one user may interact with a given post, we first add a maximum pooling layer after the user matrix embedding layer and user vector embedding layer to form a moderator matrix embedding INLINEFORM0 and a moderator vector embedding INLINEFORM1 for moderator INLINEFORM2 respectively, where INLINEFORM3 is used for the semantic transformation in the document composition process, as mentioned in the previous section  The term moderator here is to denote the pseudo user who provides the overall semantic/sentiment of all the engaged users for one document  The embedding INLINEFORM4 models the moderator stance preference, that is, the pattern of the revealed user stance: whether a user is willing to show his preference, whether a user likes to show impartiality with neutral statements and reasonable arguments, or just wants to show strong support for one stance  Ideally, the latent user stance is modeled by INLINEFORM5 for each user  Likewise, for topic information, a maximum pooling layer is added after the topic matrix embedding layer and topic vector embedding layer to form a joint topic matrix embedding INLINEFORM6 and a joint topic vector embedding INLINEFORM7 for topic INLINEFORM8 respectively, where INLINEFORM9 models the semantic transformation of topic INLINEFORM10 as in users and INLINEFORM11 models the topic stance tendency  The latent topic stance is also modeled by INLINEFORM12 for each topic 
As for comments, we view them as short documents with authors only but without likers nor their own comments  Therefore we apply document composition on comments although here users are commenters (users who comment)  It is noticed that the word embeddings INLINEFORM0 for the same word in the posts and comments are the same, but after being transformed to INLINEFORM1 in the document composition process shown in Figure FIGREF4 , they might become different because of their different engaged users  The output comment representation together with the commenter vector embedding INLINEFORM2 and topic vector embedding INLINEFORM3 are concatenated and a maximum pooling layer is added to select the most important feature for comments  Instead of requiring that the comment stance agree with the post, UTCNN simply extracts the most important features of the comment contents; they could be helpful, whether they show obvious agreement or disagreement  Therefore when combining comment information here, the maximum pooling layer is more appropriate than other pooling or merging layers  Indeed, we believe this is one reason for UTCNN's performance gains 
Finally, the pooled comment representation, together with user vector embedding INLINEFORM0 , topic vector embedding INLINEFORM1 , and document representation are fed to a fully connected network, and softmax is applied to yield the final stance label prediction for the post 
Experiment
We start with the experimental dataset and then describe the training process as well as the implementation of the baselines  We also implement several variations to reveal the effects of features: authors, likers, comment, and commenters  In the results section we compare our model with related work 
Dataset
We tested the proposed UTCNN on two different datasets: FBFans and CreateDebate  FBFans is a privately-owned, single-topic, Chinese, unbalanced, social media dataset, and CreateDebate is a public, multiple-topic, English, balanced, forum dataset  Results using these two datasets show the applicability and superiority for different topics, languages, data distributions, and platforms 
The FBFans dataset contains data from anti-nuclear-power Chinese Facebook fan groups from September 2013 to August 2014, including posts and their author and liker IDs  There are a total of 2,496 authors, 505,137 likers, 33,686 commenters, and 505,412 unique users  Two annotators were asked to take into account only the post content to label the stance of the posts in the whole dataset as supportive, neutral, or unsupportive (hereafter denoted as Sup, Neu, and Uns)  Sup/Uns posts were those in support of or against anti-reconstruction; Neu posts were those evincing a neutral standpoint on the topic, or were irrelevant  Raw agreement between annotators is 0 91, indicating high agreement  Specifically, Cohen’s Kappa for Neu and not Neu labeling is 0 58 (moderate), and for Sup or Uns labeling is 0 84 (almost perfect)  Posts with inconsistent labels were filtered out, and the development and testing sets were randomly selected from what was left  Posts in the development and testing sets involved at least one user who appeared in the training set  The number of posts for each stance is shown on the left-hand side of Table TABREF12   About twenty percent of the posts were labeled with a stance, and the number of supportive (Sup) posts was much larger than that of the unsupportive (Uns) ones: this is thus highly skewed data, which complicates stance classification  On average, 161 1 users were involved in one post  The maximum was 23,297 and the minimum was one (the author)  For comments, on average there were 3 comments per post  The maximum was 1,092 and the minimum was zero 
To test whether the assumption of this paper – posts attract users who hold the same stance to like them – is reliable, we examine the likes from authors of different stances  Posts in FBFans dataset are used for this analysis  We calculate the like statistics of each distinct author from these 32,595 posts  As the numbers of authors in the Sup, Neu and Uns stances are largely imbalanced, these numbers are normalized by the number of users of each stance  Table TABREF13 shows the results  Posts with stances (i e , not neutral) attract users of the same stance  Neutral posts also attract both supportive and neutral users, like what we observe in supportive posts, but just the neutral posts can attract even more neutral likers  These results do suggest that users prefer posts of the same stance, or at least posts of no obvious stance which might cause annoyance when reading, and hence support the user modeling in our approach 
The CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion (ABO), gay rights (GAY), Obama (OBA), and marijuana (MAR)  The posts are annotated as for (F) and against (A)  Replies to posts in this dataset are also labeled with stance and hence use the same data format as posts  The labeling results are shown in the right-hand side of Table TABREF12   We observe that the dataset is more balanced than the FBFans dataset  In addition, there are 977 unique users in the dataset  To compare with Hasan and Ng's work, we conducted five-fold cross-validation and present the annotation results as the average number of all folds BIBREF9 , BIBREF5  
The FBFans dataset has more integrated functions than the CreateDebate dataset; thus our model can utilize all linguistic and extra-linguistic features  For the CreateDebate dataset, on the other hand, the like and comment features are not available (as there is a stance label for each reply, replies are evaluated as posts as other previous work) but we still implemented our model using the content, author, and topic information 
Settings
In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer  For FBFans dataset, we learned the 50-dimensional word embeddings on the whole dataset using GloVe BIBREF21 to capture the word semantics; for CreateDebate dataset we used the publicly available English 50-dimensional word embeddings, pre-trained also using GloVe  These word embeddings were fixed in the training process  The learning rate was set to 0 03  All user and topic embeddings were randomly initialized in the range of [-0 1 0 1]  Matrix embeddings for users and topics were sized at 250 ( INLINEFORM0 ); vector embeddings for users and topics were set to length 10 
We applied the LDA topic model BIBREF22 on the FBFans dataset to determine the latent topics with which to build topic embeddings, as there is only one general known topic: nuclear power plants  We learned 100 latent topics and assigned the top three topics for each post  For the CreateDebate dataset, which itself constitutes four topics, the topic labels for posts were used directly without additionally applying LDA 
For the FBFans data we report class-based f-scores as well as the macro-average f-score ( INLINEFORM0 ) shown in equation EQREF19   DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 are the average precision and recall of the three class  We adopted the macro-average f-score as the evaluation metric for the overall performance because (1) the experimental dataset is severely imbalanced, which is common for contentious issues; and (2) for stance classification, content in minor-class posts is usually more important for further applications  For the CreateDebate dataset, accuracy was adopted as the evaluation metric to compare the results with related work BIBREF7 , BIBREF9 , BIBREF12  
Baselines
We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information; 6) UTCNN without user information, representing a pure-text CNN model where we use the same user matrix and user embeddings INLINEFORM1 and INLINEFORM2 for each user; 7) UTCNN without the LDA model, representing how UTCNN works with a single-topic dataset; 8) UTCNN without comments, in which the model predicts the stance label given only user and topic information  All these models were trained on the training set, and parameters as well as the SVM kernel selections (linear or RBF) were fine-tuned on the development set  Also, we adopt oversampling on SVMs, CNN and RCNN because the FBFans dataset is highly imbalanced 
Results on FBFans Dataset
In Table TABREF22 we show the results of UTCNN and the baselines on the FBFans dataset  Here Majority yields good performance on Neu since FBFans is highly biased to the neutral class  The SVM models perform well on Sup and Neu but perform poorly for Uns, showing that content information in itself is insufficient to predict stance labels, especially for the minor class  With the transformed word embedding feature, SVM can achieve comparable performance as SVM with n-gram feature  However, the much fewer feature dimension of the transformed word embedding makes SVM with word embeddings a more efficient choice for modeling the large scale social media dataset  For the CNN and RCNN models, they perform slightly better than most of the SVM models but still, the content information is insufficient to achieve a good performance on the Uns posts  As to adding comment information to these models, since the commenters do not always hold the same stance as the author, simply adding comments and post contents together merely adds noise to the model 
Among all UTCNN variations, we find that user information is most important, followed by topic and comment information  UTCNN without user information shows results similar to SVMs — it does well for Sup and Neu but detects no Uns  Its best f-scores on both Sup and Neu among all methods show that with enough training data, content-based models can perform well; at the same time, the lack of user information results in too few clues for minor-class posts to either predict their stance directly or link them to other users and posts for improved performance  The 17 5% improvement when adding user information suggests that user information is especially useful when the dataset is highly imbalanced  All models that consider user information predict the minority class successfully  UCTNN without topic information works well but achieves lower performance than the full UTCNN model  The 4 9% performance gain brought by LDA shows that although it is satisfactory for single topic datasets, adding that latent topics still benefits performance: even when we are discussing the same topic, we use different arguments and supporting evidence  Lastly, we get 4 8% improvement when adding comment information and it achieves comparable performance to UTCNN without topic information, which shows that comments also benefit performance  For platforms where user IDs are pixelated or otherwise hidden, adding comments to a text model still improves performance  In its integration of user, content, and comment information, the full UTCNN produces the highest f-scores on all Sup, Neu, and Uns stances among models that predict the Uns class, and the highest macro-average f-score overall  This shows its ability to balance a biased dataset and supports our claim that UTCNN successfully bridges content and user, topic, and comment information for stance classification on social media text  Another merit of UTCNN is that it does not require a balanced training data  This is supported by its outperforming other models though no oversampling technique is applied to the UTCNN related experiments as shown in this paper  Thus we can conclude that the user information provides strong clues and it is still rich even in the minority class 
We also investigate the semantic difference when a user acts as an author/liker or a commenter  We evaluated a variation in which all embeddings from the same user were forced to be identical (this is the UTCNN shared user embedding setting in Table TABREF22 )  This setting yielded only a 2 5% improvement over the model without comments, which is not statistically significant  However, when separating authors/likers and commenters embeddings (i e , the UTCNN full model), we achieved much greater improvements (4 8%)  We attribute this result to the tendency of users to use different wording for different roles (for instance author vs commenter)  This is observed when the user, acting as an author, attempts to support her argument against nuclear power by using improvements in solar power; when acting as a commenter, though, she interacts with post contents by criticizing past politicians who supported nuclear power or by arguing that the proposed evacuation plan in case of a nuclear accident is ridiculous  Based on this finding, in the final UTCNN setting we train two user matrix embeddings for one user: one for the author/liker role and the other for the commenter role 
Results on CreateDebate Dataset
Table TABREF24 shows the results of UTCNN, baselines as we implemented on the FBFans datset and related work on the CreateDebate dataset  We do not adopt oversampling on these models because the CreateDebate dataset is almost balanced  In previous work, integer linear programming (ILP) or linear-chain conditional random fields (CRFs) were proposed to integrate text features, author, ideology, and user-interaction constraints, where text features are unigram, bigram, and POS-dependencies; the author constraint tends to require that posts from the same author for the same topic hold the same stance; the ideology constraint aims to capture inferences between topics for the same author; the user-interaction constraint models relationships among posts via user interactions such as replies BIBREF7 , BIBREF9  
The SVM with n-gram or average word embedding feature performs just similar to the majority  However, with the transformed word embedding, it achieves superior results  It shows that the learned user and topic embeddings really capture the user and topic semantics  This finding is not so obvious in the FBFans dataset and it might be due to the unfavorable data skewness for SVM  As for CNN and RCNN, they perform slightly better than most SVMs as we found in Table TABREF22 for FBFans 
Compared to the ILP BIBREF7 and CRF BIBREF9 methods, the UTCNN user embeddings encode author and user-interaction constraints, where the ideology constraint is modeled by the topic embeddings and text features are modeled by the CNN  The significant improvement achieved by UTCNN suggests the latent representations are more effective than overt model constraints 
The PSL model BIBREF12 jointly labels both author and post stance using probabilistic soft logic (PSL) BIBREF23 by considering text features and reply links between authors and posts as in Hasan and Ng's work  Table TABREF24 reports the result of their best AD setting, which represents the full joint stance/disagreement collective model on posts and is hence more relevant to UTCNN  In contrast to their model, the UTCNN user embeddings represent relationships between authors, but UTCNN models do not utilize link information between posts  Though the PSL model has the advantage of being able to jointly label the stances of authors and posts, its performance on posts is lower than the that for the ILP or CRF models  UTCNN significantly outperforms these models on posts and has the potential to predict user stances through the generated user embeddings 
For the CreateDebate dataset, we also evaluated performance when not using topic embeddings or user embeddings; as replies in this dataset are viewed as posts, the setting without comment embeddings is not available  Table TABREF24 shows the same findings as Table TABREF22 : the 21% improvement in accuracy demonstrates that user information is the most vital  This finding also supports the results in the related work: user constraints are useful and can yield 11 2% improvement in accuracy BIBREF7   Further considering topic information yields 3 4% improvement, suggesting that knowing the subject of debates provides useful information  In sum, Table TABREF22 together with Table TABREF24 show that UTCNN achieves promising performance regardless of topic, language, data distribution, and platform 
Conclusion
We have proposed UTCNN, a neural network model that incorporates user, topic, content and comment information for stance classification on social media texts  UTCNN learns user embeddings for all users with minimum active degree, i e , one post or one like  Topic information obtained from the topic model or the pre-defined labels further improves the UTCNN model  In addition, comment information provides additional clues for stance classification  We have shown that UTCNN achieves promising and balanced results  In the future we plan to explore the effectiveness of the UTCNN user embeddings for author stance classification 
Acknowledgements
Research of this paper was partially supported by Ministry of Science and Technology, Taiwan, under the contract MOST 104-2221-E-001-024-MY2 
Context 3: Introduction
Users of photo-sharing websites such as Flickr often provide short textual descriptions in the form of tags to help others find the images  With the availability of GPS systems in current electronic devices such as smartphones, latitude and longitude coordinates are nowadays commonly made available as well  The tags associated with such georeferenced photos often describe the location where these photos were taken, and Flickr can thus be regarded as a source of environmental information  The use of Flickr for modelling urban environments has already received considerable attention  For instance, various approaches have been proposed for modelling urban regions BIBREF0 , and for identifying points-of-interest BIBREF1 and itineraries BIBREF2 , BIBREF3   However, the usefulness of Flickr for characterizing the natural environment, which is the focus of this paper, is less well-understood 
Many recent studies have highlighted that Flickr tags capture valuable ecological information, which can be used as a complementary source to more traditional sources  To date, however, ecologists have mostly used social media to conduct manual evaluations of image content with little automated exploitation of the associated tags BIBREF4 , BIBREF5 , BIBREF6   One recent exception is BIBREF7 , where bag-of-words representations derived from Flickr tags were found to give promising result for predicting a range of different environemental phenomena 
Our main hypothesis in this paper is that by using vector space embeddings instead of bag-of-words representations, the ecological information which is implicitly captured by Flickr tags can be utilized in a more effective way  Vector space embeddings are representations in which the objects from a given domain are encoded using relatively low-dimensional vectors  They have proven useful in natural language processing, especially for encoding word meaning BIBREF8 , BIBREF9 , and in machine learning more generally  In this paper, we are interested in the use of such representations for modelling geographic locations  Our main motivation for using vector space embeddings is that they allow us to integrate the textual information we get from Flickr with available structured information in a very natural way  To this end, we rely on an adaptation of the GloVe word embedding model BIBREF9 , but rather than learning word vectors, we learn vectors representing locations  Similar to how the representation of a word in GloVe is determined by the context words surrounding it, the representation of a location in our model is determined by the tags of the photos that have been taken near that location  To incorporate numerical features from structured environmental datasets (e g  average temperature), we associate with each such feature a linear mapping that can be used to predict that feature from a given location vector  This is inspired by the fact that salient properties of a given domain can often be modelled as directions in vector space embeddings BIBREF10 , BIBREF11 , BIBREF12   Finally, evidence from categorical datasets (e g  land cover types) is taken into account by requiring that locations belonging to the same category are represented using similar vectors, similar to how semantic types are sometimes modelled in the context of knowledge graph embedding BIBREF13  
While our point-of-departure is a standard word embedding model, we found that the off-the-shelf GloVe model performed surprisingly poorly, meaning that a number of modifications are needed to achieve good results  Our main findings are as follows  First, given that the number of tags associated with a given location can be quite small, it is important to apply some kind of spatial smoothing, i e  the importance of a given tag for a given location should not only depend on the occurrences of the tag at that location, but also on its occurrences at nearby locations  To this end, we use a formulation which is based on spatially smoothed version of pointwise mutual information  Second, given the wide diversity in the kind of information that is covered by Flickr tags, we find that term selection is in some cases critical to obtain vector spaces that capture the relevant aspects of geographic locations  For instance, many tags on Flickr refer to photography related terms, which we would normally not want to affect the vector representation of a given location  Finally, even with these modifications, vector space embeddings learned from Flickr tags alone are sometimes outperformed by bag-of-words representations  However, our vector space embeddings lead to substantially better predictions in cases where structured (scientific) information is also taken into account  In this sense, the main value of using vector space embeddings in this context is not so much about abstracting away from specific tag usages, but rather about the fact that such representations allow us to integrate numerical and categorical features in a much more natural way than is possible with bag-of-words representations 
The remainder of this paper is organized as follows  In the next section, we provide a discussion of existing work  Section SECREF3 then presents our model for embedding geographic locations from Flickr tags and structured data  Next, in Section SECREF4 we provide a detailed discussion about the experimental results  Finally, Section SECREF5 summarizes our conclusions 
Vector space embeddings
The use of low-dimensional vector space embeddings for representing objects has already proven effective in a large number of applications, including natural language processing (NLP), image processing, and pattern recognition  In the context of NLP, the most prominent example is that of word embeddings, which represent word meaning using vectors of typically around 300 dimensions  A large number of different methods for learning such word embeddings have already been proposed, including Skip-gram and the Continuous Bag-of-Words (CBOW) model BIBREF8 , GloVe BIBREF9 , and fastText BIBREF14   They have been applied effectively in many downstream NLP tasks such as sentiment analysis BIBREF15 , part of speech tagging BIBREF16 , BIBREF17 , and text classification BIBREF18 , BIBREF19   The model we consider in this paper builds on GloVe, which was designed to capture linear regularities of word-word co-occurrence  In GloVe, there are two word vectors INLINEFORM0 and INLINEFORM1 for each word in the vocabulary, which are learned by minimizing the following objective: DISPLAYFORM0
where INLINEFORM0 is the number of times that word INLINEFORM1 appears in the context of word INLINEFORM2 , INLINEFORM3 is the vocabulary size, INLINEFORM4 is the target word bias, INLINEFORM5 is the context word bias  The weighting function INLINEFORM6 is used to limit the impact of rare terms  It is defined as 1 if INLINEFORM7 and as INLINEFORM8 otherwise, where INLINEFORM9 is usually fixed to 100 and INLINEFORM10 to 0 75  Intuitively, the target word vectors INLINEFORM11 correspond to the actual word representations which we would like to find, while the context word vectors INLINEFORM12 model how occurrences of INLINEFORM13 in the context of a given word INLINEFORM14 affect the representation of this latter word  In this paper we will use a similar model, which will however be aimed at learning location vectors instead of the target word vectors 
Beyond word embeddings, various methods have been proposed for learning vector space representations from structured data such as knowledge graphs BIBREF20 , BIBREF21 , BIBREF22 , social networks BIBREF23 , BIBREF24 and taxonomies BIBREF25 , BIBREF26   The idea of combining a word embedding model with structured information has also been explored by several authors, for example to improve the word embeddings based on information coming from knowledge graphs BIBREF27 , BIBREF28   Along similar lines, various lexicons have been used to obtain word embeddings that are better suited at modelling sentiment BIBREF15 and antonymy BIBREF29 , among others  The method proposed by BIBREF30 imposes the condition that words that belong to the same semantic category are closer together than words from different categories, which is somewhat similar in spirit to how we will model categorical datasets in our model 
Embeddings for geographic information
The problem of representing geographic locations using embeddings has also attracted some attention  An early example is BIBREF31 , which used principal component analysis and stacked autoencoders to learn low-dimensional vector representations of city neighbourhoods based on census data  They use these representations to predict attributes such as crime, which is not included in the given census data, and find that in most of the considered evaluation tasks, the low-dimensional vector representations lead to more faithful predictions than the original high-dimensional census data 
Some existing works combine word embedding models with geographic coordinates  For example, in BIBREF32 an approach is proposed to learn word embeddings based on the assumption that words which tend to be used in the same geographic locations are likely to be similar  Note that their aim is dual to our aim in this paper: while they use geographic location to learn word vectors, we use textual descriptions to learn vectors representing geographic locations 
Several methods also use word embedding models to learn representations of Points-of-Interest (POIs) that can be used for predicting user visits BIBREF33 , BIBREF34 , BIBREF35   These works use the machinery of existing word embedding models to learn POI representations, intuitively by letting sequences of POI visits by a user play the role of sequences of words in a sentence  In other words, despite the use of word embedding models, many of these approaches do not actually consider any textual information  For example, in BIBREF34 the Skip-gram model is utilized to create a global pattern of users' POIs  Each location was treated as a word and the other locations visited before or after were treated as context words  They then use a pair-wise ranking loss BIBREF36 which takes into account the user's location visit frequency to personalize the location recommendations  The methods of BIBREF34 were extended in BIBREF35 to use a temporal embedding and to take more account of geographic context, in particular the distances between preferred and non-preferred neighboring POIs, to create a “geographically hierarchical pairwise preference ranking model”  Similarly, in BIBREF37 the CBOW model was trained with POI data  They ordered POIs spatially within the traffic-based zones of urban areas  The ordering was used to generate characteristic vectors of POI types  Zone vectors represented by averaging the vectors of the POIs contained in them, were then used as features to predict land use types  In the CrossMap method BIBREF38 they learned embeddings for spatio-temporal hotspots obtained from social media data of locations, times and text  In one form of embedding, intended to enable reconstruction of records, neighbourhood relations in space and time were encoded by averaging hotspots in a target location's spatial and temporal neighborhoods  They also proposed a graph-based embedding method with nodes of location, time and text

ground_truth: ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era

Ancient Chinese history records in several dynasties and articles written by celebrities during 1000BC-200BC collected from the internet 

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 are the baselines?
Context 3:  The concatenation of the location, time and text vectors were then used as features to predict peoples' activities in urban environments  Finally, in BIBREF39 , a method is proposed that uses the Skip-gram model to represent POI types, based on the intuition that the vector representing a given POI type should be predictive of the POI types that found near places of that type 
Our work is different from these studies, as our focus is on representing locations based on a given text description of that location (in the form of Flickr tags), along with numerical and categorical features from scientific datasets 
Analyzing Flickr tags
Many studies have focused on analyzing Flickr tags to extract useful information in domains such as linguistics BIBREF40 , geography BIBREF0 , BIBREF41 , and ecology BIBREF42 , BIBREF7 , BIBREF43   Most closely related to our work, BIBREF7 found that the tags of georeferenced Flickr photos can effectively supplement traditional scientific environmental data in tasks such as predicting climate features, land cover, species occurrence, and human assessments of scenicness  To encode locations, they simply combine a bag-of-words representation of geographically nearby tags with a feature vector that encodes associated structured scientific data  They found that the predictive value of Flickr tags is roughly on a par with that of the scientific datasets, and that combining both types of information leads to significantly better results than using either of them alone  As we show in this paper, however, their straightforward way of combining both information sources, by concatenating the two types of feature vectors, is far from optimal 
Despite the proven importance of Flickr tags, the problem of embedding Flickr tags has so far received very limited attention  To the best of our knowledge, BIBREF44 is the only work that generated embeddings for Flickr tags  However, their focus was on learning embeddings that capture word meaning (being evaluated on word similarity tasks), whereas we use such embeddings as part of our method for representing locations 
Model Description
In this section, we introduce our embedding model, which combines Flickr tags and structured scientific information to represent a set of locations INLINEFORM0   The proposed model has the following form: DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 are parameters to control the importance of each component in the model  Component INLINEFORM2 will be used to constrain the representation of the locations based on their textual description (i e  Flickr tags), INLINEFORM3 will be used to constrain the representation of the locations based on their numerical features, and INLINEFORM4 will impose the constraint that locations belonging to the same category should be close together in the space  We will discuss each of these components in more detail in the following sections 
Tag Based Location Embedding
Many of the tags associated with Flickr photos describe characteristics of the places where these photos were taken BIBREF45 , BIBREF46 , BIBREF47   For example, tags may correspond to place names (e g  Brussels, England, Scandinavia), landmarks (e g  Eiffel Tower, Empire State Building) or land cover types (e g  mountain, forest, beach)  To allow us to build location models using such tags, we collected the tags and meta-data of 70 million Flickr photos with coordinates in Europe (which is the region our experiments will focus on), all of which were uploaded to Flickr before the end of September 2015  In this section we first explain how tags can be weighted to obtain bag-of-words representations of locations from Flickr  Subsequently we describe a tag selection method, which will allow us to specialize the embedding depending on which aspects of the considered locations are of interest, after which we discuss the actual embedding model 
Tag weighting  Let INLINEFORM0 be a set of geographic locations, each characterized by latitude and longitude coordinates  To generate a bag-of-words representation of a given location, we have to weight the relevance of each tag to that location  To this end, we have followed the weighting scheme from BIBREF7 , which combines a Gaussian kernel (to model spatial proximity) with Positive Pointwise Mutual Information (PPMI) BIBREF48 , BIBREF49  
Let us write INLINEFORM0 for the set of users who have assigned tag INLINEFORM1 to a photo with coordinates near INLINEFORM2   To assess how relevant INLINEFORM3 is to the location INLINEFORM4 , the number of times INLINEFORM5 occurs in photos near INLINEFORM6 is clearly an important criterion  However, rather than simply counting the number of occurrences within some fixed radius, we use a Gaussian kernel to weight the tag occurrences according to their distance from that location: INLINEFORM7
where the threshold INLINEFORM0 is assumed to be fixed, INLINEFORM1 is the location of a Flickr photo, INLINEFORM2 is the Haversine distance, and we will assume that the bandwidth parameter INLINEFORM3 is set to INLINEFORM4   A tag occurrence is counted only once for all photos by the same user at the same location, which is important to reduce the impact of bulk uploading  The value INLINEFORM5 reflects how frequent tag INLINEFORM6 is near location INLINEFORM7 , but it does not yet take into account the total number of tag occurrences near INLINEFORM8 , nor how popular the tag INLINEFORM9 is overall  To measure how strongly tag INLINEFORM10 is associated with location INLINEFORM11 , we use PPMI, which is a commonly used measure of association in natural language processing  However, rather than estimating PPMI scores from term frequencies, we will use the INLINEFORM12 values instead: INLINEFORM13
where: INLINEFORM0
with INLINEFORM0 the set of all tags, and INLINEFORM1 the set of locations 
Tag selection  Inspired by BIBREF50 , we use a term selection method in order to focus on the tags that are most important for the tasks that we want to consider and reduce the impact of tags that might relate only to a given individual or a group of users  In particular, we obtained good results with a method based on Kullback-Leibler (KL) divergence, which is based on BIBREF51   Let INLINEFORM0 be a set of (mutually exclusive) properties of locations in which we are interested (e g  land cover categories)  For the ease of presentation, we will identify INLINEFORM1 with the set of locations that have the corresponding property  Then, we select tags from INLINEFORM2 that maximize the following score: INLINEFORM3
where INLINEFORM0 is the probability that a photo with tag INLINEFORM1 has a location near INLINEFORM2 and INLINEFORM3 is the probability that an arbitrary tag occurrence is assigned to a photo near a location in INLINEFORM4   Since INLINEFORM5 often has to be estimated from a small number of tag occurrences, it is estimated using Bayesian smoothing: INLINEFORM6
where INLINEFORM0 is a parameter controlling the amount of smoothing, which will be tuned in the experiments  On the other hand, for INLINEFORM1 we can simply use a maximum likelihood estimation: INLINEFORM2
Location embedding  We now want to find a vector INLINEFORM0 for each location INLINEFORM1 such that similar locations are represented using similar vectors  To achieve this, we use a close variant of the GloVe model, where tag occurrences are treated as context words of geographic locations  In particular, with each location INLINEFORM2 we associate a vector INLINEFORM3 and with each tag INLINEFORM4 we associate a vector INLINEFORM5 and a bias term INLINEFORM6 , and consider the following objective (which in our full model ( EQREF7 ) will be combined with components that are derived from the structured information): INLINEFORM7
Note how tags play the role of the context words in the GloVe model, while instead of learning target word vectors we now learn location vectors  In contrast to GloVe, our objective does not directly refer to co-occurrence statistics, but instead uses the INLINEFORM0 scores  One important consequence of this is that we can also consider pairs INLINEFORM1 for which INLINEFORM2 does not occur in INLINEFORM3 at all; such pairs are usually called negative examples  While they cannot be used in the standard GloVe model, some authors have already reported that introducing negative examples in variants of GloVe can lead to an improvement BIBREF52   In practice, evaluating the full objective above would not be computationally feasible, as we may need to consider millions of locations and millions of tags  Therefore, rather than considering all tags in INLINEFORM4 for the inner summation, we only consider those tags that appear at least once near location INLINEFORM5 together with a sample of negative examples 
Structured Environmental Data
There is a wide variety of structured data that can be used to describe locations  In this work, we have restricted ourselves to the same datasets as BIBREF7   These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg  temperature, avg  precipitation, avg  solar radiation, avg  wind speed, and avg  water vapor pressure)  In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids)  Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type 
Numerical features  Numerical features can be treated similarly to the tag occurrences, i e  we will assume that the value of a given numerical feature can be predicted from the location vectors using a linear mapping  In particular, for each numerical feature INLINEFORM0 we consider a vector INLINEFORM1 and a bias term INLINEFORM2 , and the following objective: INLINEFORM3
where we write INLINEFORM0 for set of all numerical features and INLINEFORM1 is the value of feature INLINEFORM2 for location INLINEFORM3 , after z-score normalization 
Categorical features  To take into account the categorical features, we impose the constraint that locations belonging to the same category should be close together in the space

ground_truth: SVM with unigram, bigram, and trigram features, SVM with average word embedding, SVM with average transformed word embeddings, CNN, ecurrent Convolutional Neural Networks, SVM and deep learning models with comment information

SVM with unigram, bigram, trigram features, with average word embedding, with average transformed word embeddings, CNN and RCNN, SVM, CNN, RCNN with comment information

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 dataset is used in this paper?
Context 3:  To formalize this, we represent each category type INLINEFORM0 as a vector INLINEFORM1 , and consider the following objective: INLINEFORM2
Evaluation Tasks
We will use the method from BIBREF7 as our main baseline  This will allow us to directly evaluate the effectiveness of embeddings for the considered problem, since we have used the same structured datasets and same tag weighting scheme  For this reason, we will also follow their evaluation methodology  In particular, we will consider three evaluation tasks:
Predicting the distribution of 100 species across Europe, using the European network of nature protected sites Natura 2000 dataset as ground truth  For each of these species, a binary classification problem is considered  The set of locations INLINEFORM0 is defined as the 26,425 distinct sites occurring in the dataset 
Predicting soil type, again each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments  For these experiments, none of the soil type features are used for generating the embeddings 
Predicting CORINE land cover classes at levels 1, 2 and level 3, each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments  For these experiments, none of the CORINE features are used for generating the embeddings 
In addition, we will also consider the following regression tasks:
Predicting 5 climate related features: the average precipitation, temperature, solar radiation, water vapor pressure, and wind speed  We again use the same set of locations INLINEFORM0 as for species distribution in this experiment  None of the climate features is used for constructing the embeddings for this experiment 
Predicting people's subjective opinions of landscape beauty in Britain, using the crowdsourced dataset from the ScenicOrNot website as ground truth  The set INLINEFORM0 is chosen as the set of locations of 191 605 rated locations from the ScenicOrNot dataset for which at least one georeferenced Flickr photo exists within a 1 km radius 
Experimental Setup
In all experiments, we use Support Vector Machines (SVMs) for classification problems and Support Vector Regression (SVR) for regression problems to make predictions from our representations of geographic locations  In both cases, we used the SVM INLINEFORM0 implementation BIBREF53   For each experiment, the set of locations INLINEFORM1 was split into two-thirds for training, one-sixth for testing, and one-sixth for tuning the parameters  All embedding models are learned with Adagrad using 30 iterations  The number of dimensions is chosen for each experiment from INLINEFORM2 based on the tuning data  For the parameters of our model in Equation EQREF7 , we considered values of INLINEFORM3 from {0 1, 0 01, 0 001, 0 0001} and values of INLINEFORM4 from {1, 10, 100, 1000, 10 000, 100 000}  To compute KL divergence, we need to determine a set of classes INLINEFORM5 for each experiment  For classification problems, we can simply consider the given categories, but for the regression problems we need to define such classes by discretizing the numerical values  For the scenicness experiments, we considered scores 3 and 7 as cut-off points, leading to three classes (i e  less than 3, between 3 and 7, and above 7)  Similarly, for each climate related features, we consider two cut-off values for discretization: 5 and 15 for average temperature, 50 and 100 for average precipitation, 10 000 and 17 000 for average solar radiation, 0 7 and 1 for average water vapor pressure, and 3 and 5 for wind speed  The smoothing parameter INLINEFORM6 was selected among INLINEFORM7 based on the tuning data  In all experiments where term selection is used, we select the top 100 000 tags  We fixed the radius INLINEFORM8 at 1km when counting the number of tag occurrences  Finally, we set the number of negative examples as 10 times the number of positive examples for each location, but with a cap at 1000 negative examples in each region for computational reasons  We tune all parameters with respect to the F1 score for the classification tasks, and Spearman INLINEFORM9 for the regression tasks 
Variants and Baseline Methods
We will refer to our model as EGEL (Embedding GEographic Locations), and will consider the following variants  EGEL-Tags only uses the information from the Flickr tags (i e  component INLINEFORM0 ), without using any negative examples and without feature selection  EGEL-Tags+NS is similar to EGEL-Tags but with the addition of negative examples  EGEL-KL(Tags+NS) additionally considers term selection  EGEL-All is our full method, i e  it additionally uses the structured information  We also consider the following baselines  BOW-Tags represents locations using a bag-of-words representation, using the same tag weighting as the embedding model  BOW-KL(Tags) uses the same representation but after term selection, using the same KL-based method as the embedding model  BOW-All combines the bag-of-words representation with the structured information, encoded as proposed in BIBREF7   GloVe uses the objective from the original GloVe model for learning location vectors, i e  this variant differs from EGEL-Tags in that instead of INLINEFORM1 we use the number of co-occurrences of tag INLINEFORM2 near location INLINEFORM3 , measured as INLINEFORM4  
Results and Discussion
We present our results for the binary classification tasks in Tables TABREF23 – TABREF24 in terms of average precision, average recall and macro average F1 score  The results of the regression tasks are reported in Tables TABREF25 and TABREF29 in terms of the mean absolute error between the predicted and actual scores, as well as the Spearman INLINEFORM0 correlation between the rankings induced by both sets of scores  It can be clearly seen from the results that our proposed method (EGEL-All) can effectively integrate Flickr tags with the available structured information  It outperforms the baselines for all the considered tasks  Furthermore, note that the PPMI-based weighting in EGEL-Tags consistently outperforms GloVe and that both the addition of negative examples and term selection lead to further improvements  The use of term selection leads to particularly substantial improvements for the regression problems 
While our experimental results confirm the usefulness of embeddings for predicting environmental features, this is only consistently the case for the variants that use both the tags and the structured datasets  In particular, comparing BOW-Tags with EGEL-Tags, we sometimes see that the former achieves the best results  While this might seem surprising, it is in accordance with the findings in BIBREF54 , BIBREF38 , among others, where it was also found that bag-of-words representations can sometimes lead to surprisingly effective baselines  Interestingly, we note that in all cases where EGEL-KL(Tags+NS) performs worse than BOW-Tags, we also find that BOW-KL(Tags) performs worse than BOW-Tags  This suggests that for these tasks there is a very large variation in the kind of tags that can inform the prediction model, possibly including e g  user-specific tags  Some of the information captured by such highly specific but rare tags is likely to be lost in the embedding 
To further analyze the difference in performance between BoW representations and embeddings, Figure TABREF29 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations  What is clearly noticeable in Figure TABREF29 is that GloVe performs better than the bag-of-words model for large corpora and worse for smaller corpora  This issue has been alleviated in our embedding method by the addition of negative examples 
 Conclusions
In this paper, we have proposed a model to learn geographic location embeddings using Flickr tags, numerical environmental features, and categorical information  The experimental results show that our model can integrate Flickr tags with structured information in a more effective way than existing methods, leading to substantial improvements over baseline methods on various prediction tasks about the natural environment 
Acknowledgments
Shelan Jeawak has been sponsored by HCED Iraq  Steven Schockaert has been supported by ERC Starting Grant 637277 

ground_truth:  the same datasets as BIBREF7

same datasets as BIBREF7