Ambiguous Words Research Articles

Space

A thing, being placed in space, like in aitch-two-oh, despising the risk, longs to displace the space; but your eye does not notice the splashes of space on the floor. Brodsky. Dedication to a chair ‘Space’ is one of the most popular and, at the same time, the most ambiguous words in the theory and practice of architecture. Perhaps there is not a single philosopher, urbanist or designer who would not use it in their work. The materials of the current issue of our journal include a wide range of reflections on the essence and properties of space – from criticism of established ideas in practical architecture to reflections on the metrics of structured, meaning-forming space of the modern city. Space in our consciousness is inextricably linked to the concept of North and South. The hot, arid and antiquities-rich South and the cold, seemingly unfriendly, but in its own way beautiful and promising North. We have divided these notions, although in fact there is no unambiguous border between them. It is even more difficult to define the boundary between the West and the East. Nevertheless, we include a selection of materials from our regular authors, researchers of architecture of the Far East. And the usual coordinates disappear completely when we start to fantasize about architecture in outer space. As usual, we do not pretend to exhaustively cover the immense topic of Space. Rather, our goal was to ask as many meaningful questions as possible and to bring many different opinions into contact.

A corpus of Chinese word segmentation agreement

The absence of explicit word boundaries is a distinctive characteristic of Chinese script, setting it apart from most alphabetic scripts, leading to word boundary disagreement among readers. Previous studies have examined how this feature may influence reading performance. However, further investigations are required to generate more ecologically valid and generalizable findings. In order to advance our understanding of the impact of word boundaries in Chinese reading, we introduce the Chinese Word Segmentation Agreement (CWSA) corpus. This corpus consists of 500 sentences, comprising 9813 character tokens and 1590 character types, and provides data on word segmentation agreement at each character position. The data revealed a high level of overall segmentation agreement (92%). However, participants disagreed on the position of word boundaries in 8.96% of the cases. Moreover, about 85% of the sentences contained at least one ambiguous word boundary. The character strings with high levels of disagreement were tentatively classified into three categories, namely the morphosyntactic type (e.g., “反映–了”), modifier–head type (e.g., “科學–教育”), and others (e.g., “大力–支持”). Finally, the agreement scores also significantly influenced reading behaviors, as evidenced by analyses with published eye movement data. Specifically, a high level of disagreement was associated with longer single fixation durations. We discuss the implications of these results and highlight how the CWSA corpus can facilitate future research on word segmentation in Chinese reading.

Readers use recent experiences with word meanings to support the processing of lexical ambiguity: Evidence from eye movements.

Fluent reading comprehension demands the rapid access and integration of word meanings. This can be challenging when lexically ambiguous words have less frequent meanings (e.g., the dog meaning of boxer). Indeed, readers fixate on lexically ambiguous words that are disambiguated toward their subordinate meaning for longer than matched control words embedded within identical sentence contexts. Word-meaning priming studies have shown that participants flexibly use recent experiences with ambiguous words to guide their interpretation when these words are presented in isolation, even after substantial delays. However, word-meaning priming paradigms have almost always used artificial tasks to measure word-meaning availability and we do not therefore know how priming would support lexical processing when reading for comprehension. Thus, we conducted two eye-movement experiments to examine word-meaning priming during sentence reading. Both experiments employed a 2 (ambiguity: low-ambiguity control vs. high-ambiguity) × 2 (priming: unprimed vs. primed) within-participants design, with either a 1-min delay (Experiment 1; N = 28) or a 30-min delay (Experiment 2; N = 60) between prime and test sentences. Both experiments showed greater reductions in go-past times and total reading times following priming for high-ambiguity target words than matched low-ambiguity control words, indicating that recent encounters support the processing of word meanings during sentence reading and that this effect extends beyond the simple repetition effect observed for low-ambiguity control words. This illustrates the remarkable flexibility of the human language system in using diverse input to refine stored lexical knowledge even in skilled readers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mapping Disciplinary Competencies and Learning Outcomes to the Competency-Based Veterinary Education Framework Using Veterinary Pharmacology as an Example.

The competency-based veterinary education (CBVE) framework describes essential domains of competence and related abilities for veterinary graduates. Translating these outcomes into daily teaching is a challenge, particularly regarding the underpinning basic and clinical science knowledge. In this article, we identified a lack of specific reference to the selection and use of drugs within the CBVE framework; this requires pharmacological knowledge and pharmacology-specific competencies. To fill the gap and provide guidance to veterinary pharmacology educators, we first identified competencies within the CBVE framework relevant to the field of veterinary pharmacology. We then mapped the Day One competencies in veterinary pharmacology published by Werners and Fajt in 2021 to the pharmacology-relevant CBVE competencies. This exercise has led to identifying gaps, redundancies, and a lack of reference to clinical practice within the Day One competencies in veterinary pharmacology, as well as gaps and ambiguous wording within the CBVE framework. Further research is necessary to update the Day One competencies in veterinary pharmacology, align basic and clinical pharmacology concepts and skills with the CBVE framework, embed pharmacology-specific competencies into teaching, and identify progression milestones that guide students toward safe prescribing and the appropriate and effective use of drugs.

DiBiMT: A Gold Evaluation Benchmark for Studying Lexical Ambiguity in Machine Translation

Abstract Despite the remarkable progress made in the field of Machine Translation (MT), current systems still struggle when translating ambiguous words, especially when these express infrequent meanings. In order to investigate and analyze the impact of lexical ambiguity on automatic translations, several tasks and evaluation benchmarks have been proposed over the course of the last few years. However, work in this research direction suffers from critical shortcomings. Indeed, existing evaluation datasets are not entirely manually curated, which significantly compromises their reliability. Furthermore, current literature fails to provide detailed insights into the nature of the errors produced by models translating ambiguous words, lacking a thorough manual analysis across languages. With a view to overcoming these limitations, we propose Disambiguation Biases in MT (DiBiMT), an entirely manually curated evaluation benchmark for investigating disambiguation biases in eight language combinations and assessing the ability of both commercial and non-commercial systems to handle ambiguous words. We also examine and detail the errors produced by models in this scenario by carrying out a manual error analysis in all language pairs. Additionally, we perform an extensive array of experiments aimed at studying the behavior of models when dealing with ambiguous words. Finally, we show the ineffectiveness of standard MT evaluation settings for assessing the disambiguation capabilities of systems and highlight the need for additional efforts in this research direction and ad-hoc testbeds such as DiBiMT. Our benchmark is available at: https://nlp.uniroma1.it/dibimt/.

Expressive Writing About One's Trauma Increases Accessibility of Cannabis Information in Memory Among Trauma-Exposed Individuals.

Trauma survivors are more likely than others to use cannabis, and post-traumatic stress disorder (PTSD) commonly co-occurs with cannabis use disorder (CUD). Automatic memory associations between trauma reminders and cannabis use have been suggested as contributing mechanisms. These associations can be studied experimentally by manipulating trauma cue exposure in a cue-reactivity paradigm (CRP) and examining effects on the accessibility of cannabis information in memory in trauma survivors with and without PTSD. Cannabis users with trauma histories (N = 202) completed a PTSD measure (PTSD Checklist-5) and were randomized to a trauma or neutral expressive writing task as an online CRP. Next, participants completed a cue-behavior word association task, which involved presentation of a series of ambiguous cue words to which participants provided the first word that came to mind. Some of these ambiguous cues pertained to cannabis (e.g., weed, pot) and some to other substances (e.g., blow, shot). This task was scored by two independent raters. Linear regression models tested the hypothesized main and interactive effects of CRP condition (trauma, neutral) and PTSD group (probable PTSD, no PTSD) on the number of cannabis and other substance responses generated. Main effects of CRP condition were found for cannabis responses (b = 0.41, p = .048; trauma > neutral) but not other substance responses. Unexpectedly, no main effects or interactions of PTSD group were observed for either outcome. In cannabis users with trauma histories, writing about one's trauma specifically activates greater accessibility of cannabis-related information in memory, regardless of PTSD.

Demystifying Ambiguous Words in Request for Proposals of Information Systems in Japan

Disagreements in interpreting words in software requirements specifications (SRSs) can lead to project failure. Various approaches to identifying and preventing ambiguous words in SRSs have been proposed. Yet, it is unclear which ambiguous words are used in the actual SRSs and to what extent they need to be modified. This paper quantitatively analyzes existing SRSs to clarify (1) how many ambiguous words are included in SRSs and (2) how many of these words require correction. This paper targets the Request for Proposals (RFPs), which describe the initial requirements of 40 systems of local governments, libraries, universities, and hospitals in Japan. Ten ambiguous Japanese words were analyzed. The result shows that “juubun” (sufficient) appeared most frequently, and 42% required correction when this word was used. The result also indicates that the number of ambiguous words varied greatly among the RFPs and that larger RFPs did not necessarily contain more ambiguous words.

Homophone priming in bilingual preference formation.

Homophone (HP) priming occurs when phonologically ambiguous words persistently coactivate their contextually irrelevant meanings. If suppressing those meanings fails, they subliminally bias preferences. Yet, it is unclear if prior findings generalize beyond individual words and to bilingual contexts. This has implications for consumer behavior and the debate on differences between first (L1) and second language (L2) lexical processing. We present four multi-item experiments with German-English bilinguals. An initial eye-tracked primed choice task established that homophones affect decision making. Three visual preference experiments with written and/or auditory primes and high- or low-proficiency L2 users found that homophones bias preferences more in L1 than L2. The L1-L2 gap widened if listening or low proficiency made suppression more difficult. We argue that the interplay between reduced suppression in L2 as predicted by activation-suppression models and lower subjective frequency of L2 homophones assumed by the frequency lag hypothesis explain the size of the L1-L2 priming gap. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

HiNER: Hierarchical feature fusion for Chinese named entity recognition

Named Entity Recognition (NER) aims to extract structured entity information from unstructured textual data by identifying entity boundaries and categories. Chinese NER is more challenging than that of English due to the complex structure and ambiguous word boundaries, as well as nested and discontinuous occurrences of entities. Previous Chinese NER methods are limited by their character-based approach and dependence on external lexical information, which is often non-contextualized, leading to the introduction of noise and potentially compromising model performance. This paper proposes a novel Chinese NER model, HiNER, which leverages external semantic enhancement and hierarchical attention fusion. Specifically, we initially formulate the Chinese NER as a character–character relation classification task, thoroughly taking into account the cases of nested and discontinuous entities. Then, by incorporating syntactic information, we develop a Triformer module that is used to better integrate Chinese character, lexical, and syntactic embeddings, carefully considering the impact of external semantic enhancement on the original text embeddings and reducing extrinsic information interference to some extent. In addition, through the fusion of local and global attention mechanisms, the representation of character–character relationships is enhanced, allowing for the effective capture of semantic features at various hierarchical levels within the Chinese context. We conduct extensive experiments on seven Chinese NER datasets, and the results indicate that the HiNER model achieves state-of-the-art (SOTA) performance. The outcomes also confirm that external semantic enhancement and hierarchical attention fusion can provide better assistance in accomplishing the Chinese NER task.

Development and content validation of a questionnaire identifying patients’ functional priorities and abilities after hip or knee arthroplasty

Purpose To develop a self-report questionnaire evaluating functional priorities after hip or knee arthroplasty and evaluate patients’ understanding of its items and conceptual relevance. Methods A self-report questionnaire was first developed based on the International Classification of Functioning, Disability, and Health (ICF) core set for osteoarthritis (OA). In the second stage, two research physiotherapists thoroughly reviewed and refined the questionnaire, and another physiotherapist conducted cognitive think-aloud interviews with 18 patients to assess the face and content validity of the questionnaire. Results All categories and corresponding activities of ICF core set for OA were used to develop the questionnaire. Several questionnaire issues were identified and addressed. Most challenges were related to comprehension, followed by item ordering and visual elements. Patients identified ambiguous wording which we subsequently simplified. Ten activities of the core set were excluded due to lack of face validity, two activities were added, and four activities were modified. Conclusion The findings suggest that the ICF core set for OA needs to be adjusted for patients undergoing hip or knee arthroplasty and highlight the feasibility of applying a modified core set to assess functional priorities after hip or knee arthroplasty.

Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation

This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms, historical attention information, dynamically obtain information related to the generated words, and constraint mechanisms were introduced to embed semantic information, represent polysemy, and annotate semantic roles of verbs. This article used the Workshop on MT (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, and OpenSubtitles corpus, and enhanced the data in the corpora. The improved NMT model was compared with traditional NMT models, Rule-Based MT (RBMT), and Statistical MT (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in five corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in five corpora was 0.90. The improved NMT model in this article can effectively improve the accuracy of verb recognition in MT, providing new methods for verb recognition in MT.

Challenges of Using Historical Aurora Observations for the Reconstruction of Solar Activity before the 19th Century, Especially during and near the Maunder Minimum

In order to complement gaps in the surveillance of solar activity in historical times, various proxies are used to reconstruct past solar cycles and long-term maxima and minima of solar activity, the most famous being the Maunder Minimum (MM), which is usually defined to span the period between the years 1645 and 1715. We explain the problems within existing data bases and call upon trying to find the original sources of Schröder, since his aurorae catalog spans the whole MM and contradicts what has been deduced from more used compilations. We take a critical look at the proposed source-critical scheme introduced by Neuhäuser and Neuhäuser and show it to be counterproductive because it largely ignores the source situation, i.e., the scientific understanding of the reporters of times long past and their intentions. While historical sunspot and aurora reports can be useful to fine-tune our knowledge of solar activity in times before the onset of systematical surveillances, they should not be used as an index of solar activity, since they cannot be quantitatively expressed due to the non-scientific manner of the reports and ambiguous wording. Reconstructions based on cosmogenic isotopes are significantly preferable for establishing the level of solar activity in the past. The conclusions reached by this review should be regarded as a caution against expecting important conclusions to emerge from low quality data.

Enhancing Word Sense Disambiguation for Amharic homophone words using Bidirectional Long Short-Term Memory network

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fidel's ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (both pronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, which tackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m'hur) and ምሑር(m'hur), በአል(be'al) and በዢል(be'al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), decision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limited datasets.

Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP.

This study suggests a way to utilize the existing medical ontology and natural language processing techniques to extract major medical concepts from lay vocabularies of health consumers on social media and group them based on the defined semantic types in the ontology. Diabetes-related discussions on Tumblr was used to test the efficiency of SpaCy and the Markov-Viterbi algorithm to map lay medical terms to the defined medical concepts in the UMLS. The system discussed in this paper can better analyze free texts, take care of word ambiguity and extract the lifestyle indicators from the daily life discussions of diabetic people on Tumblr. The findings of this study can contribute to developing health applications that track the health behavior of those living with chronic conditions such as diabetes. This approach can also assist researchers who are interested in processing lay languages used by health consumers to foster an understanding of their health behavior.

Constitutional Protection of Future Natural Resources under the Iraqi Constitution Of 2005

Most constitutions of the world's countries are keen to regulate the issue of protecting natural resources at the core of the constitutional document. Therefore, these constitutions are eager to provide constitutional protection for these resources, whether they exist or may exist in the future. Consequently, we discussed the constitutional protection of future natural resources under the Iraqi Constitution of 2005 in our research. This research is vital because the Iraqi Constitution stipulates the regulation of natural resources and focuses on the oil and gas resources, as they are the most prominent among these resources and are considered a significant source of revenues in the state's general budget. The study focuses on the fact that the process of sharing natural resources, especially future ones, was not clearly stated in the Constitution, as the texts it contained were marred by ambiguity and inconsistency due to ambiguous wording and the failure to legislate a law for these resources that clarifies the details of the constitutional texts. Hence, disputes arise between the federal government and the regions and governorates—not organized by region on future natural resources. The research was divided into two requirements according to a scientific plan that adopted the scientific sequence in studying the research. In the first requirement, we addressed the constitutional framework for the future natural resources in Iraq in two sections. The first is an explanation of the concept of natural resources. The second deals with the distinction between constitutional protection and criminal protection as an entry point with which we pave the way for entering into the study. The subject of the study. The second requirement deals with the federal government's authority over future natural resources, as per the provisions of the Iraqi Constitution of 2005, in two sections. The first section dealt with the constitutional organization of the relationship between the federal government and the region's governments and governorates regarding future natural resources. The second section was devoted to constitutional principles and their role in protecting future natural resources. Then, we ended with a conclusion that included the most important results and proposals drawn from the research.

Membangun Koneksi Individu: Strategi Berbahasa Indonesia Sebagai Alat Penting Komunikasi Interpersonal di Tempat Kerja

This study aims to examine the use of Indonesian as a tool to improve the effectiveness of interpersonal communication in the workplace, strengthen work relationships, and create a collaborative work environment. The method used is qualitative with data collection techniques, namely interviews. Data analysis is carried out qualitatively consisting of data reduction, data presentation, and conclusions. The results of the analysis show that good and appropriate use of Indonesian in the workplace is very important to maintain a level of professionalism, by paying attention to PUEBI guidelines and language structure, and avoiding the use of double, ambiguous words, or terminology unfamiliar to the interlocutor. The way a person speaks reflects their identity and culture. Interpersonal communication plays an important role in the work environment, affecting productivity and relationships between individuals. Improving interpersonal communication in the office environment involves a variety of strategies, from consistency in language adjustment to participating in activities outside of work hours and maintaining a positive attitude and openness to the opinions of others. Thus, effective interpersonal communication is not only the key to success in the workplace, but also supports the growth and development of both individuals and the organization

Comparison of various approaches to tagging for the inflectional Slovak language

Morphological tagging provides essential insights into grammar, structure, and the mutual relationships of words within the sentence. Tagging text in a highly inflectional language presents a challenging task due to word ambiguity. This research aims to compare six different automatic taggers for the inflectional Slovak language, seeking for the most accurate tagger for literary and non-literary texts. Our results indicate that it is useful to differentiate texts into literary and non-literary and subsequently, based on the text style to deploy a tagger. For literary texts, UDPipe2 outperformed others in seven out of nine examined tagset positions. Conversely, for non-literary texts, the RNNTagger exhibited the highest performance in eight out of nine examined tagset positions. The RNNTagger is recommended for both types of the text, the best captures the inflection of the Slovak language, but UDPipe2 demonstrates a higher accuracy for literary texts. Despite dataset size limitations, this study emphasizes the suitability of various taggers for the inflectional languages like Slovak.

Leveraging Bidirectionl LSTM with CRFs for Pashto Tagging

Part-of-speech tagging plays a vital role in text processing and natural language understanding. Very few attempts have been made in the past for tagging Pashto Part-of-Speech. In this work, we present a Long Short-term Memory–based approach for Pashto part-of-speech tagging with special focus on ambiguity resolution. Initially, we created a corpus of Pashto sentences having words with multiple meanings and their tags. We introduce a powerful sentences representation and new architecture for Pashto text processing. The accuracy of the proposed approach is compared with state-of-the-art Hidden Markov Model. Our Model shows 87.60% accuracy for all words excluding punctuation and 95.45% for ambiguous words; however, Hidden Markov Model shows 78.37% and 44.72% accuracy, respectively. Results show that our approach outperforms Hidden Markov Model in Part-of-Speech tagging for Pashto text.

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

As the global economy is developing, real time translation among major influential languages such as English, Chinese has become a necessity. Translation from English to Chinese is difficult since Chinese has a different grammar and ambiguous word boundaries. Furthermore, there are issues with the current Chinese-English machine translation, including difficult-to-understand extended sentences and inaccurate word translation. To address this drawback, the methodology for more precisely categorizing vocabulary in both English and Chinese is described in this research. The data are collected from UM-Corpus news dataset. Then, using SEGBOT: Neural Text Segmentation method, English words are segmented from the sentences available on the dataset. Afterward, the data are fed to pre-processing. In pre-processing segment; words missing in translation from the news dataset are eliminated and enhances the input data utilizing Iterated Square-Root Cubature Kalman Filter. The outcome from the pre-processing data is transferred to the Pyramidal Convolution Shuffle Attention Neural Network (PCSANN). The word order, grammatical structure, consistent style and smooth flow for English to Chinese translation are successfully classified by using PCSANN. The Waterwheel Plant Algorithm (WWPA) is used to optimize the weight parameter of PCSANN. The proposed PCSANN-WWPA is applied in python working platform. Performance metrics, like accuracy, precision, F1-score, and recall are examined to compute proposed method. The gained results of the proposed PCSANN-WWPA method attains higher accuracy of 16.65%, 18.85%, and 17.89%, higher sensitivity of 16.34%, 12.23%, and 18.54% and higher precision of 14.89%, 16.89%, and 18.23%. The proposed ECTT-PCSANN-WWPA method is compared with the existing methods such as ECTT-RNN, ECTT-DNN, and ECTT-MLPNN models respectively.

The Targetedness of English Schwa: Evidence from Schwa-Initial Minimal Pairs

Schwa in English shows a considerable amount of contextual variation, to the extent that previous work has proposed that it is acoustically targetless. Although the consensus of previous research seems to suggest that schwa is targeted, the sources of schwa’s contextual variation have yet to be fully explained. We explored a potential source of variation in English schwa, namely, whether schwa occurs in a content word (word-initial schwa, e.g., accompany) or is a function word (phrase-initial schwa, e.g., a company). We sought to determine whether English speakers distinguish word- and phrase-initial schwas in production, as well as whether word- and phrase-initial schwas differ in their level of targetedness. To elicit hyperarticulation of word- and phrase-initial schwas and thereby facilitate our ability to observe their targets, participants produced ambiguous and unambiguous word- and phrase-initial schwa pairs in neutral and biased sentence contexts. The first and second formant trajectories of the schwas were analyzed using growth curve analysis, allowing us to demonstrate that word-initial and phrase-initial schwas are both targeted and have different targets. Ultimately, our results suggest different underlying representations for schwas in function and content words.