Lexical Density Research Articles

Spontaneous speech: a robust measurement before, during and after awake brain surgery in patients with glioma

Background Patients with glioma often report language complaints with devastating effect on daily life. Analysing spontaneous speech can help to understand underlying language problems. Spontaneous speech monitoring is also of importance during awake brain surgery: it can guide tumour resection and contributes to maintaining language function. We aimed to investigate the spontaneous speech of patients with glioma in the perioperative period and the additional value of spontaneous speech analyses compared to standardised language testing. Methods We elicited and transcribed spontaneous speech of eight patients with glioma elected for awake brain surgery preoperatively, intraoperatively and 2.0–3.5 months postoperatively. Linguistic errors were coded. Type Token Ratio, Mean Length of Utterance of words, minimal utterances, and errors were extracted from the transcriptions. Patients were categorised based on total error patterns: stable, decrease or increase during surgery. Reliable Change Index scores were calculated for all spontaneous speech variables to objectify changes between time points. Language performance on language tests was compared to spontaneous speech variables. Results Most errors occurred in lexico-syntax, followed by phonology/articulation, syntax, and semantics. The predominant errors were Repetitions, Self-corrections, and Incomplete sentences. Most patients remained stable over time in almost all spontaneous speech variables, except in Incomplete sentences, which deteriorated in most patients postoperatively compared to intraoperatively. Some spontaneous speech variables (total errors, MLUw, TTR) gave more information on language change than a standard language test. Conclusions While the course of spontaneous speech over time remained relatively stable in most patients, Incomplete sentences seems to be a robust marker of language difficulties patients with glioma. These errors can be prioritised in spontaneous speech analysis to save time, especially to determine intra- to postoperative deterioration. Importantly, spontaneous speech analyses can give more information on language change than standardised language testing and should therefore be used in addition to standardised language tests.

Micro- and Macrostructural Language Features in Vertebrobasilar or Carotid System Stroke Without Diagnosis of Aphasia.

This study aimed to investigate the macro- and microstructural features of language in patients with ischemic stroke without aphasia. Participants were grouped according to arterial system damage and given the Aphasia Language Assessment Test (ADD) to detect aphasia. A narrative sample was obtained and analyzed for macrostructural and microstructural features of the language. The study sample consisted of 31 participants with ischemic stroke (15 vertebrobasilar system [VBS] involvement and 16 carotid system [CS] involvement) and 31 healthy participants, totaling 62 individuals. The healthy control group scored higher than the stroke group on the microstructural feature type-token ratio and mean length of utterance in the narrative analysis and on the auditory comprehension, repetition, naming, grammar, speech act, and writing subtests in ADD. Effort behavior, errors, edits, repetitions, and pauses among microstructural features and uncertainty, filler expression, and anomia among macrostructural features were significantly higher in the vertebrobasilar and CS groups than in the healthy control group. The total ADD score and speech fluency and reading subtest scores were significantly higher in individuals with VBS damage than in individuals with CS lesions (p < .05). Language components may be impaired differently in patients with carotid and vertebrobasilar lesions. Speech and language disorders in individuals who have experienced cerebrovascular accidents should be evaluated in the subacute and chronic phases, and the therapeutic needs of patients with ischemic stroke should be determined, regardless of the presence of a clinical aphasia diagnosis.

Sememe richness: a supplementary indicator of lexical richness in Chinese second-language writing

Abstract This article uses the vocabulary measurement indicators of lexical diversity, lexical sophistication, lexical density, and lexical accuracy for second-language writing levels to systematically measure the lexical richness of essays written by Chinese as a second language (CSL) learners. It employs the methodology of building a corpus based on the collected compositions for data analysis. It shows that the Uber index of lexical diversity and the lexical density of CSL learners does not always show a positive correlation with increased test scores. Lexical sophistication usually positively correlates with test scores, but the growth trend tends to be flat from the 81–90 score range. Lexical accuracy below 80 points is positively correlated with writing scores; however, the data above 91 points is slightly lower than that of 81–90 points. This article further hypothesizes that the above measurement indicators fail to accurately measure CSL students’ Chinese lexical ability because those indicators do not include the sememe factors of Chinese words and thus rely too much on word form. This article further puts forward the specific manifestations (polysemes, homographs, and multi-category words) of sememe richness and proposes specific solutions to add sememe richness to the four original formulas through the use of the four above-mentioned measurement indicators in CSL.

The The correlations between the average semester growth rates of syntactic and lexical complexity in L2 writing development at secondary school

The article is devoted to the development of language complexity in a relatively under-researched area of adolescent writing from the point of view of Complex Dynamic Systems Theory (CDST). It describes the second part of a panel study which aimed to examine the relationships among the average semester growth rates (ASGRs) of specific measures of syntactic and lexical complexity within and between these two subsystems in writing in English as a foreign language at the secondary school level. The study involved the analysis of The Written English Developmental Corpus of Polish Learners (WEDCPL). The corpus, which comprises over 1900 essays, was created on the basis of 21 repeated measurements conducted in a group of 100 secondary school learners during a period of three years. With respect to the specific measures of syntactic and lexical complexity, the study indicated that nominalization developed faster than subordination and coordination, and that lexical variation developed faster than lexical density and sophistication per semester. Moreover, the relationships between the ASGRs of both syntactic and lexical measures were more supportive within than between the subsystems. The main pedagogical implication for English language teachers refers to fostering more coordinated development of language complexity at secondary school in the EFL context.

Comparative Study of Chinese Translations of “Gitanjali” based on Natural Language Processing

This paper employs the HanLP natural language processing model to conduct a quantitative text analysis of four Chinese translations of “Gitanjali”. The analysis examines text features such as character and sentence count, lexical properties, lexical density, and high-frequency words. The results indicate that, except for Wu Yan’s translation, the text features of the other translations are relatively similar in terms of data distribution. Wu Yan’s translation has higher character and sentence count, and usage of lexical properties than the other translations, but it has the lowest lexical density, indicating a lower information density. The study also reveals that the choice of lexical properties and high-frequency words in each translation is influenced by the translator’s personal language preferences. Although the types of high-frequency words are relatively fixed, their frequencies vary significantly. Based on these findings, the study further uncovers the intrinsic logic and structural relationships among different text features, offering significant insights for the study of specific literary and translation texts, as well as broader translation and linguistic research. This paper demonstrates the effectiveness of HanLP in literary and translation research, highlighting the potential of natural language processing models in large-scale text analysis.

Determinants of Multilevel Discourse Outcomes in Anomia Treatment for Aphasia.

Individuals with aphasia identify discourse-level communication (i.e., language in use) as a high priority for treatment. The central premise of most aphasia treatments is that restoring language at the phoneme, word, and/or sentence level will generalize to discourse. However, treatment-related changes in discourse-level communication are modest, are poorly understood, and vary greatly among individuals with aphasia. In response, this study consisted of a multilevel discourse analysis of archival, monologic discourse outcomes across two high-intensity Semantic Feature Analysis (SFA) clinical trials. Aim 1 evaluated changes in theoretically motivated discourse outcomes representing lexical-semantic processing, lexical diversity, grammatical complexity, and discourse informativeness. Aim 2 explored the potential moderating role of nonlanguage cognitive factors (semantic memory, divided attention, and executive function) on discourse outcomes. This study was a retrospective analysis of archival monologic discourse outcomes after intensive SFA for n = 60 (Aim 1) and a subset n = 44 (Aim 2). Outcome measures included lexical-semantic processing (% semantic errors), lexical diversity (moving average type-token ratio), grammatical complexity (mean utterance length), and discourse informativeness (% correct information units). Bayesian generalized mixed-effects models were used to examine changes across four study time points: enrollment, entry, exit, and 1-month follow-up. The present study found no evidence for meaningful or statistically reliable improvements in monologue discourse performance after SFA when measured using standard, general-topic discourse stimuli. There was weak and inconsistent evidence that nonlanguage cognitive factors may play a role in moderating treatment response. These findings indicate a clear need to pair theoretically informed treatments designed to facilitate generalization to discourse with intentional measurement paradigms designed to capture it. Furthermore, there is a clear need to examine how established treatments, restorative or compensatory, can better facilitate generalization to discourse-level communication. These priorities are critical for meaningfully improving everyday communication and reducing the profound communication and psychosocial consequences of aphasia. https://doi.org/10.23641/asha.26524081.

In the Belly of Text Complexity: Unravelling the Nexus between Lexical Density and Readability

This study set out to explore the link between lexical density (LD) as a tool for determining text complexity and the general readability of texts (in this study, texts assigned to Senior High School learners in Ghana). To clearly establish this link, the levels of LD of assigned texts have to be first determined, and then, compared with their corresponding readability values. To that end, Ure’s (1971) LD formula and Halliday’s (1985b) LD formula have been used to determine the LD values of the sampled texts. Gunning’s (1952) and Flesch’s (1948) readability indices have been used to determine the readability values of same assigned texts. LD values for the sampled texts were found to be generally very high (above the widely agreed minimum of 40%); as their corresponding readability values were low. The correlation between LD and readability is therefore adequately revealed: high LD translates directly into low readability while low LD translates directly into high readability. Implicatively, low readability of texts definitely affects learner progress negatively. Keywords: lexical density, readability, Senior High School learners in Ghana, Ure’s (1971) LD formula, Halliday’s (1985) LD formula, Gunning’s (1952) readability index, Flesch’s (1948) readability index

Lexical Density of Reading Materials in An English Textbook: A Content Analysis

In reading, students are required to learn to comprehend the content of the text. Lexical density is one of the factors that influences students’ reading comprehension. Lexical density is a condition of words’ proportion in a text, which shows the ratio of content words (CW) and grammatical function words (GFW). This research used the “Passport to the World” English textbook for the 8th grade of junior high school to be analyzed. This study aims to find out the genres of reading materials offered by the curriculum 2013 found in the textbook and to examine the lexical density of the reading materials in the textbook. A qualitative approach was applied, while content analysis was used as the research design. Data were collected with document analysis. The findings showed that there are 3 genres of reading materials, among 5 genres offered by the curriculum 2013, included in the textbook such as descriptive, recount, and functional texts in the form of announcements. From those, it was found that two texts were categorized as low lexical density, 11 texts were categorized as medium lexical density, and four texts were labeled as high lexical density. In addition, the composition of this text did not reach the ideal ratio of 1:2:1 for index difficulty. However, this textbook can still be suggested to be used in the class with some usage modifications in the classroom.

The effects of lexical densities and task types on Chinese students’ incidental vocabulary acquisition

This study examines the effects arose by different lexical densities (2%, 5%) and task types (mind map, continuation task) on Chinese senior high students. Four classes of intermediate-level Chinese EFL learners were randomly assigned to four groups, where the treatment condition for each group includes two variables (group1: 2%+mind map, group2: 5%+mind map, group3: 5%+continuation task, group4: 2%+continuation task). It was found that (1) lexical density (2%, 5%) would not result in a significant difference on incidental vocabulary acquisition; (2) Both tasks would lead to a significant difference on incidental vocabulary acquisition and (3) mind map showed superiority in enhancing receptive vocabulary, productive vocabulary and word retention under the text of 5% of lexical density; (4) 2% of lexical density more facilitated output vocabulary when the continuation task was administered. Pedagogical implications of these results for EFL vocabulary acquisition are discussed.

Back to Basics in Measuring Lexical Diversity: Too Simple to Be True

Abstract Measuring lexical diversity in texts that have different lengths is problematic because length has a significant effect on the number of types a text contains, thus hampering any comparison. Treffers-Daller et al. (2018) recommended a simple solution, namely counting the number of types in a section of a given length that was extracted from the middle of each of the texts to be analysed. By applying this approach to second language essays, the authors observed that using the number of types was slightly more effective for differentiating amongst the Common European Framework of Reference for Languages (CEFR) levels than were the Type-Token Ratio (TTR) and the Guiraud index and that these three indices were more effective than were mathematically more complex ones. However, their conclusions regarding these two points are incorrect, and a less basic approach should be used. The last section addresses two broader issues in applied linguistics that are highlighted by these problems.

Stylometric Analysis of AI Chatbot-Generated Emails: Are Students Losing Their Linguistic Fingerprint?

The integration of artificial intelligence (AI) in education, particularly through AI chatbots like ChatGPT, Copilot, Gemini, and Bing, has revolutionized second language teaching and learning. These chatbots, which utilize advanced natural language processing and machine learning algorithms, hold great potential. They can operate complex cognitive tasks, provide immediate feedback, enhance motivation and self-confidence, reduce anxiety and improve language skills. Despite these advantages, concerns about academic integrity, loss of unique voice, and lack of emotional depth have emerged. This research aims at conducting a stylometric analysis on 25 AI chatbot-generated emails produced by undergraduate English language learners at Sultan Qaboos University. By examining stylistic features like tone, lexical density, lexical diversity, choice of words, repetition, formality level and emotional depth, the study will provide insights into the strengths and limitations of AI chatbots in enhancing email writing skills while preserving the unique voice of student writing. Findings reveal that AI-generated emails are characterized by a repetitive structure, a high level of formality and politeness, a high lexical diversity and a lack of emotional depth and personal anecdotes. The outcomes of this research will shed light on the awareness and skills students need to acquire to optimize the use of AI chatbots without losing their linguistic fingerprint.

Patterns of lexical and syntactic adjustment in early infant-directed speech related to language development in Hungarian

ABSTRACT Infant-directed speech (IDS) is known to be characterised by phonetic and prosodic cues along with reduced vocabulary and syntax compared to adult-directed speech (ADS). However, there is considerable variation between mothers in the degree of lexical and syntactic reduction of their IDS. The present study aims to investigate the correspondences of the inter-individual variation of maternal IDS at 6 and 18 months with infants’ language development at 18 months. 109 dyads of mothers and their firstborn infants participated in the study. Mothers’ ID and AD storytelling based on standard picture stimuli were recorded at 6 and 18 months of their infants’ age. We analysed measures of speech quantity (number of utterances and words), syntactic complexity (mean length of utterance), and lexical diversity (type-token ratio). Language growth was measured bimonthly using the Hungarian adaptation of the MacArthur-Bates CDI W&G form. The results did not reveal any association between characteristics of mothers’ ID narratives and their infants’ concurrent language skills at 18 months. However, we found a longitudinal link between a distinct pattern of linguistic simplification in maternal ID storytelling at 6 months and the development of expressive vocabulary in infants at 18 months. Infants whose mother tends to reduce both lexical and syntactic complexity of ID narratives the most are more likely to exhibit higher language outcomes. Further research is warranted to explore the background factors and longer-term effects of this maternal strategy.

Analyzing Lexical Density and Readability in IELTS Band 4 Students’ Writing Test

This study explores the lexical density and readability levels of writing produced by IELTS Band 4 students. The main objectives are to assess the lexical density and readability of their writing across ten writing tests. The study employs a descriptive qualitative research method, using data collected from 10 writing tests conducted by IELTS Band 4 students. Lexical density is determined using the "Analyze My Writing" website, while readability is assessed using the Flesch Reading Ease formula and scale. The findings reveal a wide range of lexical density, ranging from 46.98% to 63.93%, indicating varying levels of content richness and complexity in the students' writing. Similarly, readability scores vary significantly, with scores ranging from 34.21 to 66.32, reflecting differences in text difficulty and ease of comprehension. These findings underscore the diverse linguistic proficiency and accessibility of writing among IELTS Band 4 students.

A corpus-based study of live grammatical metaphor in English academic writing

ABSTRACT Nominalization is an important resource for creating grammatical metaphor (GM). Previous research shows that nominalization is characteristic of academic writing but does not demonstrate discipline sensitivity within the academic genre. The reason might be that some nominalizations have developed into dead GM. This study aims to identify nominalizations that create live GM and explore the discipline sensitivity of live GMs based on a corpus of academic article abstracts across three discipline groups. The findings indicate that a clause construes an event or a status, which is either conveyed by the verbal group or a nominal group functioning as a participant of the verbal group. Verb nominalizations construing events and adjective nominalizations construing statuses are the key factors in identifying live GM. Notably, nominalizations not cooccurring with verbalizations are not necessarily dead GMs. The corpus-based research shows that both verb and adjective nominalizations exhibit discipline sensitivity. Verb nominalizations are more prevalent in natural science texts, whereas adjective nominalizations are more prevalent in the humanities and social science texts. However, live GMs are not inherently discipline sensitive; rather, it is those GMs contributing to the lexical density of the text that are discipline sensitive.

Exploring the capabilities of ChatGPT in women’s health: obstetrics and gynaecology

Artificial Intelligence (AI) is transforming healthcare, with Large Language Models (LLMs) like ChatGPT offering novel capabilities. This study evaluates ChatGPT’s performance in interpreting and responding to the UK Royal College of Obstetricians and Gynaecologists MRCOG Part One and Two examinations – international benchmarks for assessing knowledge and clinical reasoning in Obstetrics and Gynaecology. We analysed ChatGPT’s domain-specific accuracy, the impact of linguistic complexity, and its self-assessment confidence. A dataset of 1824 MRCOG questions was curated, ensuring minimal prior exposure to ChatGPT. ChatGPT’s responses were compared to known correct answers, and linguistic complexity was assessed using token counts and Type-Token ratios. Confidence scores were assigned by ChatGPT and analysed for self-assessment accuracy. ChatGPT achieved 72.2% accuracy on Part One and 50.4% on Part Two, performing better on Single Best Answer (SBA) than Extended Matching (EMQ) Questions. The findings highlight the potential and significant limitations of ChatGPT in clinical decision-making in women’s health.

Retracted: The Relationship Between Oral and Written Language in Narrative Production by Arabic-Speaking Children: Fundamental Skills and Influences.

https://doi.org/10.1044/2024_Nov2024ASHA. This study aims to investigate the relationship between oral and written language skills in narrative production among Arabic-speaking children, focusing on cognitive and linguistic abilities. It examines the differences in narrative parameters between oral and written narratives and explores the associations between these parameters and cognitive and linguistic skills. The research involved 237 sixth-grade Arabic-speaking students from low-socioeconomic status schools in Israel. Each participant was instructed to orally tell a narrative and to write another narrative based on two sets of six sequential pictures. Various narrative features were analyzed, including word count for length, type-token ratio (TTR) for lexical diversity, mean length of utterance (MLU) for morphosyntax, and number of episodes for macrostructure. Cognitive linguistic measures, including Raven's Progressive Matrices, reading comprehension (RC), and morphological awareness (MA) were also assessed. The study found significant differences between oral and written narratives regarding lexical diversity and macrostructure. Participants exhibited significantly higher TTR in written narratives compared to oral narratives, whereas the number of episodes was significantly higher in oral narratives than in written ones. However, no significant differences were observed in narrative length or MLU. Moreover, the study identified significant predictors for various aspects of written narratives, particularly MA and RC, which significantly predicted TTR, MLU, and macrostructure. Additionally, the inclusion of word count in oral narratives significantly enhanced the explained variance for narrative length and macrostructure in written language. The results highlight the importance of the oral-written interface in both micro- and macrostructure representations in both oral and written modalities. They suggest that cognitive and linguistic skills, such as MA and RC, play a crucial role in narrative production. The findings have implications for educational practices and literacy outcomes in the Arab world, enhancing the understanding of the challenges and strategies involved in written language production among Arabic-speaking children.

Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.

Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Metacognitive Instruction for Improving the Effectiveness of Collaborative Writing for EFL Learners’ Writing Development

AbstractThere has been substantial research on the effects of metacognitive instruction on improving L2 learners’ language skills. However, little is known about the impact of such instruction on improving the effectiveness of L2 students’ collaborative writing. To fill this research gap, we conducted a quasi-experimental study to investigate how such an instructional programme would help Chinese EFL learners’ writing development through collaborative writing. A total of 62 post-graduate students from a university in central China were randomly allocated to either an experimental group receiving three weeks of collaborative writing interventions with metacognitive instruction for writing collaboratively, or a control group that was taught via a genre-based approach. Pre-test, immediate post-test, and delayed post-tests were administered with both groups at the onset, immediately after, and six weeks after the interventions to compare their change in writing production, specifically in lexical complexity, accuracy, and fluency. Results revealed that the metacognitive instruction and collaborative writing interventions helped improve participants’ writing performance in lexical variation, accuracy, and fluency. However, no significant effects were observed in lexical density and lexical sophistication. We discuss the results for their pedagogical and theoretical implications.

A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’ EFL writing

In the existing literature, scholars have proposed various indices to measure the lexical richness (LR) of English as a foreign language (EFL) writing. However, there are currently issues of redundant indices and inconsistent usage. Attempting to address the research question of which indices are the most sensitive and effective ones to distinguish between different grade levels of Chinese university students’ EFL writing, this study aims to put forward a refined and concise model of indices that can truthfully reflect LR in EFL writing. A total of 180 compositions were selected from a Chinese EFL learner corpus: &lt;i&gt;Spoken and written English corpus of Chinese learners&lt;/i&gt;. Scores of 28 LR indices of these compositions were computed using the software &lt;i&gt;Lexical Complexity Analyzer&lt;/i&gt;,&lt;i&gt; MATTR&lt;/i&gt;,&lt;i&gt; &lt;/i&gt;and&lt;i&gt; Coh-Metrix&lt;/i&gt;. One-way ANOVA or Welch’s ANOVA, depending on the variable’s homogeneity of variances, was conducted for each index. Two criteria were applied to determine which index of a measure should be included in the refined model: whether the difference of an index is significant among different grade levels and the effect size of ANOVA. Based on the quantitative results of ANOVAs and qualitative human judgment based on literature, six indices of the six LR measures were included in the refined model: lexical density, lexical sophistication-I, verb sophistication-II, number of different words-expected sequence 50, corrected TTR, and squared verb variation-I. This refined model addresses the issues of redundancy and inconsistency in previous studies, providing a more accurate and efficient tool for assessing LR in EFL writing.

Analysis of Lexical Density and Readability of Pakistani English Newspaper Dawn

This research study aimed to analyze the lexical density and readability of Pakistani English Newspaper and so, to suggest their suitability for the level of students. Eight newspapers from four genres were selected as sample. The theory of Systemic Functional Linguistics based on the theory of lexical density by Ure (1971) was applied for finding lexical density and readability indices by using content analysis design through an online text analyzer software. Readability values were compared with the FleschmReading Ease Scale. The results of this study showed that lexical density values of all genres were greater than 40% according to the Flesch Reading Ease Scale. All the texts were lexically dense having a greater number of contents carrying lexical items as compared to grammatical items. The findings of study could also be applied to academic textbooks in order to check complexity and suitability according to the level of the students.