Parts Of Speech Categories Research Articles

MOST FREQUENT WORDS USED IN PRIDE AND PREJUDICE’S NOVEL AND MOVIE

Abstract
 Altering a novel into a screenplay has been recorded as the interesting process in the field of literary works. There will be a number of adjustments that become consideration of the director particularly dialogues. This research aims to analyze the lexical differences used by Pride and Prejudice story in both novel and movie’s version. Also, the researchers strive to interpret the results based on the highlight of each text. The novel and movie script are converted into .txt format, and using AntConc device to sort the 50 highest words used by those sources. Then, the word list is categorized based on the part of speech by Mulyana (2013). The findings reveal that in the fifty word list, most of the lexical results include in the part of speech categories except Adjective and Exclamation. Thus, the first rank in the word list is positioned by the article (the) with the number of 918 times used in the movie and 4330 times used in the novel. The dominant issue can be portrayed through observing the noun and pronoun. They chiefly indicate the gender issue experienced by the main character of the story.

High Frequency Words Produced by Typically Developing Mandarin-Speaking Children Between 3 and 6 Years of Age

The purpose of this study was to provide high frequency word lists for Mandarin-speaking children between 3 and 6 years of age and to explore the differences between each part of speech (POS) category among different age groups. Participants were 209 typically developing native Mandarin speakers aged between 3 and 6 years, born in Taiwan, and recruited from Mandarin-language preschools in Taipei, New Taipei City, and Miaoli. Language samples were collected through conversations, free play, and story retelling. The researchers then transcribed the samples, segment utterances, and words, tagging the POS corresponding to each word. The frequencies of word occurrences were then analyzed and ranked to generate a high frequency word list. The mean frequency of each POS category was calculated to identify significant differences between age groups. The results showed high frequency word lists, including the corresponding POS tagging. Significant differences were found in 10 of the 11 POS categories among age groups. The results of this study presented preliminary information concerning high frequency words produced by Mandarin-speaking children aged between 3 and 6 years and the development of their use of each POS category.

Automated analysis of lexical features in frontotemporal degeneration

We implemented an automated analysis of lexical aspects of semi-structured speech produced by healthy elderly controls (n = 37) and three patient groups with frontotemporal degeneration (FTD): behavioral variant FTD (n = 74), semantic variant primary progressive aphasia (svPPA, n = 42), and nonfluent/agrammatic PPA (naPPA, n = 22). Based on previous findings, we hypothesized that the three patient groups and controls would differ in the counts of part-of-speech (POS) categories and several lexical measures. With a natural language processing program, we automatically tagged POS categories of all words produced during a picture description task. We further counted the number of wh-words, and we rated nouns for abstractness, ambiguity, frequency, familiarity, and age of acquisition. We also computed the cross-entropy estimation, where low cross-entropy indicates high predictability, and lexical diversity for each description. We validated a subset of the POS data that were automatically tagged with the Google Universal POS scheme using gold-standard POS data tagged by a linguist, and we found that the POS categories from our automated methods were more than 90% accurate. For svPPA patients, we found fewer unique nouns than in naPPA and more pronouns and wh-words than in the other groups. We also found high abstractness, ambiguity, frequency, and familiarity for nouns and the lowest cross-entropy estimation among all groups. These measures were associated with cortical thinning in the left temporal lobe. In naPPA patients, we found increased speech errors and partial words compared to controls, and these impairments were associated with cortical thinning in the left middle frontal gyrus. bvFTD patients' adjective production was decreased compared to controls and was correlated with their apathy scores. Their adjective production was associated with cortical thinning in the dorsolateral frontal and orbitofrontal gyri. Our results demonstrate distinct language profiles in subgroups of FTD patients and validate our automated method of analyzing FTD patients' speech.

Interactive POS-aware network for aspect-level sentiment classification

Existing aspect-level sentiment-classification models completely rely on the learning from given datasets. However, these are easily misled by biased samples, resulting in learning some ill-suited rules that limit their potential. The information of some specific part-of-speech (POS) categories often indicates the word sentiment polarity, which can be introduced as prior knowledge to facilitate prediction of the model. Accordingly, we propose an interactive POS-aware network (IPAN) that explicitly introduces the POS information as reliable guidance to assist the model in accurately predicting sentiment polarity. We distinguish the information of different POS categories using a POS-filter gate and reinforce the features extracted from adjectives, adverbs, and verbs via a POS-highlighting attention mechanism. This enables the model to concentrate on the words that contain significant sentiment orientations and to obtain the most practical learning experience. To emphasize the target information, we construct a target-context gate that enables the interaction of the target information with contexts; consequently, the model considerably focuses on target-related sentiment features. The experiments on SemEval2014 and Twitter datasets verify that our IPAN consistently outperforms the current state-of-the-art methods.

How the Probabilistic Structure of Grammatical Context Shapes Speech.

Does systematic covariation in the usage patterns of forms shape the sublexical variance observed in conversational speech? We address this question in terms of a recently proposed discriminative theory of human communication that argues that the distribution of events in communicative contexts should maintain mutual predictability between language users, present evidence that the distributions of words in the empirical contexts in which they are learned and used are geometric, and thus support this. Here, we extend this analysis to a corpus of conversational English, showing that the distribution of grammatical regularities and the sub-distributions of tokens discriminated by them are also geometric. Further analyses reveal a range of structural differences in the distribution of types in parts of speech categories that further support the suggestion that linguistic distributions (and codes) are subcategorized by context at multiple levels of abstraction. Finally, a series of analyses of the variation in spoken language reveals that quantifiable differences in the structure of lexical subcategories appears in turn to systematically shape sublexical variation in speech signal.

Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory

Abstract Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity features, such as standard part-of-speech (POS) tags such as adjectives, adverbs, verbs, and nouns. However, there seems to be a lack of research focusing on the subcategories of these tags. The aim of this research was to propose a method that better recognizes the polarity of natural language text by utilizing different polarity features using the standard POS category and the subcategory combinations in order to explore the specific polarity of text. Several experiments were conducted to examine and compare the efficacies of the proposed method in terms of F-measure, recall, and precision using an Amazon dataset. The results showed that JJ + NN + VB + RB + VBP + RP, which is a POS subcategory combination, obtained better accuracy compared to the baseline approaches by 4.4% in terms of F-measure.

Exploiting Parts-of-Speech for effective automated requirements traceability

ContextRequirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. ObjectiveFirst, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. MethodWe developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. ResultsThe results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. ConclusionWe showed that ConPos outperforms existing IR-based RT approaches that discard some POS tags from the input documents.

Semantic, Morphological, and Functional aspects of words and part of speech category in Chinese

Semantic, Morphological, and Functional aspects of words and part of speech category in Chinese

품사 분포 분석을 활용한 동일 원문에 기초한 번역문 간 문체 차이 연구

The paper suggests a novel method of analyzing parts-of-speech (POS) as a tool for investigating stylistic differences between translations of the same original literary work. The method consists of two stages: (1) analysis of differences in overall POS distribution and (2) in-depth investigation of POS categories with prominent differences. The method is illustrated by a case study involving two English translations of a Korean novel. Following Stage 1 analysis of differences in overall POS distribution, nouns were selected as a case for in-depth investigation to explain the marked difference observed in that category between the translations. The analysis shows that the key source of the difference is in the degree to which the two translations rely on nominalization, namely nouns derived from verbs and adjectives.

A Part-Of-Speech term weighting scheme for biomedical information retrieval

In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users’ search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature. We hypothesize that NLP can be incorporated into IR to strengthen the conventional IR models. In this study, we propose two NLP-empowered IR models, POS-BoW and POS-MRF, which incorporate automatic POS-based term weighting schemes into bag-of-word (BoW) and Markov Random Field (MRF) IR models, respectively. In the proposed models, the POS-based term weights are iteratively calculated by utilizing a cyclic coordinate method where golden section line search algorithm is applied along each coordinate to optimize the objective function defined by mean average precision (MAP). In the empirical experiments, we used the data sets from the Medical Records track in Text REtrieval Conference (TREC) 2011 and 2012 and the Genomics track in TREC 2004. The evaluation on TREC 2011 and 2012 Medical Records tracks shows that, for the POS-BoW models, the mean improvement rates for IR evaluation metrics, MAP, bpref, and P@10, are 10.88%, 4.54%, and 3.82%, compared to the BoW models; and for the POS-MRF models, these rates are 13.59%, 8.20%, and 8.78%, compared to the MRF models. Additionally, we experimentally verify that the proposed weighting approach is superior to the simple heuristic and frequency based weighting approaches, and validate our POS category selection. Using the optimal weights calculated in this experiment, we tested the proposed models on the TREC 2004 Genomics track and obtained average of 8.63% and 10.04% improvement rates for POS-BoW and POS-MRF, respectively. These significant improvements verify the effectiveness of leveraging POS tagging for biomedical IR tasks.

A Preflow Approach to Maximum Flows in Parametric Networks Applied to Assessing the Legal Information

The present paper proposes a fractal-like approach to the parametric flow problem, derived from the rules and recursion of generative linguistics. In the same manner in which any sentence can be analysed in terms of "theme" of the sentence (that which is spoken about in the sentence) and "rheme" of the sentence (that which is said about the theme in the sentence), in the proposed parametric preflow-push algorithm, a "partitioning push" (a non-cancelling push of flow in the parametric residual network) might leave the node unbalanced for only a subinterval of the range of parameter values. This will lead to separating the problem for the two disjoints subintervals, which allows the algorithm to continue after the same rules, independently, on each of the partitioned subintervals. The algorithm runs as the template-like structure of a dialogue act which reveals a design where information about the items (part-of-speech) is a two sections vector with one segment for each of the used part of speech categories. The article also proposes a possible application of the algorithm in assessing legal information and in semantic evaluation of legislation.

Slovak Morphosyntactic Tagset

Morphological annotation constitutes essential, very useful and very common linguistic information presented in corpora, especially for highly inflectional languages. The morphological tagset used in the Slovak National Corpus has been designed with several goals in mind – the tags are compact and easily human-readable, without sacrificing their informational contents. The tags consist of ASCII letters, numbers and several other characters. In general, they have a variable number of symbols, but their order is obligatory, and each category or specific feature is assigned a particular character, which can be shared among several parts of speech. The tagset is highly functional and pragmatic, although some allowances had to be made to accommodate traditional analysis of Slovak morphology and part of speech categories. In particular, function words are classified according to their syntactic (and semantic) roles, which is a reason why the tagset is sometimes described as a morphosyntactic one.

О соотношении «частей речи» и «падежей» в русском языке(On the Interrelations Between “Parts of Speech” and “Cases” in Russian)

The seemingly simple relations between these two categories turn out to be quite complex in the framework of a dependency grammar in which all kinds of segmental linguistic signs are involved: not only free, explicit words but also morphologically incorporated lexemes and implicit lex- emes. To the set of traditional cases are added CAS (general case), AGR (agreement case), PRP (prepositional phrase), CJP (conjunction phrase), and NOC ("no case", not marked for case). The part of speech category is enriched by the introduction of two subword labels, AFF (derivational affixes) and FLEX (desinencies). The possible combina- tions of the "part of speech" and "case" labels are investigated separately for each type of lexeme.

Part of speech mismatches in modular grammar

The Jingulu language of central-northern Australia presents some difficulties in terms of classifying certain of its lexemes into part of speech categories. Personal names, for instance, which should be nouns on notional grounds, have the phonological and morphosyntactic properties of interjections, whilst notionally verbal roots are distinctly non-verbal in their distribution. These phenomena are analysed according to the principles of autolexical syntax, wherein different levels of representation of the same linguistic item (morphem, word, phrase and so forth) need not necessarily correspond to one another exactly.

Influence of various system features of romance words on their survival*

Abstract It is found that the degree of survival of Latin words in five major modern Romance languages depends on their age, polysemy and part of speech category. Besides, the dependence of part of speech category, polysemy, frequency, word‐building structure on their age are considered in this paper.

Syntactic indices of language use of mentally retarded and normal children.

Three groups of 11-year old children institutionalized and non-institutionalized retardates and normals, were compared in their language usage on responses to TAT cards. The distribution of their words into common and uncommon, and over the part of speech categories was analysed, as well as the complexity and diversity of their sentences, verb and noun phrases. Retarded children did not differ from normal children in their usage of common and uncommon words, nor in their use of part of speech categories. Also, their verb phrases and noun phrases were in many respects comparable to those used by normal children. The clearest differences were found in the measures of sentence complexity and diversity: institutionalized retardates received the lowest scores of the three groups on these measures (significantly lower than the scores of the normal children), the scores of the non-institutionalized retardates were intermediate and those of the normal children the highest. Additional comparisons of the patterns of scores with those received by younger children showed that the institutionalized retardates were most comparable to normal 5-year olds, particularly in their sentence usage, whereas the non-institutionalized retardates were most comparable to normal children of the same chronological age.