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2022 ◽  
pp. 1026-1048
Author(s):  
Sugandha Kaur ◽  
Bidisha Som

Previous studies show that the presence of a context word in picture naming either facilitates or interferes with the naming. Although there has been extensive research in this area, there are many conflicting findings, making it difficult to reach firm conclusions. This chapter aims to delve into the dynamics of such processing and understand the nuances involved in experimental manipulations that may influence the pattern of results and be responsible for differences in outcomes. The series of experiments reported in this chapter was aimed at refining our understanding of mechanisms in the way bilinguals process language production by examining two different paradigms—primed picture naming and picture-word interference. This was investigated by manipulating both the type of visual context words presented with the picture and the time interval between the presentation of context word and picture. The results are interpreted within the context of current models of lexical access.


2021 ◽  
Vol 13 (1) ◽  
pp. 51-65
Author(s):  
Adriana Mezeg

This article first gives an overview of the different uses of French apposition and then focuses on nominal appositions, a kind of supplementive clause introduced by a nominal group (NG) without an article. Only translations of initial nominal appositions are examined, i.e. those which are placed at the beginning of the sentence and where the content of the initial structure is expressed by an apposition or NG as the subject. In this context, word order and the use of commas are discussed, which are often of importance for Slovenian language users. Based on the FraSloK corpus, the following conclusions can be drawn: (a) sentence-initial position is maintained much more often in novels than in newspaper articles; (b) the expression of the content of initial structures with an apposition and an NG, which functions as a subject, is fairly evenly represented in more than half of the cases from newspaper articles, while in novels the subject function is prominent; (c) apart from the change in sentence position, Slovenian apposition corresponds to the source structure, and when its content is expressed by an NG with subject function, there are changes at different levels compared to French; (d) the (non-)use of the comma cannot be satisfactorily justified on the basis of the present corpus, but the examples suggest that it is based on translators’ personal choices and also depends on the possibilities of expression in the target language. Suggestions have already been made to change the rules and usage examples, which are not tenable in our cases, and would require further consideration.


Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2210
Author(s):  
Zhongyuan Zhao ◽  
Weiguang Sheng ◽  
Jinchao Li ◽  
Pengfei Ye ◽  
Qin Wang ◽  
...  

Modulo-scheduled coarse-grained reconfigurable array (CGRA) processors have shown their potential for exploiting loop-level parallelism at high energy efficiency. However, these CGRAs need frequent reconfiguration during their execution, which makes them suffer from large area and power overhead for context memory and context-fetching. To tackle this challenge, this paper uses an architecture/compiler co-designed method for context reduction. From an architecture perspective, we carefully partition the context into several subsections and only fetch the subsections that are different to the former context word whenever fetching the new context. We package each different subsection with an opcode and index value to formulate a context-fetching primitive (CFP) and explore the hardware design space by providing the centralized and distributed CFP-fetching CGRA to support this CFP-based context-fetching scheme. From the software side, we develop a similarity-aware tuning algorithm and integrate it into state-of-the-art modulo scheduling and memory access conflict optimization algorithms. The whole compilation flow can efficiently improve the similarities between contexts in each PE for the purpose of reducing both context-fetching latency and context footprint. Experimental results show that our HW/SW co-designed framework can improve the area efficiency and energy efficiency to at most 34% and 21% higher with only 2% performance overhead.


Author(s):  
Songtao Fang ◽  
Zhenya Huang ◽  
Ming He ◽  
Shiwei Tong ◽  
Xiaoqing Huang ◽  
...  

Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.


10.2196/17903 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e17903
Author(s):  
Alejandro Garcia-Rudolph ◽  
Joan Saurí ◽  
Blanca Cegarra ◽  
Montserrat Bernabeu Guitart

Background The World Health Organization’s International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing “context” as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them. Objective The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities. Methods Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde’s updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation. Results We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast – 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps “to know accessibility before you go” to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was “resilience,” recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the “resilience” definition. Conclusions This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.


Author(s):  
Nur Nabilah Abdullah ◽  
◽  
Rafidah Sahar ◽  

Intercultural communication refers to interaction between speakers of different backgrounds, such as different linguistic and cultural origins (Kim 2001). Interaction in face-to face situations has demonstrated that spoken language involves both verbal and semiotic resources for social action. Semiotic resources that include use of talk, gestures, eye gaze and other nonverbal cues can convey semantic content and can become a crucial point in conversation (Hazel et al. 2014). Drawing on a Aonversation Analysis (CA) approach, we explore how participants employed semiotic resources in word searches activities in an intercultural context. Word searches are moments in interaction when a speaker’s turn is temporarily ceased as the speaker displays difficulty in searching for appropriate linguistic items so as to formulate the talk (Schegloff et al. 1977; Kurhila 2006). In this study, naturally occurring interactions in a multilingual setting were video recorded. The participants were Asian university students with different language backgrounds. The findings suggest that multilingual participants mutually collaborate by utilizing verbal affordances, gaze, gesture and other nonverbal cues as useful semiotic resources in the meaning-making process, and thus resolving word search impediments to facilitate intercultural interaction.


2020 ◽  
Vol 39 (4) ◽  
pp. 5535-5545
Author(s):  
Jinying Cui

The corpus software has many functions, such as keyword retrieval, context co-occurrence, word list generation and word frequency statistics. It can quickly and accurately provide various corpus and information, such as word-formation collocation, context, word frequency and so on. In this paper, the author analyzes the application of deep learning and target visual detection in English vocabulary online teaching. Deep learning is a kind of machine learning algorithm which includes multi-layer non-linear mapping and tries to obtain high-level abstract representation of data. By extracting features from information, the identifiable components in the image can be extracted. The results show that the application of corpus in College English vocabulary teaching can promote students’autonomous use of corpus in English vocabulary learning. The simulation experiment improves the performance of the system by choosing parameters, and the classification accuracy is more than 90%. Corpus can enable students to learn real and natural language and master natural collocation. At the same time, corpus can help students understand the semantic and pragmatic norms of words in communication and recognize the characteristics of register variants. Future research can use Map-reduce technology to accelerate the training process, save training time and test more hyperparameters.


2020 ◽  
Vol 10 (6) ◽  
pp. 2052
Author(s):  
Dianyuan Zhang ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Hongli Pei ◽  
Wenqing Wu ◽  
...  

Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.


2020 ◽  
Author(s):  
Alejandro Garcia-Rudolph ◽  
Joan Saurí ◽  
Blanca Cegarra ◽  
Montserrat Bernabeu Guitart

BACKGROUND The World Health Organization’s International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing “context” as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them. OBJECTIVE The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities. METHODS Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde’s updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation. RESULTS We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast – 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps “to know accessibility before you go” to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was “resilience,” recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the “resilience” definition. CONCLUSIONS This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.


2020 ◽  
Vol 5 (1) ◽  
pp. e002000 ◽  
Author(s):  
Moise Chi Ngwa ◽  
Alemu Wondimagegnehu ◽  
Ifeanyi Okudo ◽  
Collins Owili ◽  
Uzoma Ugochukwu ◽  
...  

IntroductionIn August 2017, a cholera outbreak started in Muna Garage Internally Displaced Persons camp, Borno state, Nigeria and >5000 cases occurred in six local government areas. This qualitative study evaluated perspectives about the emergency response to this outbreak.MethodsWe conducted 39 key informant interviews and focus group discussions, and reviewed 21 documents with participants involved with surveillance, water, sanitation, hygiene, case management, oral cholera vaccine (OCV), communications, logistics and coordination. Qualitative data analysis used thematic techniques comprising key words in context, word repetition and key sector terms.ResultsAuthorities were alerted quickly, but outbreak declaration took 12 days due to a 10-day delay waiting for culture confirmation. Outbreak investigation revealed several potential transmission channels, but a leaking latrine around the index cases’ house was not repaired for more than 7 days. Chlorine was initially not accepted by the community due to rumours that it would sterilise women. Key messages were in Hausa, although Kanuri was the primary local language; later this was corrected. Planning would have benefited using exercise drills to identify weaknesses, and inventory sharing to avoid stock outs. The response by the Rural Water Supply and Sanitation Agency was perceived to be slow and an increased risk from a religious festival was not recognised. Case management was provided at treatment centres, but some partners were concerned that their work was not recognised asking, ‘Who gets the glory and the data?’ Nearly one million people received OCV and its distribution benefited from a robust infrastructure for polio vaccination. There was initial anxiety, rumour and reluctance about OCV, attributed by many to lack of formative research prior to vaccine implementation. Coordination was slow initially, but improved with activation of an emergency operations centre (EOC) that enabled implementation of incident management system to coordinate multisectoral activities and meetings held at 16:00 hours daily. The synergy between partners and government improved when each recognised the government’s leadership role.ConclusionDespite a timely alert of the outbreak, delayed laboratory confirmation slowed initial response. Initial responses to the outbreak were not well coordinated but improved with the EOC. Understanding behaviours and community norms through rapid formative research should improve the effectiveness of the emergency response to a cholera outbreak. OCV distribution was efficient and benefited from the polio vaccine infrastructure.


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