Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is pote
ntially an effective method to address this issue, the pseudo-labels it yields on unlabeled data could induce noise. In this paper, we use two means to alleviate the noise in the pseudo-labels. One is that inspired by the curriculum learning, we refine the conventional self-training to progressive self-training. Specifically, the base model infers pseudo-labels on a progressive subset at each iteration, where samples in the subset become harder and more numerous as the iteration proceeds. The other is that we use a discriminator to filter the noisy pseudo-labels. Experimental results on four SemEval datasets show that our model significantly outperforms the previous baselines and achieves state-of-the-art performance.
Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengt
hs and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword extraction library, are available in the following repository: https://github.com/asahi417/kex
Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care,
and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.
Recurrent Neural Networks (RNN) have been widely used in various Natural Language Processing (NLP) tasks such as text classification, sequence tagging, and machine translation. Long Short Term Memory (LSTM), a special unit of RNN, has the benefit of
memorizing past and even future information in a sentence (especially for bidirectional LSTM). In the shared task of detecting spans which make texts toxic, we first apply pretrained word embedding (GloVe) to generate the word vectors after tokenization. And then we construct Bidirectional Long Short Term Memory-Conditional Random Field (Bi-LSTM-CRF) model by Baidu research to predict whether each word in the sentence is toxic or not. We tune hyperparameters of dropout rate, number of LSTM units, embedding size with 10 epochs and choose the best epoch with validation recall. Our model achieves an F1 score of 66.99 percent in test dataset.
Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts. We investigate German closed noun compounds and focus on the int
eraction of compound-based lexical features (such as frequency and productivity) and terminology-based features (contrasting domain-specific and general language) across word representations and classifiers. Our prediction experiments complement insights from classification using (a) manually designed features to characterise termhood and compound formation and (b) compound and constituent word embeddings. We find that for a broad binary distinction into easy' vs. difficult' general-language compound frequency is sufficient, but for a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features.
Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the cor
responding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.
This research sets out a window in which we look at to discuss vocabularies which
concepts developed from the old dictionary into new meanings. The book binding of the
Jordanian novelist6 Ibrahim Nasr Allah who simplified his novels and made his po
etry
fertilized and meaningful.
The scholar followed vocabularies and recognize their origins and meanings, approaching
the concepts development .On the other hand, he observed what happened and changed in
the area of the modern concepts.
And the searcher sees that the arrangement of importance in the preoccupation with the
practical side, in the process following vocabularies in literary novels poetry and divans,
and inquest to success to reach the aim of study, the researcher follows the way. Baptized
to stand on the most important aspects of semantic evolution of the term.
A reliable and continuous supply of electrical energy is necessary for the functioning
of today’s complex society. Because of the increasing consumption and the extension of
existing electrical transmission networks and these power systems are oper
ated closer and
closer to their limits accordingly the possibilities of overloading, equipment failures and
blackout are also increasing, furthermore, we have an additional obstacle which is that
electrical energy cannot be stored efficiently, so, electrical energy should be generated only
when it's needed.
Due to the fact that world is facing a lack of oil reserves and the difficulties related to
have alternative sources to generate electrical power, then, electrical load forecasting is
considered as a crucial factor in electrical power system either from economical or
technical point of view on both planning and operating levels.
This research introduces a short term electrical load forecasting system by using
artificial neural networks with a simulation in Matlab environment in addition to an
interface for the system and all that is depending on previous load data and weather
parameters in Tartous province.
Economic literature has shown the important and prominent role of financial
development in economic development and growth, through the effective pooling and
allocation of national savings towards investments in support of economic development.
He
nce, it is highly important to look for the real determinants of financial development.
This study investigates the determinants of the financial development of Syria, Lebanon
and Jordan for the period between 1995 and 2014, by applying the method of Ordinary
Least Squares (OLS), to a set of determinants adopted in previous studies. The study found
a statistically significant effect of only three of the nine determinants tested on the level of
private credit by depository institutions (financial sector activity). It also concludes a
statistically significant effect of only five determinants on the level of Liquid Liabilities
(financial sector size). The determinants are: inflation, bank concentration, rule of law,
control of corruption, contract enforcement and improving supervision of banks. Reforms
that contribute in reducing corruption, enforcing contracts, improving the rule of law,
improving supervision on banks, reducing the level of inflation and the level of bank
concentration, are the most important factors that we need to focus on in the long run, to
achieve financial development (size and activity).This in turn contributes to real economic
development in Syria, Lebanon and Jordan.
In this research I studied structural concepts , and how to move into the Arabic
cultural field. I defined these concepts, and the most important people who founded it and
contributed to its construction as a structural approach . Then I traced the
ways that
brought it to the modern Arabic criticism .And the role of competency in the process of
vulnerability and influence. Perhaps the practical steps I have taken has made the structural
approach take its obvious features in our Arabic criticism. This research is presented in
detail by the structural critic Kamal Abu Dib , through his structural applications. And
because it is impossible to take note of all structural aspects that resembled the archipelago
to look up from the top, I tried to study structuralism as a way of thinking and literary
criticism. In particular ,structuralism seeks to discover the relationship between the literary
system ( the text ) and the culture , that the text part of it.
