Corpus Statistics Research Articles

Toward more careful corpus statistics: uncertainty estimates for frequencies, dispersions, association measures, and more

This article demonstrates that, counter to current practice, (i) corpus-linguistic studies should provide uncertainty/interval estimates for all corpus-linguistic statistics, even for basic/fundamental ones such as frequencies, dispersions, or association measures, and (ii) these statistics should be based on text-/file-based bootstrapping and confidence/data ellipses covering two or more dimensions of information. Four small case studies – three more programmatic and one more applied – are offered to exemplify the logic and method. The first case study shows how parametric confidence intervals or confidence intervals from word-based bootstrapping can be inappropriate; the second case study exemplifies the computation of frequency-cum-dispersion intervals; the third does the same for collocational/collostructional data (the ditransitive); and the last case study exemplifies the use of these methods in a diachronic statutory-interpretation context.

Capabilities And Distinctive Features Of The Coca Corpus

The article analyzes the compliance of COCA with functional corpus principles and its advantages. Attention was paid to the capability of COCA corpus, a second-generation representative of the modern English corpus, was selected is the object of study, to meet the above requirements. It also deals with the corpus results and statistics related to the language use as well as the conclusions made relying on them.

Choosing Linguistics over Vision to Describe Images

In this paper, we address the problem of automatically generating human-like descriptions for unseen images, given a collection of images and their corresponding human-generated descriptions. Previous attempts for this task mostly rely on visual clues and corpus statistics, but do not take much advantage of the semantic information inherent in the available image descriptions. Here, we present a generic method which benefits from all these three sources (i.e. visual clues, corpus statistics and available descriptions) simultaneously, and is capable of constructing novel descriptions. Our approach works on syntactically and linguistically motivated phrases extracted from the human descriptions. Experimental evaluations demonstrate that our formulation mostly generates lucid and semantically correct descriptions, and significantly outperforms the previous methods on automatic evaluation metrics. One of the significant advantages of our approach is that we can generate multiple interesting descriptions for an image. Unlike any previous work, we also test the applicability of our method on a large dataset containing complex images with rich descriptions.

Effects of Chord Inversion and Bass Patterns on Harmonic Expectancy in Musicians

This study tests the respective roles of pitch-class content and bass patterns within harmonic expectation using a mix of behavioral and computational experiments. In our first two experiments, participants heard a paradigmatic chord progression derived from music theory textbooks and were asked to rate how well different target endings completed that progression. The completion included the progression’s paradigmatic target, different inversions of that chord (i.e., different members of the harmony were heard in the lowest voice), and a “mismatched” target, a triad that shared its lowest pitch with the paradigmatic ending but altered other pitch-class content. Participants generally rated the paradigmatic target most highly, followed by other inversions of that chord, with lowest ratings generally elicited by the mismatched target. This suggests that listeners’ harmonic expectations are sensitive to both bass patterns and pitch-class content. However, these results did not hold in all cases. A final computational experiment was run to determine whether variations in behavioral responses could be explained by corpus statistics. To this end, n-gram chord-transition models and frequency measurements were compiled for each progression. Our findings suggest that listeners rate highly and have stronger expectations about chord progressions that occur frequently and behave consistently within tonal corpora.

Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)

The design of models that govern diseases in population is commonly built on information and data gathered from past outbreaks. However, epidemic outbreaks are never captured in statistical data alone but are communicated by narratives, supported by empirical observations. Outbreak reports discuss correlations between populations, locations and the disease to infer insights into causes, vectors and potential interventions. The problem with these narratives is usually the lack of consistent structure or strong conventions, which prohibit their formal analysis in larger corpora. Our interdisciplinary research investigates more than 100 reports from the third plague pandemic (1894-1952) evaluating ways of building a corpus to extract and structure this narrative information through text mining and manual annotation. In this paper we discuss the progress of our ongoing exploratory project, how we enhance optical character recognition (OCR) methods to improve text capture, our approach to structure the narratives and identify relevant entities in the reports. The structured corpus is made available via Solr enabling search and analysis across the whole collection for future research dedicated, for example, to the identification of concepts. We show preliminary visualisations of the characteristics of causation and differences with respect to gender as a result of syntactic-category-dependent corpus statistics. Our goal is to develop structured accounts of some of the most significant concepts that were used to understand the epidemiology of the third plague pandemic around the globe. The corpus enables researchers to analyse the reports collectively allowing for deep insights into the global epidemiological consideration of plague in the early twentieth century.

Matching phrase-frames to rhetorical moves in social science research article introductions

This study contributes to the emerging line of research on the linguistic realizations of rhetorical functions in disciplinary writing by presenting a list of phrase-frames identified from the Corpus of Social Science Research Article Introductions (COSSRAI) aligned with the rhetorical move-steps that they occurred in. COSSRAI contains the introduction sections of 600 research articles published in leading journals in six social science disciplines. The corpus was manually annotated for rhetorical move-steps using an extensively adapted version of Swales’ (2004) Create a Research Space model. A list of phrase-frames was extracted from the corpus using a combination of corpus statistics and manual filtering. The phrase-frames were then systematically mapped to the rhetorical move-steps they occurred in. Three types of phrase-frames emerged from our analysis: specialized phrase-frames that occurred in one move-step only, semi-specialized phrase-frames that occurred primarily in one move-step but also in others, and non-specialized phrase-frames that occurred in multiple move-steps with no clear association with any of them. We conclude with a discussion of the pedagogical implications of the list of phrase-frames aligned with rhetorical move-steps and the findings of our analysis.

Building a Parallel Corpus for English Translation Teaching Based on Computer-Aided Translation Software

This paper conducts an in-depth study on the construction of parallel corpus for English translation teaching through computer-aided translation software, and this study adopts a combination of corpus statistics and analysis to portray and study the system. In the corpus statistics, we carried out a detailed data analysis of the translated text and the original text with the help of corpus statistics software to find out the linguistic characteristics of the translated text and the areas that can be improved from the perspective of data statistics. After that, based on frequency, mean value, grading value, and multiplication analysis, we carry out qualitative analysis to study the movie subtitle translation strategy and its causes from multiple perspectives. This thesis aims to make research on film and television translation from a new angle with the help of corpus translation and to make up for the gap in the research direction of film and television translation. Firstly, through the corpus analysis methods such as frequency value, mean value, graded value and multiplier value, a quantitative analysis of film texts is carried out from three levels, namely, high frequency, medium frequency and basic, and it is found that Chinese films have many distinctive characteristics in terms of long words and sentences, words and sentences, beginning and end of sentences, idioms and the use of four-character words.

Unsupervised multi-granular Chinese word segmentation and term discovery via graph partition.

This study aims at realizing unsupervised term discovery in Chinese electronic health records (EHRs) by using the word segmentation technique. The existing supervised algorithms do not perform satisfactorily in the case of EHRs, as annotated medical data are scarce. We propose an unsupervised segmentation method (GTS) based on the graph partition principle, whose multi-granular segmentation capability can help realize efficient term discovery. A sentence is converted to an undirected graph, with the edge weights based on n-gram statistics, and ratio cut is used to split the sentence into words. The graph partition is solved efficiently via dynamic programming, and multi-granularity is realized by setting different partition numbers. A BERT-based discriminator is trained using generated samples to verify the correctness of the word boundaries. The words that are not recorded in existing dictionaries are retained as potential medical terms. We compared the GTS approach with mature segmentation systems for both word segmentation and term discovery. MD students manually segmented Chinese EHRs at fine and coarse granularity levels and reviewed the term discovery results. The proposed unsupervised method outperformed all the competing algorithms in the word segmentation task. In term discovery, GTS outperformed the best baseline by 17 percentage points (a 47% relative percentage of increment) on F1-score. In the absence of annotated training data, the graph partition technique can effectively use the corpus statistics and even expert knowledge to realize unsupervised word segmentation of EHRs. Multi-granular segmentation can be used to provide potential medical terms of various lengths with high accuracy.

Updating the dictionary: Semantic change identification based on change in bigrams over time

We investigate a method of updating a Danish monolingual dictionary with new semantic information on already included lemmas in a systematic way, based on the hypothesis that the variation in bigrams over time in a corpus might indicate changes in the meaning of one of the words. The method combines corpus statistics with manual annotations. The first step consists in measuring the collocational change in a homogeneous newswire corpus with texts from a 14 year time span, 2005 through 2018, by calculating all the statistically significant bigrams. These are then applied to a new version of the corpus that is split into one sub-corpus per year. We then collect all the bigrams that do not appear at all in the first three years, but appear at least 20 times in the following 11 years. The output, a dataset of 745 bigrams considered to be potentially new in Danish, are double annotated, and depending on the annotations and the inter-annotator agreement, either discarded or divided into groups of relevant data for further investigation. We then carry out a more thorough lexicographical study of the bigrams in order to determine the degree to which they support the identification of new senses and lead to revised sense inventories for at least one of the words Furthermore we study the relation between the revisions carried out, the annotation values and the degree of inter-annotator agreement. Finally, we compare the resulting updates of the dictionary with Cook et al. (2013), and discuss whether the method might lead to a more consistent way of revising and updating the dictionary in the future.

Performance Evaluation of English Part-of-Speech Tagging Based on Typical Parameter Smoothing Algorithm

Part-of-speech (POS) tagging for English is the basis for implementing English automatic correction. Although researchers have done a lot of useful studies on English POS tagging, most of them are aimed at users with English as the first language, while studies for users with English as the second language are few. For this purpose, manual tagging is performed based on the typical parameter smoothing algorithm. On this basis, a performance evaluation method for English part-of-speech tagging is proposed, which integrates the features of term clustering, non-tagged corpus statistics, word pronunciation, etc. The experimental results show that the algorithm can improve the performance of POS tagging effectively, with the tagging accuracy improved from 94.49% to 97.07%

A phraseological examination of research articles in the field of environment using key phrase frame

Given that formulaic sequences play an important role in enhancing L2 learners' academic writing skills and that phraseological sequences constitute a large proportion of written discourse, the current study was an attempt to draw up a list of key phrase frames– phraseological sequences with a variable slot– in a corpus of research articles (RAs) in the field of environment. A complete list of such frames, extracted by kfNgram software, was subjected to a series of corpus statistics such as frequency, range, and keyness, as well as manual filtering. This resulted in the generation of 65 four-word and 20 five-word key phrase frames specific to the field of environment. Structural analysis revealed that the vast majority of these key phrase frames were content word sequences, while functionally most of them belonged to referential expressions. Pedagogically speaking, L2 learners are recommended to develop a rich repertoire of formulaic sequences such as key phrase frames which are considered the cornerstone of academic discourse.

Linguistic analysis in politically correct discourse (on the English language media content)

The problem of the research is the insufficient study of political correctness (PC) in the intercultural and linguistic aspects, as well as the urgent need to establish its status in the modern integrated culture. The relevance of the study is due to there is no unified approach to the analysis of the concept of PC in the political sphere. There is a special ideological cultural and behavioral linguistic tolerant tendency in the contrary to expressions subjected to public ostracism due to the fact that the speeches of public figures contain too many politically incorrect statements. Thus, the “listener” may have an internal protest against the use of PC vocabulary in everyday life, although its use is forced upon society by all types of media. The study is touch upon the analysis of the vast corpus of statements by US President D. Trump on Twitter, as well as other open Internet sources. The problem of studying PC was dealt with as Russian scientists, such as A.B. Ostroukh, M. Yu. Palazhchenko, Yu.L. Gumanova, S.G. - Ter Minasova, L.V. Tsurikova and others, as well as their foreign colleagues: Paul Berman, Deborah Cameron and others. The aim of research is to attempt to describe the PC category in terms of cultural, behavioral and linguistic perspectives. In accordance with the tasks set for the study the following methods were used: descriptive method, methods of distributive, component, quantitative and comparative analysis. The results could be used in educational and methodological activities as well as preparation of materials on cultural linguistics, lexicology, linguistic stylistics submissions, etc. The further research course plans to establish the relationship between the occasional euphemistic vocabulary.

Linguistic features of a politically correct English language discourse

The problem of the research is the insufficient study of political correctness (PC) in the intercultural and linguistic aspects, as well as the urgent need to establish its status in the modern integrated culture. The relevance of the study is due to there is no unified approach to the analysis of the concept of PC in the political sphere. There is a special ideological cultural and behavioural linguistic tolerant tendency on the contrary to expressions subjected to public ostracism because the speeches of public figures contain too many politically incorrect statements. Thus, the “listener” may have an internal protest against the use of PC vocabulary in everyday life, although its use is forced upon society by all types of media. The study is touch upon the analysis of the vast corpus of statements by US President D. Trump on Twitter, as well as other open Internet sources. The problem of studying PC was dealt with as Russian scientists, such as A.B. Ostroukh, M. Yu. Palazhchenko, Yu.L. Gumanova, S.G. Ter Minasova, L.V. Tsurikova and others, as well as their foreign colleagues: Paul Berman, Deborah Cameron and others. The research aims to attempt to describe the PC category in terms of cultural, behavioural and linguistic perspectives. By the tasks set for the study, the following methods were used: descriptive method, methods of distributive, component, quantitative and comparative analysis. The results could be used in educational and methodological activities as well as preparation of materials on cultural linguistics, lexicology, linguistic stylistics submissions, etc. The further research course plans to establish the relationship between the occasional euphemistic vocabulary.

Verbo y contexto de uso: Un análisis basado en corpus con métodos cualitativos y cuantitativos

The semantic analysis of verbs is a theoretical and methodological challenge due to the syntactic and semantic complexity of these lexical units. The aim of this research is to identify the lexical-syntactic structures, that is, the patterns formed considering the syntactic structure, the arguments and the semantic types of the Spanish verbs. The analysis of Spanish verbs is carried out following the proposal of Corpus Pattern Analysis (Hanks, 2004a). This analysis is complemented with the automation of the procedure, combining a dependency analyser with a series of algorithms based on corpus statistics. As a result of the process, a database of lexical-syntactic patterns of 182 manually annotated verbs is offered, as well as an interface for automatic analysis which, according to the evaluation carried out, shows a 63.41% accuracy with respect to manual identification. This would be a contribution both to the theory of lexical semantics and to the description of the Spanish lexicon from a corpus-based methodology.

Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces

Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and invivo, in two downstream text classification tasks.

A novel unsupervised corpus-based stemming technique using lexicon and corpus statistics

Word Stemming is a widely used mechanism in the fields of Natural Language Processing, Information Retrieval, and Language Modeling. Language-independent stemmers discover classes of morphologically related words from the ambient corpus without using any language related rules. In this article, we proposed a fully unsupervised language-independent text stemming technique that clusters morphologically related words from the corpus of the language using both lexical and co-occurrence features such as lexical similarity, suffix knowledge, and co-occurrence similarity. The method applies to a wide range of inflectional languages as it identifies morphological variants formed through different linguistic processes such as affixation, compounding, conversion, etc.The proposed approach has been tested in Information Retrieval application for four languages (English, Marathi, Hungarian, and Bengali) using standard TREC, CLEF, and FIRE test collections. A significant improvement over word-based retrieval, five other corpus-based stemmers, and rule-based stemmers has been achieved in all the languages. Besides, information retrieval, the proposed approach has also been tested in text classification and inflection removal tasks. Our algorithm excelled over other baseline methods in all the test scenarios. Thus, we successfully achieved the objective of developing a multipurpose stemming algorithm that cannot only be used for information retrieval task but also for non-traditional tasks such as text classification, sentiment analysis, inflection removal, etc.

TED Multilingual Discourse Bank (TED-MDB): a parallel corpus annotated in the PDTB style

TED-Multilingual Discourse Bank, or TED-MDB, is a multilingual resource where TED-talks are annotated at the discourse level in 6 languages (English, Polish, German, Russian, European Portuguese, and Turkish) following the aims and principles of PDTB. We explain the corpus design criteria, which has three main features: the linguistic characteristics of the languages involved, the interactive nature of TED talks—which led us to annotate Hypophora, and the decision to avoid projection. We report our annotation consistency, and post-annotation alignment experiments, and provide a cross-lingual comparison based on corpus statistics.

Taming big data: Applying the experimental method to naturalistic data sets.

Psychological researchers have traditionally focused on lab-based experiments to test their theories and hypotheses. Although the lab provides excellent facilities for controlled testing, some questions are best explored by collecting information that is difficult to obtain in the lab. The vast amounts of data now available to researchers can be a valuable resource in this respect. By incorporating this new realm of data and translating it into traditional laboratory methods, we can expand the reach of the lab into the wilderness of human society. This study demonstrates how the troves of linguistic data generated by humans can be used to test theories about cognition and representation. It also suggests how similar interpretations can be made of other research in cognition. The first case tests a long-standing prediction of Gentner's natural partition hypothesis: that verb meaning is more subject to change due to the textual context in which it appears than is the meaning of nouns. Within a diachronic corpus, verbs and other relational words indeed showed more evidence of semantic change than did concrete nouns. In the second case, corpus statistics were employed to empirically support the existence of phonesthemes-nonmorphemic units of sound that are associated with aspects of meaning. A third study also supported this measure, by demonstrating that it corresponds with performance in a lab experiment. Neither of these questions can be adequately explored without the use of big data in the form of linguistic corpora.

Niveles de literacidad en tercer grado de una institución educativa de Bello

En el artículo se describe el proceso de la investigación realizada en la Institución Educativa Hernán Villa Baena (I.E.H.V.B.) en el municipio de Bello-Antioquia con el objetivo de analizar los niveles de literacidad de los niños y niñas de tercer grado de esta institución. La investigación realizada es de tipo mixta, dado el tratamiento descriptivo del corpus (cualitativo) y el análisis estadístico que lo acompañó (cuantitativo). Para dar cumplimiento al objetivo propuesto a los estudiantes se les aplicaron tres pruebas (diagnóstica, propuesta educativa llamada Con las TIC leo y comprendo y la prueba de salida); de igual manera, se contó con la participación de madres y docentes a quienes se les realizó una entrevista. Los resultados muestran que los niveles de literacidad de los estudiantes mejoraron a partir de la implementación de la propuesta pedagógica, además se hallaron diferencias entre las percepciones que tienen los docentes y las madres en relación al proceso de aprendizaje de lectura y escritura de los niños.

Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study.

Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their potential with a document-level corpus statistics study (914 texts) to estimate their overall capacity to differentiate between easy and difficult text and a classification task (11000 sentences) to determine usefulness of features at sentence-level for simplification. For the corpus statistics study we tested five document-level features for each chain type: total number of chains, average chain length, average chain span, number of crossing chains, and the number of chains longer than half the document length. We found significant differences between easy and difficult text for average chain length and the average number of cross chains. For the sentence classification study, we compared the lexical chain features to standard bag-of-words features on a range of classifiers: logistic regression, naïve Bayes, decision trees, linear and RBF kernel SVM, and random forest. The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ∼90% (compared to 78% for bag-of-words). Overall, we find several lexical chain features provide specific information useful for identifying difficult sentences of text, beyond what is available from standard lexical features.