Distinct Words Research Articles

Collection and evaluation of lexical complexity data for Russian language using crowdsourcing

Estimating word complexity with binary or continuous scores is a challenging task that has been studied for several domains and natural languages. Commonly this task is referred to as Complex Word Identification (CWI) or Lexical Complexity Prediction (LCP). Correct evaluation of word complexity can be an important step in many Lexical Simplification pipelines. Earlier works have usually presented methodologies of lexical complexity estimation with several restrictions: hand-crafted features correlated with word complexity, performed feature engineering to describe target words with features such as number of hypernyms, count of consonants, Named Entity tag, and evaluations with carefully selected target audiences. Modern works investigated the use of transforner-based models that afford extracting features from surrounding context as well. However, the majority of papers have been devoted to pipelines for the English language and few translated them to other languages such as German, French, and Spanish. In this paper we present a dataset of lexical complexity in context based on the Russian Synodal Bible collected using a crowdsourcing platform. We describe a methodology for collecting the data using a 5-point Likert scale for annotation, present descriptive statistics and compare results with analogous work for the English language. We evaluate a linear regression model as a baseline for predicting word complexity on handcrafted features, fastText and ELMo embeddings of target words. The result is a corpus consisting of 931 distinct words that used in 3,364 different contexts.

A Retrospective Analysis of Medical Student Performance Evaluations, 2014-2020: Recommend with Reservations.

The Medical Student Performance Evaluations (MSPE) is a cornerstone of residency applications. Little is known regarding adherence to Association of American Medical Colleges (AAMC) MSPE recommendations and longitudinal changes in MSPE content. Evaluate current MSPE quality and longitudinal changes in MSPE and grading practices. Retrospective analysis. Students from all Liaison Committee on Medical Education (LCME)-accredited medical schools from which the Stanford University Internal Medicine residency program received applications between 2014-2015 and 2019-2020. Inclusion of key words to describe applicant performance and metrics thereof, including distribution among students and key word assignment explanation; inclusion of clerkship grades, grade distributions, and grade composition; and evidence of grade inflation over time. MSPE comprehensiveness varied substantially among the 149 schools analyzed. In total, 25% of schools provided complete information consistent with AAMC recommendations regarding key word/categorization of medical students and clerkship grades in 2019-2020. Seventy-seven distinct key word terms appeared across the 139 schools examined in 2019-2020. Grading practices markedly varied, with 2-83% of students receiving the top internal medicine clerkship grade depending on the year and school. Individual schools frequently changed key word and grading practices, with 33% and 18% of schools starting and/or stopping use of key words and grades, respectively. Significant grade inflation occurred over the 6-year study period, with an average 14% relative increase in the proportion of students receiving top clerkship grades. A minority of schools complies with AAMC MSPE guidelines, and MSPEs are inconsistent across time and schools. These practices may impair evaluation of students within and between schools.

Skin disorders (twak rogas) revealed in the Atharvaveda: Descriptions of medicinal plants and utilization

In the Atharvaveda, two distinct words are mentioned namely, the krimi and kṛmi. The word kṛmi means insect (kīṭakamu) and whereas krimi refers to worm/germ. It mentions a large number of skin diseases, both of major and minor types, caused by the micro-organisms (krimi), which are of human concern. It describes the skin diseases, twak rogas, in the name of takman, yakṣma, kuṣṭa, kilasa, palita and śvitra, having grey and white spots, generally from deeper layers of skin and refers herbal remedies for them. A deep knowledge of the herbs and their various medicinal properties can be inferred from many of the Atharvaveda hymns for skin disorders. In this article, the human skin diseases revealed in the Atharvaveda, their classification, causative factors, liturgies and a number of herbal medical remedies with special reference to skin disorders are discussed.

An analysis of the Word Adjacency Network method—Part 2—A true understanding of the method

AbstractThis second of a two-part examination of the Word Adjacency Network (WAN) method considers the idea behind the method, which is that authors place function words in proximity to each other in such distinctive ways that the distinctions can be used for authorship attribution. By doing control experiments, it shows that the attributions made by the method are not due to distinctive word adjacencies at all, contrary to the claim of its inventors. It shows that the method is in fact another word-counting method, the counting hidden by superfluously complicated mathematics. It concludes that the claim that the placements of function words in a text can be used for attributing its authorship remains unsubstantiated. It also demonstrates that the claimed theoretical basis for the method, that WAN are Markov chains, is false.

Understanding the nature and severity of reading difficulties among students with language and reading comprehension difficulties.

This study investigated the presence of word reading difficulties in a sample of students in Grades 1-4 (n = 357) identified with language and reading comprehension difficulties. This study also examined whether distinct word reading and listening comprehension profiles emerged within this sample and the extent to which these groups varied in performance on cognitive and demographic variables. Findings showed that the majority of students (51%) with language and reading comprehension difficulties demonstrated significant risk in word reading (more than 1 SD below the mean), even though the participant screening procedures did not examine word reading directly. Three latent profiles emerged when students were classified into subgroups based on their performance in listening comprehension (LC) and word reading (WR): (1) severe difficulties in LC and moderate difficulties in WR (11%), (2) mild difficulties in both LC and WR (50%), and (3) moderate difficulties in LC and mild difficulties in WR (39%). Of note, even though students were identified for participation on the basis of poor oral language and reading comprehension abilities, all profiles demonstrated some degree of word reading difficulties. Findings revealed there were differences in age and performance on measures of working memory, nonverbal reasoning, and reading comprehension performance between profiles. Implications for educators providing instruction to students with or at risk for dyslexia and developmental language disorders were discussed.

Gluing and Cutting Cube Tiling Codes in Dimension Six

Let S be a set of arbitrary objects, and let $$s\mapsto s'$$ be a permutation of S such that $$s''=(s')'=s$$ and $$s'\ne s$$ . Let $$S^d=\{v_1\dots v_d:v_i\in S\}$$ . Two words $$v,w\in S^d$$ are dichotomous if $$v_i=w'_i$$ for some $$i\in [d]=\{1,\dots ,d\}$$ , and they form a twin pair if $$v_i'=w_i$$ and $$v_j=w_j$$ for every $$j\in [d]\setminus \{i\}$$ . A polybox code is a set $$V\subset S^d$$ in which every two distinct words are dichotomous. A polybox code V is a cube tiling code if $$|V|=2^d$$ . A 2-periodic cube tiling of $$\mathbb {R}^d$$ and a cube tiling of flat torus $$\mathbb {T}^d$$ can be encoded in a form of a cube tiling code. A twin pair v, w in which $$v_i=w_i'$$ is glue (at the ith position) if the pair v, w is replaced by one word u such that $$u_j=v_j=w_j$$ for every $$j\in [d]\setminus \{i\}$$ and $$u_i=*$$ , where $$*\notin S$$ is some extra fixed symbol. A word u with $$u_i=*$$ is cut (at the ith position) if u is replaced by a twin pair q, t such that $$q_i=t_i'\ne *$$ and $$u_j=q_j=t_j$$ for every $$j\in [d]\setminus \{i\}$$ . If $$V,W\subset S^d$$ are two cube tiling codes and there is a sequence of twin pairs which can be interchangeably gluing and cutting in a way which allows us to pass from V to W, then we say that W is obtained from V by gluing and cutting. In the paper it is shown that for every two cube tiling codes in dimension six one can be obtained from the other by gluing and cutting.

Drawing constitutional boundaries: A digital historical analysis of the writing process of Pinochet’s 1980 authoritarian constitution

Drawing conceptual boundaries is one of the defining features of constitution-making processes. These historically situated operations of boundary making are central to the definition of what counts as “constitutional” in a political community. In this article, we study the operations of conceptual delimitation performed by the Constitutional Commission (1973–1978) that drafted the 1980 Chilean Constitution, the trademark of Augusto Pinochet’s dictatorship. Using the eleven volumes of the Commission’s Official Records as our textual material (10,915 pages and 80,005 distinct words), we apply vector semantics, spectral clustering and bigram graph-based analysis to explore conceptual boundaries and the behavior of specific keywords shaping the space of constitutional meanings. Our results identify the ways in which the Commission defines the normative horizon of the new social and political order by transforming old semantic references into a renewed conceptual framework. This analysis shows the immanent relations between political action and conceptual elaboration that underlie the creation of constitutional texts, as well as the potential of computational methods for the study of constitutional history and constitution-making processes.

Different computational relations in language are captured by distinct brain systems.

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.

Gesture is the primary modality for language creation.

How language began is one of the oldest questions in science, but theories remain speculative due to a lack of direct evidence. Here, we report two experiments that generate empirical evidence to inform gesture-first and vocal-first theories of language origin; in each, we tested modern humans' ability to communicate a range of meanings (995 distinct words) using either gesture or non-linguistic vocalization. Experiment 1 is a cross-cultural study, with signal Producers sampled from Australia (n = 30, Mage = 32.63, s.d. = 12.42) and Vanuatu (n = 30, Mage = 32.40, s.d. = 11.76). Experiment 2 is a cross-experiential study in which Producers were either sighted (n = 10, Mage = 39.60, s.d. = 11.18) or severely vision-impaired (n = 10, Mage = 39.40, s.d. = 10.37). A group of undergraduate student Interpreters guessed the meaning of the signals created by the Producers (n = 140). Communication success was substantially higher in the gesture modality than the vocal modality (twice as high overall; 61.17% versus 29.04% success). This was true within cultures, across cultures and even for the signals produced by severely vision-impaired participants. The success of gesture is attributed in part to its greater universality (i.e. similarity in form across different Producers). Our results support the hypothesis that gesture is the primary modality for language creation.

Authorship Attribution via Occupancy-problem-type Indices

ABSTRACT In this paper, we propose a new methodology for authorship attribution based on a profile of indices related to the occupancy problem, called occupancy-problem indices. The occupancy problem has a long history and is an important example in standard textbooks like Feller (1971). We base our methodology on function words. We establish a testing procedure by constructing a confidence band of the occupancy-problem indices using the sampling distribution of the number of distinct function words. We validate our proposed methodology using controlled and constructed writing samples whose authorship is known. We then apply this methodology to explore the question of who wrote the 15th Oz book, which has a disputing authorship between Lyman Frank Baum (1856–1919) and his successor Ruth Plumly Thompson (1891–1976) on the Oz series.

A Study of Dissimilarities in the Pronunciation of Same Letters/Combinations of Same Letters Initiating with Alphabet ‘S’ in Distinct English Words

The present research article puts forththe dissimilarities in the pronunciation of same letters/combinations of same lettersinitiating with alphabet ‘S’ in distinct English words. The same letters/combinations of same letters initiating with alphabet ‘S’ in distinct English words, having dissimilarities in the pronunciation, are seventy-two in total which have been focussedwith four hundred and thirty-fivesamplesconsisting of words plus phonetic transcriptions whichwill stimulate the English language learnersto become supreme in the chosen fragment of presentation. This article consists of introduction, hypothesis, objectives, methodology, significance, limitations, ‘S’ alphabet and explanations with samples.The conclusion as well as references are the important parts of any researchand they have been incorporated in the final part of the article.

On primitive words with non-primitive product

Let𝒜be an alphabet of sizen≥ 2. Our goal in this paper is to give a complete description of primitive wordsp≠qover𝒜such thatpqis non-primitive. As an application, we will count the cardinality of the setℰ(l,𝒜) of all couples (p,q) of distinct primitive words such that |p| = |q| =landpqis non-primitive, wherelis a positive integer. Then we give a combinatorial formula for the cardinalityε(n,l) of this set. The density in {(p,q) :p,qare distinct primitive words and |p| = |q| =l} of the setℰ(l,𝒜) is also discussed.

Detecting geospatial location descriptions in natural language text

ABSTRACT References to geographic locations are common in text data sources including social media and web pages. They take different forms from simple place names to relative expressions that describe location through a spatial relationship to a reference object (e.g. the house beside the Waikato River). Often complex, multi-word phrases are employed (e.g. the road and railway cross at right angles; the road in line with the canal) where spatial relationships are communicated with various parts of speech including prepositions, verbs, adverbs and adjectives. We address the problem of automatically detecting relative geospatial location descriptions, which we define as those that include spatial relation terms referencing geographic objects, and distinguishing them from non-geographical descriptions of location (e.g. the book on the table). We experiment with several methods for automated classification of text expressions, using features for machine learning that include bag of words that detect distinctive words, word embeddings that encode meanings of words and manually identified language patterns that characterise geospatial expressions. Using three data sets created for this study, we find that ensemble and meta-classifier approaches, that variously combine predictions from several other classifiers with data features, provide the best F-measure of 0.90 for detecting geospatial expressions.

Part-of-Speech (POS) Tagging Using Deep Learning-Based Approaches on the Designed Khasi POS Corpus

Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a good performance of the tagger. Our main contribution in this research work is the designed Khasi POS corpus. Till date, there has been no form of any kind of Khasi corpus developed or formally developed. In the present designed Khasi POS corpus, each word is tagged manually using the designed tagset. Methods of deep learning have been used to experiment with our designed Khasi POS corpus. The POS tagger based on BiLSTM, combinations of BiLSTM with CRF, and character-based embedding with BiLSTM are presented. The main challenges of understanding and handling Natural Language toward Computational linguistics to encounter are anticipated. In the presently designed corpus, we have tried to solve the problems of ambiguities of words concerning their context usage, and also the orthography problems that arise in the designed POS corpus. The designed Khasi corpus size is around 96,100 tokens and consists of 6,616 distinct words. Initially, while running the first few sets of data of around 41,000 tokens in our experiment the taggers are found to yield considerably accurate results. When the Khasi corpus size has been increased to 96,100 tokens, we see an increase in accuracy rate and the analyses are more pertinent. As results, accuracy of 96.81% is achieved for the BiLSTM method, 96.98% for BiLSTM with CRF technique, and 95.86% for character-based with LSTM. Concerning substantial research from the NLP perspectives for Khasi, we also present some of the recently existing POS taggers and other NLP works on the Khasi language for comparative purposes.

The Generative Dissensus of Reading the Feminist Novel, 1995-2020: A Computational Analysis of Interpretive Communities

This article furthers ongoing work on the merits of the feminist novel’s intrinsic variability by probing its dynamics in four publishing contexts: contemporary anglophone literary criticism, prestigious review publications, marketing materials, and online book reviews by social readers. We explore how these interpretive communities converge and diverge in their assessments of feminist fiction over the past twenty-five years by evaluating articles from the MLA International Bibliography, book reviews in The New York Times, The New Yorker, Times Literary Supp-lement, and other prominent periodicals, blurbs from Amazon, and Goodreads reviews. We trace the feminist novel’s ambivalent fates—or rather, feminist novels’ ambivalent fates—in and across these four domains. To do so, we engage computational methods of topic modeling, most distinctive word analysis, and named entity recognition. We synthesize these quantitative results with qualitative attention to provocative examples from our corpus. In so doing, we consider how literary scholars can develop more robust understandings of what feminism and feminist fiction mean to contemporary readers and what we stand to gain by bringing this diverse interpretive labor into our scholarly conversations.

Dickinson’s Prosodic Music: Subtlety and Exuberance

This essay explores Dickinson’s prosodic music by evidencing its expressions of subtlety and exuberance. The essay unfolds in four steps. The first step finds the poet’s prosodic music in distinctive word arrangements with these three features: interlaced phonic echoes, the rhythms of short-lined verse where rhyme marks stanzas, and the motions of intonation. The second step instances Dickinson’s prosodic subtlety in one of her envelope poems, “A Pang is more conspicuous in Spring” (Fr1545B). The third step identifies Dickinson’s prosodic exuberance in two of her bee poems, “There is a flower that Bees prefer” (Fr642) and “I suppose the time will come” (Fr1389). In this step, we discern a hermeneutic key to Dickinson’s lyric art: when a sound in the world catches her ear, the poet’s prosodic music intensifies to reflect her enchantment. The essay’s last step applies the hermeneutic key to a superlative sound in Dickinson’s poetry, that of the wind in “Of all the Sounds despatched abroad” (Fr334).

Swearing on Twitter: Khaleeji Dialect

Swearing is an inevitable part of any language and culture around the world, yet each culture has its own distinctive swear words and phrases used among its members. This language phenomenon is observed on Twitter, one of the most popular social network platforms where heated comments often occur. In this study, the examination of the Khaleeji dialect of the Arabian Gulf countries on Twitter reflects the influence of culture, religion, and language on the perception and use of swearing in the Arabian Gulf. Upon corpus-based categorization and analysis of one hundred swear words and phrases written by Khaleejis on Twitter, the data shows that Khaleejis refer to ten different thematic swearing categories, with animals being their top reference. The findings accordingly answer the following questions: What swear words do Khaleejis often use? What is the logic behind opting for the swear words recorded?

A heterogeneous stacking ensemble based sentiment analysis framework using multiple word embeddings

AbstractWord embedding techniques have been proposed in the literature to analyze and determine the sentiments expressed in various textual documents such as social media posts, online product reviews, and so forth. However, it is difficult to capture the entire gamut of intricate inter‐dependencies among words in the textual documents using a specific word embedding technique. In this article, we aim to address this issue by proposing a computation‐efficient stacking ensemble based sentiment analysis framework using multiple word embeddings. The proposed framework uses a combination of three distinct word embeddings generated by three different state of the art word embedding techniques, namely, Word2Vec, GloVe, and BERT for performing the sentiment analysis task. It uses an explicitly trained Word2Vec model to generate the first set of 200‐dimensional word embedding. Similarly, pre‐trained GloVe and BERT models are used to generate the other two sets of 200‐dimensional and a 768‐dimensional word embeddings, respectively. These three distinct word embedding sets are then used to train a heterogeneous stacking ensemble based classifier model comprising LSTM, GRU, and Bi‐GRU based base‐level classifiers, and a LSTM based meta‐level classifier. Experimental results on four different datasets, namely, Sentiment140, IMDB Review, Twitter conversation thread, and Twitter Emotion show that the proposed framework achieves high performance with low false positive rate. The proposed framework is also shown to outperform other sentiment analysis frameworks proposed in the literature.

Source-side Reordering to Improve Machine Translation between Languages with Distinct Word Orders

English and Hindi have significantly different word orders. English follows the subject-verb-object (SVO) order, while Hindi primarily follows the subject-object-verb (SOV) order. This difference poses challenges to modeling this pair of languages for translation. In phrase-based translation systems, word reordering is governed by the language model, the phrase table, and reordering models. Reordering in such systems is generally achieved during decoding by transposing words within a defined window. These systems can handle local reorderings, and while some phrase-level reorderings are carried out during the formation of phrases, they are weak in learning long-distance reorderings. To overcome this weakness, researchers have used reordering as a step in pre-processing to render the reordered source sentence closer to the target language in terms of word order. Such approaches focus on using parts-of-speech (POS) tag sequences and reordering the syntax tree by using grammatical rules, or through head finalization. This study shows that mere head finalization is not sufficient for the reordering of sentences in the English-Hindi language pair. It describes various grammatical constructs and presents a comparative evaluation of reorderings with the original and the head-finalized representations. The impact of the reordering on the quality of translation is measured through the BLEU score in phrase-based statistical systems and neural machine translation systems. A significant gain in BLEU score was noted for reorderings in different grammatical constructs.

A Pseudo-Value Approach to Analyze the Semantic Similarity of the Speech of Children With and Without Autism Spectrum Disorder.

Conversational impairments are well known among people with autism spectrum disorder (ASD), but their measurement requires time-consuming manual annotation of language samples. Natural language processing (NLP) has shown promise in identifying semantic difficulties when compared to clinician-annotated reference transcripts. Our goal was to develop a novel measure of lexico-semantic similarity – based on recent work in natural language processing (NLP) and recent applications of pseudo-value analysis – which could be applied to transcripts of children’s conversational language, without recourse to some ground-truth reference document. We hypothesized that: (a) semantic coherence, as measured by this method, would discriminate between children with and without ASD and (b) more variability would be found in the group with ASD. We used data from 70 4- to 8-year-old males with ASD (N = 38) or typically developing (TD; N = 32) enrolled in a language study. Participants were administered a battery of standardized diagnostic tests, including the Autism Diagnostic Observation Schedule (ADOS). ADOS was recorded and transcribed, and we analyzed children’s language output during the conversation/interview ADOS tasks. Transcripts were converted to vectors via a word2vec model trained on the Google News Corpus. Pairwise similarity across all subjects and a sample grand mean were calculated. Using a leave-one-out algorithm, a pseudo-value, detailed below, representing each subject’s contribution to the grand mean was generated. Means of pseudo-values were compared between the two groups. Analyses were co-varied for nonverbal IQ, mean length of utterance, and number of distinct word roots (NDR). Statistically significant differences were observed in means of pseudo-values between TD and ASD groups (p = 0.007). TD subjects had higher pseudo-value scores suggesting that similarity scores of TD subjects were more similar to the overall group mean. Variance of pseudo-values was greater in the ASD group. Nonverbal IQ, mean length of utterance, or NDR did not account for between group differences. The findings suggest that our pseudo-value-based method can be effectively used to identify specific semantic difficulties that characterize children with ASD without requiring a reference transcript.