Textual Mentions Research Articles

Impact of Sarvangasana and Uttanpadasana on Hemodynamic parameters

Many people in these days associate yoga solely with asanas, or relaxing means of exercise, however, asana is only one of many techniques used to heal an individual; only three of the 196 sutras mention asana, and the rest of the text mentions other aspects of yoga such as mindful breathing, meditation, lifestyle and diet changes, visualization, and the use of sound, among others. In the present study we looked into the impact of two different asanas Sarvangasana and Uttanpadasana on hemodynamic parameters in non-obese individuals. It's a comparison analysis of two different types of asanas to see how yoga affects vital signs including blood pressure (systolic and diastolic) and pulse rate. The subject rested in a supine position on the tilt table for 10 to 15 minutes on the days of the experiment (before and after SVGN) until the blood pressure and pulse rate were steady. Before performing each asana (Rest), the participants were measured for vital signs such as systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate (PR), as well as at 1Min, 2Min, in asana and 5min after completing the asana (Rest). In the supine position, at the right upper limb, the pulse rate (PR) and blood pressure (BP) were measured using an LED blood pressure monitor (Omron HEM-7130). Three measurements were taken at 1 Min, 2 Min during asana and 5min after asana, the sum of the three values was determined. In the present study, out of 33 participants performing SVGN, 19(57.7%) were female and 14(42.4%) were male with the mean age group of 37.3 years. Among 17 participants performing UNPN, 7(41.1%) were female and 10 (58.8%) were male with the mean age group 29.9 years. By applying Pearson correlation analysis to these hemodynamic parameters SBP, DBP and PR before and after performing SVGN asana in 33 individuals, the results showed all these three parameters were significantly affected (p<0.05) by sarvangasana. Similarly, through Pearson correlation analysis to the blood parameters SBP, DBP and PR before and after performing UNPN in 17 individuals, the results showed all these three parameters were significantly affected (p<0.05) by uttanpadasana. Yoga's convergence with modern medicine in resource-strapped public health systems may be critical in providing us with comprehensive health care. Economic considerations should be included in future studies, as yoga plays an important role in Primordial prevention. It is necessary to examine the cost-effectiveness of treating lifestyle-related diseases with and without yoga versus medication alone.

Trade Linkage and Transmission of Geopolitical Risks: Evidence from the Peace Progress in 2018

Purpose - Using unexpected changes in geopolitical tensions on the Korean peninsula as a quasinatural experimental setting, we examine whether and how geopolitical risks travel across borders through firm-level imports and exports linkages. We also test whether the effects are driven by either imports or exports and assess whether firms can effectively hedge themselves against geopolitical risks.
 Design/methodology - We focus on a series of unanticipated geopolitical events taken place in Korea in 2018. Making use of the shocks to geopolitical climate, we identify five milestone events toward peace talks. We employ the event studies methodology. We examine heterogenous firm-level stock price reactions around key event dates depending on firms’ exposure to geopolitical risks. As a measure of firms’ exposure to geopolitical risks in Korea, we utilize a text-based measure of firm-level trade links. When a firm announces and discusses its purchase of inputs from Korea or sales of outputs to Korea in their annual disclosure filings, we define a firm to have a trade relationship with Korea and have exposure to Korean geopolitical risks. Similarly, we use a measure of a firm’s hedging policies based on a firm’s textual mention of the use of foreign exchange derivatives in their annual disclosure.
 Findings - We find that U.S. firms that have direct trade links to Korea gained significantly more value when the intensity of geopolitical risks drops compared to firms without such trade links to Korea. The effects are pronounced for firms purchasing inputs from or selling outputs to Korea. We find that the effectiveness of foreign exchange hedging against geopolitical risks is limited.
 Originality/value - We document the international transmission of geopolitical uncertainty through trade linkages. Export links as well as import links serve as important nexus of transmission of geopolitical risks across borders. Hedging strategies involving foreign-exchanges derivatives do not seem to insulate firms again geopolitical risks. With the recent movements of localization and reshuffling of the global value chain, our results suggest a significant impact of geopolitical risks in Korea on the construction of the global value chain.

A simple neural vector space model for medical concept normalization using concept embeddings

ObjectiveMedical concept normalization (MCN), the task of linking textual mentions to concepts in an ontology, provides a solution to unify different ways of referring to the same concept. In this paper, we present a simple neural MCN model that takes mentions as input and directly predicts concepts. Materials and MethodsWe evaluate our proposed model on clinical datasets from ShARe/CLEF eHealth 2013 shared task and 2019 n2c2/OHNLP shared task track 3. Our neural MCN model consists of an encoder, and a normalized temperature-scaled softmax (NT-softmax) layer that maximizes the cosine similarity score of matching the mention to the correct concept. We adopt SAPBERT as the encoder and initialize the weights in the NT-softmax layer with pre-computed concept embeddings from SAPBERT. ResultsOur proposed neural model achieves competitive performance on ShARe/CLEF 2013 and establishes a new state-of-the-art on 2019-n2c2-MCN. Yet this model is simpler than most prior work: it requires no complex pipelines, no hand-crafted rules, and no preprocessing, making it simpler to apply in new settings. DiscussionAnalyses of our proposed model show that the NT-softmax is better than the conventional softmax on the MCN task, and both the CUI-less threshold parameter and the initialization of the weight vectors in the NT-softmax layer contribute to the improvements. ConclusionWe propose a simple neural model for clinical MCN, an one-step approach with simpler inference and more effective performance than prior work. Our analyses demonstrate future work on MCN may require more effort on unseen concepts.

HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning

Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report “Anna stole Ada’s car”, imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.

An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention

Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inference time. In addition, due to the numerous surface variants of biomedical mentions, it is difficult for a single matching module to achieve good results. To address the challenge, we propose an efficient biomedical entity linking method that integrates inter- and intra-entity attention to better capture the information between medical entity mentions and candidate entities themselves and each other, and the model in this paper is more lightweight. Experimental results show that our method achieves competitive performance on two biomedical benchmark datasets, NCBI and ADR, with an accuracy rate of 91.28% and 93.13%, respectively. Moreover, it also achieves comparable or even better results compared to the BERT-based entity linking method while having far fewer model parameters and very high inference speed.

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes.

BackgroundAutomated extraction of symptoms from clinical notes is a challenging task owing to the multidimensional nature of symptom description. The availability of labeled training data is extremely limited owing to the nature of the data containing protected health information. Natural language processing and machine learning to process clinical text for such a task have great potential. However, supervised machine learning requires a great amount of labeled data to train a model, which is at the origin of the main bottleneck in model development.ObjectiveThe aim of this study is to address the lack of labeled data by proposing 2 alternatives to manual labeling for the generation of training labels for supervised machine learning with English clinical text. We aim to demonstrate that using lower-quality labels for training leads to good classification results.MethodsWe addressed the lack of labels with 2 strategies. The first approach took advantage of the structured part of electronic health records and used diagnosis codes (International Classification of Disease–10th revision) to derive training labels. The second approach used weak supervision and data programming principles to derive training labels. We propose to apply the developed framework to the extraction of symptom information from outpatient visit progress notes of patients with cardiovascular diseases.ResultsWe used >500,000 notes for training our classification model with International Classification of Disease–10th revision codes as labels and >800,000 notes for training using labels derived from weak supervision. We show that the dependence between prevalence and recall becomes flat provided a sufficiently large training set is used (>500,000 documents). We further demonstrate that using weak labels for training rather than the electronic health record codes derived from the patient encounter leads to an overall improved recall score (10% improvement, on average). Finally, the external validation of our models shows excellent predictive performance and transferability, with an overall increase of 20% in the recall score.ConclusionsThis work demonstrates the power of using a weak labeling pipeline to annotate and extract symptom mentions in clinical text, with the prospects to facilitate symptom information integration for a downstream clinical task such as clinical decision support.

Aligning and linking entity mentions in image, text, and knowledge base

A picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in a single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent’s background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this article, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we introduce a baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge Entity Linker) for the solution of the VTKEL task. We evaluate the performances of VT-LinKEr on both datasets. We then contribute a supervised algorithm called ViTKan (Visual-Textual-Knowledge Alignment Network). We trained the ViTKan algorithm using features data of the VTKEL1k* dataset. The experimental results on VTKEL1k* and VTKEL30k datasets show that ViTKan substantially outperforms the baseline algorithm.

Dreamworlds, Storyworlds: Narrative Proliferation and the Case of Stjörnu-Odda draumr

Dreamworlds, Storyworlds: Narrative Proliferation and the Case of <i>Stjörnu-Odda draumr</i>

Concept recognition as a machine translation problem

BackgroundAutomated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models have the potential to outperform multi-class classification approaches.MethodsWe systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning through extensive studies of alternative methods and hyperparameter selections. We not only identify the best-performing systems and parameters across a wide variety of ontologies but also provide insights into the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance.ResultsBidirectional encoder representations from transformers for biomedical text mining (BioBERT) for span detection along with the open-source toolkit for neural machine translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies annotated in the CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches.ConclusionsMachine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT shared task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation.

Improving deep learning method for biomedical named entity recognition by using entity definition information

BackgroundBiomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information.Material and methodWe investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score.ResultsEntity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER.ConclusionOur entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle

Abstract Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systematic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and investigate where in the machine learning pipeline such biases can enter a coreference resolution system. We inspect many existing data sets for trans-exclusionary biases, and develop two new data sets for interrogating bias in both crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we will build systems that fail for: quality of service, stereotyping, and over- or under-representation, especially for binary and non-binary trans users.

Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision.

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.

Transregional News Media Coverage in Multilingual Countries: The Impact of Market Size, Source, and Media Type in Switzerland

ABSTRACT In reporting about the regions of a country, news media help define relevant topics and contribute to the integration of society. In multilingual countries, covering news across language boundaries is especially important, yet challenging and costly to perform. To what extent the news media use transregional coverage, that is, look beyond their own language regions, is therefore a particularly relevant question for multilingual societies. This study shows how news media in the three language regions of Switzerland observe each other. A representative sample of news items (N = 25,035) from 47 print and online news outlets was automatically structured based on textual mentions of Swiss place names (n = 189) and linked to manually coded variables. The results show that the degree of transregional coverage differs considerably depending on the size of the language regions, the topic, the source, and the media type. When covering other language regions, news media focus on sports, and news media from the two smaller media markets rely heavily on news agency reports. Both public service media and tabloid media use much transregional coverage, with the former focusing on politics and the latter on sports. Thus, media types contribute to integration differently.

Towards zero-shot cross-lingual named entity disambiguation

In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest. In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities. Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information. We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R. We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English. For instance, Spanish is more effective when training a zero-shot XNED system that disambiguates Basque mentions with respect to an English knowledge graph.

Knowledge-Driven Joint Posterior Revision of Named Entity Classification and Linking

In this paper we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content. More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called JPARK, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination. We present a comprehensive evaluation of JPARK along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.

Medical concept normalization in French using multilingual terminologies and contextual embeddings

IntroductionConcept normalization is the task of linking terms from textual medical documents to their concept in terminologies such as the UMLS®. Traditional approaches to this problem depend heavily on the coverage of available resources, which poses a problem for languages other than English. ObjectiveWe present a system for concept normalization in French. We consider textual mentions already extracted and labeled by a named entity recognition system, and we classify these mentions with a UMLS concept unique identifier. We take advantage of the multilingual nature of available terminologies and embedding models to improve concept normalization in French without translation nor direct supervision. Materials and methodsWe consider the task as a highly-multiclass classification problem. The terms are encoded with contextualized embeddings and classified via cosine similarity and softmax. A first step uses a subset of the terminology to finetune the embeddings and train the model. A second step adds the entire target terminology, and the model is trained further with hard negative selection and softmax sampling. ResultsOn two corpora from the Quaero FrenchMed benchmark, we show that our approach can lead to good results even with no labeled data at all; and that it outperforms existing supervised methods with labeled data. DiscussionTraining the system with both French and English terms improves by a large margin the performance of the system on a French benchmark, regardless of the way the embeddings were pretrained (French, English, multilingual). Our distantly supervised method can be applied to any kind of documents or medical domain, as it does not require any concept-labeled documents. ConclusionThese experiments pave the way for simpler and more effective multilingual approaches to processing medical texts in languages other than English.

What's in a Proper Name? Whether Leaders Matter

International relations research is animated by the notion that leaders matter. Existing approaches rely on proxies such as regime type to infer the importance of individuals. We propose a new measure, one based on leader mentions relative to country mentions in the relevant text. We construct the measure by scraping speeches made on the floor of U.S. Congress. We demonstrate the promise of our proposal by testing propositions developed in the study of election interventions. We show that policy divergence between the positions of candidates running in elections abroad gets them more noticed by Congress. We also show that leader mentions contain words associated with candidate interventions and concern with democracy. We demonstrate that as democracy matures, individual leader salience dims and opposition leaders catch up on visibility with incumbents. We make our software available to be used by researchers. Our method and measure can help advance research on the importance of leaders - and individuals - in politics more broadly.

Restoring the Synagogue Soundtrack: Jewish Religious Music in Nineteenth Century America by Judah M. Cohen

Reviewed by: Restoring the Synagogue Soundtrack: Jewish Religious Music in Nineteenth Century America by Judah M. Cohen Alon Schab Restoring the Synagogue Soundtrack: Jewish Religious Music in Nineteenth Century America. By Judah M. Cohen. Bloomington: Indiana University Press, 2019. [318 p., 42 b&w illus. ISBN 978-0-253-04021-3. $25] Many topics in music history are strewn with prejudice and received wisdom. Jewish music, especially nineteenth-century Jewish music that lies outside the perceived central-European 'mainstream', is a clear example of such a topic. Judah M. Cohen's new book succeeds in transcending received wisdom and sketches a convincing and, in many ways, pioneering narrative. To test the freshness of the narrative, one needs only to count Cohen's references to Abraham Z. Idelsohn's Jewish Music in Its Historical Development (New York: Holt, 1929)—four fleeting mentions in the main text, (excluding the Introduction and Conclusion). Idelsohn's absence from a book on nineteenth-century Jewish music has been almost inconceivable until recently, and Cohen earned this independence after having critically studied Idelsohn's legacy ('Rewriting the Grand Narrative of Jewish Music: Abraham Z. Idelsohn in the United States', The Jewish Quarterly Review 100, no. 3 [2010]: 417–453). While the focus of the study is on a chain of pathbreaking tune books that were published along the second half of the nineteenth century, the bulk of primary sources consists of newspaper reports, letters, and contracts. The book is, therefore, useful not only for musicologists, but also to historians, historians of American Jewry, historians of religion, as well as for those who seek to understand some of the undercurrents in contemporary American-Jewish culture. Cohen's book follows seven 'scenes' in the development of Jewish music in America, telling the stories of individuals who, in their careers and published works, demonstrate the various forces that operated on Jewish synagogal music during the formative nineteenth century. Obviously, the main forces that shaped music making in that period were theological and liturgical—the struggle between orthodox and reformed tendencies (which rarely went consistently in one direction, as Cohen convincingly shows). But the picture was, of course, much more complicated and also included the tension between the conflicting ideals of professional and congregational singing; between following revered central-European models and the will to break free from these models; between unity and independence of communities; and issues of prestige (both communal and individual). It is not easy to follow the plethora of synagogue names (and their cities), cantors and rabbis, publication titles, dates of birth, immigrations, installations, publications, and deaths. I found it useful to take notes as I read through the book, listing all those details in a way that allowed me to grasp the chronological and geographical connections between the various scenes. And yet, Cohen makes 'no claims at comprehensive [End Page 188] overview', but 'seeks to create a structure for further correction, critique, and detail' (p. 14). He confesses that the South and the West deserve their own studies, that his narrative begins only in the 1840s, and that women's significant role in the described process cannot be properly assessed based on the primary materials he used. Properly stated at the outset of the study, these limitations do not invalidate the essence of the book's narrative. Cohen's story begins with the shift from British-influenced models for choral singing to central-European approaches to synagogal music. The adoption of German hymn singing (and its wider social and philosophical implications) is discussed through the work of Wilhelm Fischer. Gustav M. Cohen aimed his The Sacred Harp of Judah (1864) 'for the use of Synagogues, Schools, and Home', a Bildung ideal that differed significantly from professionalised tune books like Sulzer's and Naumbourg's. The discussion of the Bildung-influenced tune book is complemented by a review of the works of Gustav S. Ensel and Simon Hecht. The following chapter is short but dramatic, as it can be seen as a real watershed point in which American leaders of synagogue music emerged as a significant branch in the spread of Sulzerian style (and authority). This brings Cohen to discuss the four-volume Zimrath Yah, edited...

The Uncanny Meeting Point of Languages: Hebrew in G. Shofman and David Vogel’s Vienna

G. Shofman and David Vogel wrote beautiful literature from the multilingual Vienna in the interwar period. Seemingly, they wrote monolingual Hebrew texts, but this Hebrew encodes all the other languages, and all the gaps, all the places that the chosen language cannot reach. The diverse usage of languages in the Hebrew texts was not (just) a result of a forced lingual situation but also held great poetic values.Reading Shofman and Vogel’s Hebrew prose texts today is not a fluent read, because of gaps that affect the reading: (1) periodical gaps are when the text mentions names, events and situations that were clear to readers at the time of publication, but will not be understood today without context. The second are the (2) spatial gaps: the text describes a space that the reader does not know, and so, if it contains poetic values, they will not be understood. The (3) lingual gaps that can be syntactical but mainly lexical. The most interesting gaps are (4) the unheimlich gaps, the uncanny feeling when the known and the unknown, the safe and the threatening meet in order to create the meta‑lingual meaning of the text, and change its poetic values.This paper demonstrates the presence of these four gaps in one short story by G. Shofman (In time of crisis), and in a novel written by David Vogel (Married life). In particular, to show what impact does the language(s) of the text have on past and current readers, and the way the authors express their attitude regarding the Hebrew language.

Diversified Semantic Attention Model for Fine-Grained Entity Typing

Fine-grained entity typing, which aims to assign specific types to entity mentions in text, is attracting increasing attention in the field of natural language processing (NLP). However, it is quite a challenging problem due to the highly ambiguous nature of many entity mentions. Most existing entity typing methods based on attention mechanism generally extract the salient features separately from the entity mention and the contextual words. However, these approaches suffer from two main limitations : (1) They ignore the rich information contained by entity mentions when applying the attention mechanisms. (2) They do not consider the diversity of attention processes which can be beneficial in finding the discriminative features. To address these issues, we propose the diversified semantic attention model (DSAM) for fine-grained entity typing, and the main novelties are: (1) It explicitly pursues the diversity of attention and is able to maximally gather discriminative information. (2) It integrates two level attentions—the mention-level attention and the context-level attention— to jointly capture the rich information from mentions and contexts to enhance their mutual promotions. (3) It combines the attention maps constraint and the attention segments constrain to exploit the subtle semantic differences for distinguishing the subtypes. Importantly, the proposed DSAM approach can be trained end-to-end without employing ad-hoc features or post-processing. Extensive experiments using three benchmark datasets demonstrated that our DSAM approach achieves competitive performance compared to the current state-of-the-art methods used for fine-grained entity typing.