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\Volume{27}
\Number{2}
\Month{June}
\Year{2020}
%%%%\Issuetitle{「　　　　」特集号}

\received{2019}{12}{1}
\revised{2020}{2}{29}
%%%%\rerevised{2006}{5}{6}
\accepted{2020}{4}{9}
\Issuetitle{「　　　　」特集号}

\setcounter{page}{467}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\esubject{General Paper}
\etitle{Personal Semantic Variations in Word Meanings: \\Induction, Application, and Analysis}
\eauthor{Daisuke Oba\affiref{Author_1} \and Shoetsu Sato\affiref{Author_1} \and Satoshi Akasaki\affiref{Author_1} \and \\
Naoki Yoshinaga\affiref{Author_2} \and Masashi Toyoda\affiref{Author_2}} 
\eabstract{
When people verbalize what they have felt with different sensory functions, they often represent different meanings such as with \textit{temperature range} using the same word \textit{cold} or the same meaning by using different words (e.g., \textit{hazy} and \textit{cloudy}). These interpersonal variations in word meanings have the effects of not only preventing people from communicating efficiently with each other but also causing troubles in natural language processing (\textsc{NLP}). Accordingly, to highlight interpersonal semantic variations in word meanings, a method for inducing personalized word embeddings is proposed. This method learns word embeddings from an \textsc{NLP} task, distinguishing each word used by different individuals. Review-target identification was adopted as a task to prevent irrelevant biases from contaminating word embeddings. The scalability and stability of inducing personalized word embeddings were improved using a residual network and independent fine-tuning for each individual through multi-task learning along with target-attribute predictions. The results of the experiments using two large scale review datasets confirmed that the proposed method was effective for estimating the target items, and the resulting word embeddings were also effective in solving sentiment analysis. By using the acquired personalized word embeddings, it was possible to reveal tendencies in semantic variations of the word meanings.  
}
\ekeywords{Semantic Variation, Personalized Word Embeddings, Residual Network, Multi-Task Learning, Review-Target Identification, Sentiment Analysis}

\headauthor{Oba, Sato, Akasaki, Yoshinaga, Toyoda}
\headtitle{Personal Semantic Variations in Word Meanings}

\affilabel{Author_1}{}{Graduate School of Information Science and Technology, The University of Tokyo}
\affilabel{Author_2}{}{Institute of Industrial Science, The University of Tokyo}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}

\maketitle

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}
\label{sec:introduction}

People express what they have sensed with various sensory organs as language in different ways, and semantic variations in the meanings of words inevitably exist because the senses and linguistic abilities of individuals differ. 
For example, if one were to use the word ``\textit{sour},'' \textit{how} ``\textit{sour}'' is meant can differ greatly between individuals.
Furthermore, different people may describe the appearance (color) of the same beer with different expressions such as ``\textit{yellow}'' or ``\textit{golden}.''
These semantic variations not only have the potential to cause problems in verbal communication but could also delude the potential of natural language processing (\textsc{NLP}) systems.

In the context of personalization, several studies have attempted to improve the performance of \textsc{NLP} models in user-oriented tasks such as sentiment analysis \cite{li:2011a,gao-EtAl:2013:IJCNLP,P15-1098,ebrahimi2016personalized}, dialogue systems \cite{li-EtAl:2016:P16-13,P18-1205,gu2019dually,madotto-etal-2019-personalizing}, grammatical error correction \cite{nadejde2019personalizing}, and machine translation \cite{mirkin-meunier:2015:EMNLP,wuebker-simianer-denero:2018:EMNLP,P18-2050}, all of which considered user preferences concerning the task inputs and outputs. 
However, all these studies were based on the premise of estimating \textit{subjective} output from \textit{subjective} input (e.g., estimating the sentiment polarity of the target item from input review or predicting responses from input utterances in a dialogue system).
Consequently, the model not only captures the semantic variations of the user-generated text (input) but also handles the \textit{annotation bias} of the output labels (namely, the deviation of output labels assigned by each annotator) \cite{gao-EtAl:2013:IJCNLP,gururangan2018annotation,geva2019we} and \textit{selection bias} (namely, the deviation of output labels inherited from the targets chosen by users) \cite{gao-EtAl:2013:IJCNLP}.
The contamination caused by these biases hinders the understanding of the solo impact of semantic variations, which is the target of this study. 

The goals of this study are to (i) understand which words have large (or small) interpersonal variations in their meanings (hereafter referred to as \textit{semantic variation}) and (ii) reveal how such semantic variation affects the classification accuracy concerning tasks with user-generated inputs (e.g., reviews).
To perform such analysis, a method for such analysis into the degree of personal semantic variation in word meanings is thus proposed (\secref{model}). 
It uses personalized word embeddings obtained through a task called ``review-target identification,'' in which a classifier estimates a target item (\textit{objective} output) from given reviews (\textit{subjective} input) written by various reviewers. 
It should be noted that this task is free from \textit{annotation bias} because outputs (review targets) are automatically determined without annotation, along with the suppression of \textit{selection bias} by using a dataset in which the same reviewer evaluates the same target only once, so as not to learn the deviation of output labels caused by choice of inputs. 
The resulting model makes it possible to observe only the impact of semantic variation from the acquired personalized word embeddings.

Two further remaining issues concerning inducing personalized word embeddings are the scalability and stability in learning personalized word embeddings.
To ensure that the training was scalable concerning the number of reviewers, a residual network \cite{he:2016:cvpr} was used to (i) obtain personalized word embeddings by using reviewer-specific transformation matrices and biases from a small amount of reviews for each user (\ssecref{personalization}) and (ii) fine-tune these reviewer-specific parameters (\ssecref{fine-tuning}).
Moreover, to make the training via the extreme multi-class classification (i.e., the review-target identification) stable, multi-task learning (\textsc{MTL}) with target-attribute predictions was performed during the pre-training of the parameters (\ssecref{multi-task}). 
As a result of the target attributes being likely to be more coarse-grained than the review targets, \textsc{MTL} using target-attributes made the training more stable.

During the experiments, it was hypothesized that words related to the five senses especially have inherent semantic variations, and this hypothesis was validated (\secref{experiments}). 
Two large-scale datasets retrieved from the RateBeer and Yelp websites, including a variety of expressions related to the five senses, were utilized. 
To confirm the impact of personalized word embeddings obtained by using the proposed method, the datasets were used for a specific task: identifying a target item and its attributes from a given review, using the reviewer's ID\@.
Consequently and concerning both datasets, the personalized model proposed successfully captured semantic variations and achieved better performance than a reviewer-universal model (\secref{main-exp}). 
Moreover, the personalized word embeddings obtained were extrinsically evaluated by sentiment analysis to assess their usefulness.
The results of the extrinsic evaluation evidence that this model achieved higher levels of performance as compared with other models, which demonstrated the capability of the proposed method for suppressing unfavorable biases during the training process (\secref{sub-exp}).
The acquired personalized word embeddings were finally analyzed from three perspectives (frequency, dissemination, and polysemy) to reveal which words have large (or small) semantic variations (\secref{analysis}).

The contributions of this paper are three-fold:
\begin{itemize}
\item A scalable and stable method for obtaining personalized word embeddings without contaminating them with irrelevant biases is proposed. 
The proposed method induces personalized word embeddings through a task with objective outputs via effective reviewer-wise fine-tuning on a neural network with a residual connection and \textsc{MTL} with target-attribute predictions.
\item The usefulness of the obtained personalized word embeddings not only in the review-target identification task but also in the sentiment analysis task is confirmed.
\item The tendencies in the personal semantic variations in terms of three perspectives (frequency, dissemination, and polysemy), which have been discussed in previous studies about diachronic and interdomain semantic variations, are revealed.
\end{itemize}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Related Work}
\label{sec:relatedwork}

Existing studies on personalization in \textsc{NLP} tasks and analysis of semantic variation of word meanings in terms of diachronic, geographic, domain, and political correctness will initially be discussed in this section.
Since existing methods on personalization are mostly aimed at improving accuracy on various tasks, such methods simultaneously model personal variations in word meanings and other irrelevant biases (such as \textit{annotation} and \textit{selection biases}) that contribute to task performances. 
There are a few studies that try to understand variations in word meanings in terms of time, geography, and domain, which will be reviewed. 
Finally, differences between interpersonal semantic variations in word meanings and biases related to unfavorable prejudices are then discussed. 


As discussed in \secref{introduction}, in the field of \textsc{NLP}, personalization attempts to capture three types of user preferences: (1) \textit{semantic variation} in task inputs (biases in how people use words, i.e., the target of this study), (2) \textit{annotation bias} of output labels (biases of how annotators label),\footnote{%
It is pointed out that \textsc{NLP} datasets are likely to suffer from \textit{annotation bias} \cite{geva2019we}, whether or not the context of the study is about personalization; models learn to use or rely on this \textit{annotation bias} when task accuracy is optimized \cite{tsuchiya-2018-performance,gururangan2018annotation,poliak-etal-2018-hypothesis,geva2019we}.}%
 and (3) \textit{selection bias} of output labels (biases of how people choose perspectives (e.g., review targets) that directly affect outputs (e.g., polarity labels)).
As for the history of data-driven approaches to various \textsc{NLP} tasks, existing studies have focused more on (2) and (3), particularly in the case of text generation tasks such as machine translation \cite{mirkin-meunier:2015:EMNLP,P18-2050,wuebker-simianer-denero:2018:EMNLP} and dialogue systems \cite{li-EtAl:2016:P16-13,P18-1205}. 
This is because data-driven approaches without personalization tend to suffer from the writer-dependent diversity of probable outputs.
Meanwhile, it is difficult to separate these facets; therefore, to the author's knowledge, \textit{semantic variations} of words among people have not been analyzed independently.
It is necessary to be able to eliminate these unfavorable and meaning-unrelated biases to understand the variation of word meanings among individuals.

To quantify the semantic variations of common words among domains, Tredici and Fern{\'a}ndez (2017) obtained domain-specific word embeddings using the Skip-gram \cite{mikolov2013distributed}, and they analyzed their word embeddings by using multiple metrics such as frequency.
Their approach suffers from \textit{annotation biases} since Skip-gram (or language models in general) attempts to predict words in a sentence given the other words in the sentence; therefore, inputs and outputs are both defined by the same writer.
Ebrahimi and Dou~(2016) obtained personal word vectors using the log-bilinear language model \cite{Maas_aprobabilisticmodelfor}. Their approach also suffers from \textit{annotation bias} because the log-bilinear language model predicts a word according to its previous words; they also use the inputs and outputs defined by the same writer.
Consequently, the same word can have dissimilar vectors by person not only because it has different meanings by individuals but also because it just appears with words in different topics.\footnote{%
As for two user groups, one of Toyota cars and one of Honda cars, although the meaning of the word ``\textit{car}'' used in these two groups is likely to be the same, its embedding obtained by the Skip-gram model from the two user groups will differ since ``\textit{car}'' appears with different sets of words according to each group.}
Additionally, their approach cannot be scalable to the number of domains or writers (reviewers in this study), since it simultaneously learns all the domain- or writer-specific parameters.

Semantic variations of word meanings caused by diachronic \cite{hamilton-leskovec-jurafsky:2016:P16-1,rosenfeld-erk:2018:N18-1,jaidka-chhaya-ungar:2018:Short}, geographic \cite{bamman-dyer-smith:2014:P14-2,C16-1065}, and interdomain  \cite{del2018semantic} differences of text have also been studied.
This study analyzes semantic variations of word meanings at the individual level, particularly, as discussed in  \cite{hamilton-leskovec-jurafsky:2016:P16-1,del2018semantic}, focusing on how semantic variations are correlated with word frequency, dissemination, and polysemy.

Apart from semantic variations, biases related to socially unfavorable prejudices (e.g., the association between the words ``\textit{receptionist}'' and ``\textit{female}'') have been identified, analyzed, and removed from word embeddings \cite{bolukbasi2016man,caliskan2017semantics,diaz2018addressing,swinger2018biases,kaneko-bollegala-2019-gender}. 
In these studies, ``biases'' were defined in terms of political correctness, so they differ from biases in personalized word embeddings targeted in this study.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Personalized Word Embeddings}
\label{sec:model}

The proposed neural network-based model for inducing personalized word embeddings is shown as an overview in~Fig.~\ref{model-overview}.
To clarify semantic variations in meanings of individual words, the following approach was taken: personalized word embeddings for each particular person were learned via representation learning in \textsc{NLP} tasks under the assumption that the words used by individuals were different. 
To implement this approach, two major problems should be solved: (i) what kind of tasks should be used to learn personalized word embeddings and (ii) how to effectively learn them.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 1
\begin{figure}[t]
\begin{center}
\includegraphics{27-2ia13f1.eps}
\end{center}
\hangcaption{Overview of the proposed LSTM network with residual connections for inducing personalized word embeddings via review-target identification through MTL with target-attribute predictions.}
\label{model-overview}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Task for Inducing Personalized Word Embeddings: Review-target Identification}
\label{ssec:induction-task}

As for the task of learning personalized word embeddings, if the task is too simple, a distinction between words may not be required, thus, resulting in the word embeddings being similar or fixed, even if those words were semantically irrelevant.
Moreover, as datasets for the task were likely to contain annotation and selection biases, the induced personalized word embeddings may be contaminated with those biases.
In consideration of these issues, review-target identification was adopted to induce personalized word embeddings.

The majority of review datasets associate \textit{product name}, \textit{metadata}, \textit{user name}, \textit{rating}, and \textit{time} with each \textit{review text}. The focus of review-target identification is the \textit{product name} which was given \textit{a review text} (Table~\ref{tab:task}). Compared with conventional tasks, such as sentiment analysis in which \textit{rating} was estimated given \textit{a review text}, the review-target identification is significantly more difficult because of the large number of target classes and therefore requires a model to understand (or distinctively embed) each word in the review. Moreover, since no annotator was involved when labeling output (review target in this case), annotation bias could be excluded. Moreover, since the number of reviews for each review target is one at most per reviewer in most review datasets, selection bias can, therefore, be minimized. Accordingly, the model could learn only the meanings of words through the review-target identification task.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Method for Inducing Personalized Word Embeddings}

As for effective training of personalized word embeddings, mentioned in \secref{relatedwork}, the scalability and stability of the training became two major problems because (i) it was necessary to learn embeddings for words amplified by the number of reviewers and (ii) the review-target identification task was an extreme multi-class classification with massive review targets.
To solve these problems, a long short-term memory (LSTM) \cite{hochreiter1997long} network with a residual connection similar to ResNet \cite{he:2016:cvpr} was used to obtain personalized word embeddings by using reviewer-specific transformation parameters (\ssecref{personalization}). 
The obtained personalized word embeddings were then fine-tuned in terms of the scalability in accordance with the number of reviewers (\ssecref{fine-tuning}).
After that, the learning of the proposed model was stabilized by applying MTL with target-attribute predictions (\ssecref{multi-task}).
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Reviewer-specific layers for personalization}
\label{ssec:personalization}

First, the model computed the personalized word embeddings $\bm{e}_{w_i}^{u_j}$  of each word $w_i$ in input texts via a reviewer-specific matrix $\bm{W}_{u_j}\in \mathbb{R}^{d\times d}$ and bias vector $\bm{b}_{u_j}\in \mathbb{R}^d$.
Concretely, an input word embedding $\bm{e}_{w_i}$ was transformed to $\bm{e}_{w_i}^{u_j}$ as follows:
\begin{equation}
    \bm{e}_{w_i}^{u_j} = \textrm{ReLU}(\bm{W}_{u_j} \bm{e}_{w_i}+ \bm{b}_{u_j}) + \bm{e}_{w_i}
    \label{eqn:personalization}
\end{equation}
where ReLU was a rectified linear unit function. 
As shown in \eqnref{personalization}, a residual network similar to ResNet \cite{he:2016:cvpr} was used, since semantic variation defined as that from reviewer-universal word embedding.
The use of activation functions allowed for non-linear expressions. In this research, rectified linear units (ReLU) were used as the activation function following the structure of ResNet \cite{he:2016:cvpr}. The advantage of using ReLU is that compared with other general activation functions such as sigmoid and hyperbolic tangent (tanh), the computational cost is lower and the vanishing gradients problem \cite{Hochreiter:91,10.1109/72.279181} (the situation where a deep neural network is unable to propagate gradient from the output back to the layers close to the input) does not occur.
The (vector) value of the first term in Eq.~(1) was limited to 0 or more by using ReLU that might restrict the expression of the personalized word embeddings. However, since an input word embedding $e_{w_i}$ of the second term in Eq.~(1) could represent negative values, its effect on the expression of the personalized word embeddings was limited as a whole.
Sharing the reviewer-specific parameters for transformation across words and employing a residual network enabled the model to learn personalized word embeddings even for infrequent words stably. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Reviewer-universal layers}

Given the personalized word embedding $\bm{e}_{w_i}^{u_j}$ of each word $w_i$ in an input text, the model encoded them through LSTM \cite{hochreiter1997long}.
The LSTM updated the current memory cell $\bm{c}_t$ and hidden state $\bm{h}_t$ according to the following equations:
\begin{align}
    \left[ \begin{array} { c } {\bm{i} _ { t } } \\ { \bm{f} _ { t } } \\ { \bm{o} _ { t } } \\ { \bm{\hat { c }} _ { t } } \end{array} \right] & = \left[ \begin{array} { c } { \sigma } \\ { \sigma } \\ { \sigma } \\ { \tanh } \end{array} \right] \bm{W_\textsc{LSTM}} \cdot \left[ \bm{h} _ { t - 1 };\bm{ e }^{u_j} _ { w_i } \right]\\
    \bm{c} _ { t } & = \bm{f} _ { t } \odot \bm{c} _ { t - 1 } + \bm{i} _ { t } \odot \bm{\hat { c }} _ { t }\\
    \bm{h} _ { t } & = \bm{o} _ { t } \odot \tanh \left( \bm{c} _ { t } \right)
\end{align}
where $\bm{i}_t$, $\bm{f}_t$, and $\bm{o}_t$ were the input, forget, and output gate at time step $t$, respectively. $\bm{e}_{w_i}$ was an input word embedding at time step $t$, and \bm{$W_\textsc{LSTM}$} was a weight matrix. $\bm{\hat { c }}_t$ was the current cell state. 
Operation $\odot$ denoted element-wise multiplication and $\sigma$ was the logistic sigmoid function.
Single-layer bi-directional LSTM (Bi-LSTM) was adopted to use past and future contexts. 
As the representation of the input text $\bm{h}$, Bi-LSTM concatenated the outputs from the forward and backward LSTMs:
\begin{equation}
  \bm{h} =  \left[\overrightarrow{ { \bm{h} _ { L-1 } }} ; \overleftarrow{ { \bm{h} _ { 0 } }} \right] \label{eqn:sentence_repl}
\end{equation}
Here, $L$ denoted the length of the input text, and $\overrightarrow{ \bm{h} _ { L-1 } }$ and $\overleftarrow{ \bm{h} _ {0} }$ denoted the outputs from the forward and backward LSTM at the last time step, respectively.   

Lastly, a feed-forward layer computed output probability distribution $\hat {\mathbf{y}}$ from the representation $\bm{h}$ with weight matrix $\bm{W}_{o}$ and bias vector $\bm{b}_{o}$ as
\begin{equation}
\hat{ \mathbf { y } } = \operatorname { softmax } \left( \bm{W}_{o} \bm{h} + \bm { b }_o \right) \label{eqn:output_single}
\end{equation}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Multi-task learning with target-attribute predictions for stable training}
\label{ssec:multi-task}

Training the model for the target identification task was considered to be unstable because its output space (review targets) was extremely large (more than 50,000 candidates in our datasets).
To mitigate this instability, auxiliary tasks that estimate attributes of the target (item) were set up and solved simultaneously with the target identification task (target task) by MTL.
The target items and target attributes used in this study are later summarized in Table~\ref{tab:task}.
This approach was motivated by the assumption that the attributes of the review item are more coarse-grained than the review item itself and that understanding those related metadata of the target item would contribute to the accuracy of identifying the review target. 

Specifically, independent feed-forward layers were added and used to compute outputs from shared sentence representation $\bm{h}$ defined by \eqnref{sentence_repl} for each auxiliary task (Fig.~\ref{model-overview}).
As shown in Table~\ref{tab:task}, three types of auxiliary tasks were assumed: (1) multi-class classification (the same as the target task), (2) multi-label classification, and (3) regression. 
Multi-task learning under a loss that sums up individual losses for the target and auxiliary tasks was performed.
Cross-entropy loss was used for multi-class classification, a summation of cross-entropy loss of each class was used for multi-label classification, and mean-square loss was used for regression.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Training}
\label{ssec:fine-tuning}

Under the assumption that the number of reviewers is enormous, it is impractical to simultaneously train the reviewer-specific parameters of all the reviewers due to memory limitations.
Therefore, the model was first pre-trained by using all the training data without personalization.
Fine-tuning was then applied only to the parameters in reviewer-specific layers (Fig.~\ref{model-overview}) by training reviewer-independent models based on the reviews written by each reviewer while keeping reviewer-universal layers~(Fig.~\ref{model-overview}) fixed.

More specifically, in the pre-training, the model used reviewer-universal parameters $\bm{W}$ and $\bm{b}$ (instead of $\bm{W}_{u_j}$ and $\bm{b}_{u_j}$) for \eqnref{personalization}. It then initialized the reviewer-specific parameters $\bm{W}_{u_j}$ and $\bm{b}_{u_j}$ in reviewer-specific layers by using $\bm{W}$ and $\bm{b}$.
Finally, the initialized parameters $\bm{W}_{u_j}$ and $\bm{b}_{u_j}$ were fine-tuned per reviewer using the reviews written by the reviewer.
This approach makes the model scalable even to a large number of reviewers.
Note that all the reviewer-universal parameters in the reviewer-universal layers were fixed at the time of reviewer-wise fine-tuning.

Furthermore, all the parameters in reviewer-universal and reviewer-specific layers were subjected to MTL only during the pre-training without personalization. 
Reviewer-specific parameters $\bm{W}_{u_j}$ and $\bm{b}_{u_j}$ of the pre-trained model were then fine-tuned while the target task only was optimized. 
This fixing stops the model introducing \textit{selection bias} into the personalized embeddings; otherwise, the prior output distribution of the auxiliary tasks by individuals could be implicitly learned. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experiments}
\label{sec:experiments}

The target identification task was evaluated first using two review datasets to confirm the effectiveness of the personalized word embeddings induced by the method.
If the model can successfully solve this objective task more accurately than the reviewer-universal model obtained by only pre-training the proposed reviewer-specific model, it is considered that those personalized word embeddings would capture the personal semantic variations of input words.
Next, to verify the usefulness of the personalized word embeddings concerning not only an intrinsic task but also an extrinsic task and to confirm whether the proposed method could remove biases unrelated to the meanings, the proposed model was applied to solve sentiment analysis task.
Personal semantic variation of each word was then defined, and the degree and tendencies of the semantic variation in the obtained personalized word embeddings were analyzed from the same perspectives as discussed in previous studies on semantic variations in word meanings.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Settings}

\noindent\textbf{Datasets}\quad
Datasets containing reviews of beer and services related to foods were adopted for evaluating the proposed method, since there were a variety of expressions that describe what people have sensed with various sensory units in these domains of the datasets. 
The RateBeer dataset, which included a variety of beers, was extracted from ratebeer.com\footnote{\url{https://www.ratebeer.com}} \cite{mcauley2013hidden}. 
Written by reviewers who posted at least 100 reviews, 2,695,615 reviews about 109,912 types of beer were selected. The Yelp dataset, which includes a diverse range of services, was derived from yelp.com.\footnote{\url{https://www.yelp.com/dataset}} The selected reviews were (1) those containing location metadata, (2) those falling under either the ``food'' or ``restaurant'' categories, and (3) those written by a reviewer who posted at least 100 reviews. Consequently, 426,816 reviews of 56,574 services (restaurants or foods) written by 2,414 reviewers in total were extracted. These two datasets were randomly divided into training, development, and testing sets with the ratio of 8:1:1. Hereafter, the former is referred to as \textbf{RateBeer dataset} and the latter as \textbf{Yelp dataset}. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 1
\begin{table}[t]
\caption{Summary of task settings for inducing and evaluating personalized word embeddings}
\label{tab:task}
\input{13table01.tex}
\vspace{-0.5\Cvs}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent\textbf{Target and Auxiliary Tasks}\quad
Table~\ref{tab:task} summarizes the settings of the target and auxiliary tasks.
The target task took a review, and estimated target beer for the RateBeer dataset or services from the Yelp dataset reviewed therein.
Regarding the target attributes for MTL, \textbf{style} with 89 types and \textbf{brewery} with 6,870 types were chosen for multi-class classification, and \textbf{alcohol by volume (ABV)} was chosen for regression in the experiments with the RateBeer dataset. 
As for the Yelp dataset, \textbf{location} with 19 types was used for multi-class classification, and \textbf{category} with 683 types was used for multi-label classification. 

\noindent\textbf{Sentiment Analysis Task}\quad
The settings of the sentiment analysis task are also summarized in Table~\ref{tab:task}.
As for the sentiment analysis task, \textbf{ratings} of given reviews annotated by the reviewers were used for regression. 
The ratings are integers and range from 1 to 20 in the RateBeer dataset and from 1 to 5 in the Yelp dataset. 
This task was solved as a regression task since it is natural to treat the fine-grained ratings as continuous values.

Throughout all the tasks, accuracy was used for classification, and root mean square loss (RMSE) was used for regression tasks. 
For multi-label classification, micro-F1 score was used.

\noindent\textbf{Models and Hyperparameters}\quad
As for the target item and attribute identification tasks, the proposed model (described in \secref{model}) was evaluated in terms of four different settings.\footnote{All models were implemented by using PyTorch (\url{https://pytorch.org/}) version 1.2.0.} 
The differences of the models were (1) whether fine-tuning for personalization was applied and (2) whether the model was trained through MTL before the fine-tuning.  
\tabref{parameters} lists the major hyperparameters. The embedding layer was initialized by Skip-gram embeddings \cite{mikolov2013distributed} pre-trained using review texts of training and validation sets of each dataset. 
The vocabulary for each dataset includes all the words that appeared ten times or more in the dataset. 
For optimization, the models were trained up to 100 epochs with Adam \cite{kingma2014adam}, and the model at the epoch with the best results in the target task on the development set was selected as the test model. 

As for the sentiment analysis task for extrinsically evaluating the obtained personalized word embeddings, another set of models (with the same architecture and hyperparameters) was trained as review-target identification models in Fig.~\ref{model-overview} (except that they have only one feed-forward layer for the target sentiment regression task). The embedding layers of the models are kept fixed after being initialized by the personalized word embeddings obtained from the corresponding review-target identification models with the same settings of personalization and MTL.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 2
\begin{table}[t]
\caption{Hyperparameters of the proposed model}
\label{tab:parameters}
\input{13table02.tex}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Results}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Inducing and evaluating personalized word embeddings by review-target identification}
\label{sec:main-exp}

\tabref{mainresults} lists the results of the review-target identification task using the two datasets. 
It can be inferred from these results that (1) as for the target task, the model with both MTL and personalization outperformed the others, and (2) personalization also improved the performance of auxiliary tasks. 


The model without personalization assumes that the same words written by different reviewers have the same meanings, whereas the model with personalization distinguishes them. The improvement by personalization on the target task with objective outputs partly supports the fact that the same words written by different reviewers have different meanings, even though they are in the same domain (beer, restaurant, and food). Simultaneously solving the auxiliary tasks that estimate attributes of the target item guided the model to understand the target item from various perspectives, like part-of-speech tags of words. 


It should be mentioned here that the reviewer-specific parameters were updated only on the target task by using fine-tuning. 
This means that the improvements in the performance on auxiliary tasks were obtained purely by the semantic variations captured by reviewer-specific parameters.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table3
\begin{table}[t]
\caption{Results on the review-target identification task using the RateBeer dataset and Yelp dataset}
\label{tab:mainresults}
\input{13table03.tex}
\vspace{4pt}\small
Accuracy or RMSE marked with $**$ or $*$ was significantly higher than that of the other models ($p < 0.01$ or $0.01 < p \leq 0.05$ assessed by paired t-test for accuracy and z-test for RMSE).
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Moreover, the model with fine-tuning can successfully solve this objective task more accurately than the reviewer-universal model. This indicates the validity of the proposed method to represent the personalized word embeddings.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Evaluating personalized word embeddings by sentiment analysis}
\label{sec:sub-exp}

\tabref{results-sentiment} lists the results of the extrinsic evaluation of the obtained personalized word embeddings on the sentiment analysis task. 
Similar to the results of the review-target identification task, the results obtained by the proposed model with both multi-task and personalization outperformed those of the other models. 
These results indicate that the personalized word embeddings obtained by the proposed method are useful not only for an intrinsic task used to obtain them but also for an extrinsic task.
In other words, the proposed method can model task-independent personal semantic variations as personalized word embeddings.

Moreover, to confirm whether the personalized word embeddings obtained by the proposed method could remove the biases unrelated to the meanings, the performances of models with different tasks used for personalization were compared using the sentiment analysis task.
To compare with the personalized word embeddings obtained by the proposed model using review-target identification, personalized word embeddings were also obtained using auxiliary tasks considered to be affected by selection bias (because the same output label appears multiple times in a person's training data).
\tabref{results-sentiment-sub} shows that the model with the word embeddings obtained by the proposed method achieved the best performance. The results suggest that the proposed method can suppress the meaning-unrelated biases and obtain task-independent word meanings. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 4
\begin{table}[b]
\hangcaption{Results of sentiment analysis: embedding layers are kept fixed to those of the corresponding models in \tabref{mainresults}}
\label{tab:results-sentiment}
\input{13table04.tex}
\vspace{4pt}\small
RMSE marked with (i) $+$ was significantly better than the model without multi-task and personalization on the RateBeer dataset ($p<0.05$ assessed by z-test), and (ii) $\#$ was significantly better than the model without multi-task and personalization and the model with only personalization on the Yelp dataset ($p<0.05$ assessed by z-test).
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 5
\begin{table}[b]
\hangcaption{Comparison of sentiment analysis results for different tasks used for personalization with the RateBeer dataset and the Yelp dataset}
\label{tab:results-sentiment-sub}
\input{13table05.tex}
\par\vspace{4pt}\small
Embedding layers are kept fixed after personalization on each task. The proposed target identification task is beer and service in each dataset, respectively.
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Impact of the number of reviews for personalization}

The impact of the number of reviews for personalization when solving the review-target identification problem was investigated. The reviewers were first grouped into several bins according to their number of reviews. Classification accuracies for reviews written by the reviewers in the same bin were then evaluated. Classification accuracy of the target task was plotted against the number of reviews per reviewer in Fig.~\ref{accuracy_size}. For example, the plots (and error bars) for $10^{2.3}$ represent the accuracy (variation) of the target identification for reviews written by each reviewer with $n$ reviews ($10^{2.1} \leq n < 10^{2.3}$). 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 2
\begin{figure}[b]
\begin{center}
\includegraphics{27-2ia13f2.eps}
\end{center}
%%%%\label{bin_beer}
%%%%\label{bin_yelp}
\hangcaption{Accuracies of target identification task against the number of reviews per reviewer. In the legend, \textbf{MTL} and \textbf{PRS} stands for multi-task learning and personalization.}
\label{accuracy_size}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Contrary to expectations, for the RateBeer dataset (Fig.~\ref{accuracy_size} (a)), all models obtained lower accuracies as the number of reviews increased. However, as for the Yelp dataset (Fig.~\ref{accuracy_size} (b)), the performance of the models did not deteriorate as the number of reviews increased. We consider that this difference is due to the biases of frequencies in the review targets. Since the RateBeer dataset is heavily skewed, the top-10\% frequent beers account for 74.3\% of all reviews, whereas the top-10\% frequent restaurants in the Yelp dataset only accounted for 48.0\% of the reviews. Therefore, it is more difficult to estimate infrequent targets in the RateBeer dataset, and such reviews tend to be written by experienced reviewers. The model without MTL and personalization obtained slightly lower accuracies even in the case of the Yelp dataset, the model with both MTL and personalization successfully exploited the increased reviews and obtained higher accuracies.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Analysis of Personalized Word Embeddings}
\label{sec:analysis}

The personalized word embeddings were analyzed to determine what kind of personal biases existed in each word. Here, to remove the influences of low-frequent words, only words used by 30\% or more reviewers (excluding stop words) were targeted. 

\textbf{Personal semantic variation}\footnote{%
Unlike the definitions of the semantic variation in existing studies \cite{hamilton-leskovec-jurafsky:2016:P16-1,C16-1065,del2018semantic} that measure the degree of change in the meaning of a word that occurs by diachronic or domain difference of text, personal semantic variation measures how much meanings of a word defined by individuals diverge.} of a word $w_i$ was first defined to determine how the representations of the word differ for each individual as
\begin{equation}
{\frac {1} {|\textrm{U}(w_i)|}}\sum_{u_j \in \textrm{U}(w_i)}(1 - \cos(\bm{e}^{u_j}_{w_i}, \, \overline{\bm{e}}_{w_i}))
\end{equation}
where $\bm{e}^{u_j}_{w_i}$ is the personalized word embedding to $w_i$ of a reviewer $u_j$, $\overline{\bm{e}}_{w_i}$ is the average of $\bm{e}^{u_j}_{w_i}$ for $\textrm{U}(w_i)$, and $\textrm{U}(w_i)$ is the set of the reviewers who used the word $w_i$ at least once in training data.

Three perspectives were focused, namely \textbf{frequency}, \textbf{dissemination}, and \textbf{polysemy}, that have been discussed in the studies on semantic variations of words caused by diachronic or domain differences of text used to obtain them \cite{hamilton-leskovec-jurafsky:2016:P16-1,C16-1065,del2018semantic} (\secref{relatedwork}).
Fig.~\ref{perspectives} shows semantic variations against the three metrics. 
Each x-axis corresponds to log frequency of the word ((a) and (d)), the ratio of the reviewers who used the word ((b) and (e)), and the number of synsets found in WordNet \cite{miller1995wordnet} ver. 3.0 ((c) and (f)), respectively. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 3
\begin{figure}[t]
\begin{center}
\includegraphics{27-2ia13f3.eps}
\end{center}
\hangcaption{Personal semantic variations computed from personal word embeddings for the same words on the two datasets, the RateBeer and the Yelp dataset.  
Their Pearson coefficient correlations are (a) 0.40, (b) 0.22, (c) $-0.08$, (d) 0.25, (e) 0.16, (f) $-0.19$. The trendlines show 95\% confidence intervals obtained from kernel regressions.}
\label{perspectives}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Interestingly, in contrast to the reports by  \cite{hamilton-leskovec-jurafsky:2016:P16-1} on diachronic semantic variations but consistently to the reports by  \cite{del2018semantic} on interdomain semantic variations, semantic variations correlate highly with frequency and dissemination but poorly with polysemy in these results.
This tendency of interpersonal semantic variations can be explained as follows. In the datasets used in these experiments, words related to the five senses, such as ``\textit{soft}'' and ``\textit{creamy},'' frequently appear, and their usage depends on feelings and experiences by individuals. Therefore, their meanings show high semantic variations. As for polysemy, although the semantic variations might change the degree or nuance of the word sense, they do not change its synset (e.g., as introduced in \S~1, even if how ``\textit{sour}'' differs by individuals, the meaning itself does not change). This is because those words are still used only in skewed contexts related to food and drink where word senses do not fluctuate significantly.

\tabref{examples2} lists the top-50 (and bottom-50) words with the largest (and smallest) semantic variations.
As can be seen from the tables, the list of the top-50 words contains many more adjectives (50\% and 38\% on the RateBeer and Yelp dataset, respectively) than the list of the bottom-50 words (20\% and 14\% on the RateBeer and Yelp dataset), which are likely to be used to represent individual feelings that depend on the five senses. 

To determine what kind of words have large semantic variations, the adjectives of the top-50 (and bottom-50) were classified by the five senses, which are \textbf{sight}$_1$ (vision), \textbf{hearing}$_2$ (audition), \textbf{taste}$_3$ (gustation), \textbf{smell}$_4$ (olfaction), and \textbf{touch}$_5$ (somatosensation).
From the results, in the top-50 words in the RateBeer dataset, more words represented each sense (except hearing) than the bottom-50 words. 
Differing from this, the list of top-50 words in the Yelp dataset included fewer words related to the five senses than the RateBeer dataset; however, many adjectives that could apply to various domains (e.g., ``\textit{great},'' and ``\textit{excellent}'') were included. 
This result may be due to the domain size and the lack of reviews detailing specific products in the restaurant reviews contained in the Yelp dataset.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 6
\begin{table}[p]
\hangcaption{The list of top-50 (and bottom-50) words with the largest (and the smallest) semantic variation in the RateBeer and Yelp datasets}
\label{tab:examples2}
\input{13table06.tex}
\par\vspace{4pt}\small
Adjectives are boldfaced and classified into the five senses: \textbf{sight}$_{1}$ (vision), \textbf{hearing}$_{2}$ (audition), \textbf{taste}$_{3}$ (gustation), \textbf{smell}$_{4}$ (olfaction), and \textbf{touch}$_{5}$ (somatosensation).
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 4
\begin{figure}[p]
\begin{center}
\includegraphics{27-2ia13f4.eps}
\end{center}
%%%%\label{pca_beer}
%%%%\label{pca_yelp}
\hangcaption{Two-dimensional representation of the words, \textit{\textbf{grassy}} and \textit{\textbf{great}} in the two datasets, respectively, with the words closest to them in the universal embedding space.}
\label{two-dim representation}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Whether or not some words got confused was also analyzed. The adjective words ``\textit{grassy}'' and ``\textit{great}'' with large semantic variations in each dataset were used as examples. 
Personalized word embeddings were visualized using principal component analysis~(PCA), with the nine adjective words closest to the target words in the universal embedding space in Fig.~\ref{two-dim representation}. 
As can be seen, clusters of ``\textit{grainy},'' ``\textit{bready},'' and ``\textit{doughy}'' in the RateBeer dataset and ``\textit{awesome}'' and ``\textit{excellent}'' in the Yelp dataset were mixed each others, suggesting that words representing the same meaning may differ for each individual.
Moreover, personalized word embeddings, for some words, overlapped those for multiple other words. For example, the personalized word embeddings for ``\textit{bready}'' overlapped the embeddings for ``\textit{grainy}'' and those for ``\textit{doughy}'' in the RateBeer dataset. This demonstrates that the same word could have different meanings by individuals.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusions}

Interpersonal variations in word meanings were focused on, and a hypothesis that words related to the five senses have inevitable personal semantic variations was explored. To verify this, a novel method for obtaining semantic variations by inducing personalized word embeddings through a task with objective outputs was proposed (\secref{model}). Experiments using large-scale review datasets from the RateBeer and Yelp websites showed that the combination of \textsc{MTL} and personalization improved the performance of the review-target identification (\secref{main-exp}).  Experiments on sentiment analysis proved that personalized word embeddings obtained by the proposed method are useful extrinsically (\secref{sub-exp}). This finding shows that the proposed method can capture interpersonal variations of word meanings. The analysis showed that adjectives and words related to the five senses have large interpersonal semantic variations (\secref{analysis}).

As for future studies, relationships between semantic variations and demographic factors, such as gender and age of the reviewers, which are inevitable for expressing individuality, will be analyzed. In addition to the review text, methodologies for acquiring personal semantic variations of word meanings from social media texts like Twitter will be studied.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\acknowledgment

Part of this study was presented at the 20th International Conference on Computational Linguistics and Intelligent Text Processing from 7th to 13th of April 2019 in La Rochelle, France \cite{dai-cicling} and also at the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics from 2nd to 7th of June 2019 in Minneapolis, USA \cite{oba-etal-2019-modeling}. We thank Satoshi Tohda for proofreading the early draft of our paper. This work was partially supported by Commissioned Research (201) of the National Institute of Information and Communications Technology of Japan, JST CREST Grant Number JPMJCR19A4, Japan, and NII CRIS Contract Research 2019.


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\bibliographystyle{jnlpbbl_1.5}
\bibliography{13refs}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{biography}

\bioauthor[:]{Daisuke Oba}{
He received his M.S. degree from the University of Tokyo in 2020. He is currently a doctoral student at the University of Tokyo. His research interests include natural language processing (NLP), in particular, personalized NLP.
}

\bioauthor[:]{Shoetsu Sato}{
A doctoral student at the University of Tokyo, majoring in dialogue and domain adaptation. He received a master's degree from the university in 2017.
}

\bioauthor[:]{Satoshi Akasaki}{
He received his master's degree from the University of Tokyo, Graduate School of Information Science and Technology in 2018. He is currently a doctoral student of the same university and JSPS Research Fellow (DC1). He conducts research on information extraction and dialogue systems.
}

\bioauthor[:]{Naoki Yoshinaga}{
He received his Ph.D. from the University of Tokyo in 2005. He has been JSPS Research Fellow (DC1, PD) from 2002 to 2008, and Associate Professor at the University of Tokyo since 2016. His research interests include computational linguistics and machine learning, in particular natural language processing in the wild.
}

\bioauthor[:]{Masashi Toyoda}{
He received his Ph.D. degree from the Tokyo Institute of Technology in 1999. He worked as Associate Professor at the University of Tokyo from 2006 to 2018, and has been professor since 2018. His research interests include analysis and interactive visualization of Web, social media, and IoT data.
}

\end{biography}



\biodate




\end{document}

