Natural Language Words Research Articles

Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.

Cancer-related Keywords in 2023: Insights from Text Mining of a Major Consumer Portal.

With the growing importance of monitoring cancer patients' internet usage, there is an increasing need for technology that expands access to relevant information through text mining. This study analyzed internet articles from portal sites in 2023 to identify trends in the information available to cancer patients and to derive meaningful insights. This study analyzed 19,578 news articles published on Naver, a major Korean portal site, from January 1, 2023, to December 31, 2023. Natural language processing, text mining, network analysis, and word cloud analysis were employed. The search term "am" (Korean for "cancer") was used to identify keywords related to cancer. In 2023, an average of 1,631 cancer-related articles were published monthly, with a peak of 1,946 in September and a low of 1,371 in February. A total of 132,456 keywords were extracted, with "cure" (2,218 occurrences), "lung cancer" (1,652), and "breast cancer" (1,235) being the most frequent. Term frequency-inverse document frequency analysis ranked "struggle" (1064.172) as the most significant keyword, followed by "lung cancer" (839.988) and "breast cancer" (744.840). Network analysis revealed four distinct clusters focusing on treatment, celebrity-related issues, major cancer types, and cancer-causing factors. The analysis of cancer-related keywords in 2023 indicates that news articles often prioritize gossip over essential information. These findings provide foundational data for future policy directions and strategies to address misinformation. This study underscores the importance of understanding the nature of cancer-related information consumed by the public and offers insights to guide official policies and healthcare practices.

Language experience shapes predictive coding of rhythmic sound sequences.

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language's word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker's linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term 'duration' prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.

Does executives' political identity improve firm financial performance? Evidence from China

Using data from Chinese A-share listed companies between 2010 and 2021, we employ natural language processing techniques and word frequency statistical analysis to investigate the influence of executives' subjective political values on firm financial performance. We draw on social identity theory and find that executives' political identity significantly enhances firm financial performance, with government subsidies and corporate compliance behavior playing intermediary roles. Furthermore, the promotional effect is more pronounced for enterprises that are geographically closer to the government, have lower marketization and are in the central and western regions. Our study contributes to the literature on the subjective political attitudes of Chinese executives and enhances the understanding of the economic consequences of such relationships. Additionally, our research offers valuable insights into corporate performance in countries without partisan conflict.

Visual experience modulates the sensitivity to the distributional history of words in natural language.

Different experiential traces (i.e., linguistic, motor, and perceptual) are likely contributing to the organization of human semantic knowledge. Here, we aimed to address this issue by investigating whether visual experience may affect the sensitivity to distributional priors from natural language. We conducted an independent reanalysis of data from Bottini et al., in which early blind and sighted participants performed an auditory lexical decision task. Since previous research has shown that semantic neighborhood density-the mean distance between a target word and its closest semantic neighbors-can influence performance in lexical decision tasks, we investigated whether vision may alter the reliance on this semantic index. We demonstrate that early blind participants are more sensitive to semantic neighborhood density than sighted participants, as indicated by the significantly faster response times for words with higher levels of semantic neighborhood density shown by the blind group. These findings suggest that an early lack of visual experience may lead to enhanced sensitivity to the distributional history of words in natural language, deepening in turn our understanding of the strict interplay between linguistic and perceptual experience in the organization of conceptual knowledge.

Experimental study on short-text clustering using transformer-based semantic similarity measure

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks.

Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study1

ABSTRACT The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. This longitudinal mixed methods study (N = 40 participants, n = 1440 narratives) with three measurements within a year showed that at the between-person level the use of negative-valenced words was strongly associated with the increase in anxiety and depression symptoms over time while the use of positive-valenced words was slightly associated with the decrease in anxiety and depression symptom. These effects were not supported for within-person level (i.e. changes in word usage). No significant differences were observed in the effects between depressed and non-depressed groups. Summing up, the overall use of positive- and negative-valenced words (particularly negative-valenced words) had a stronger effect on the severity of psychopathological symptoms than their change over time. The results were discussed in the context of natural language processing and its application in diagnosing depression and anxiety symptoms.

The poetry of psychological distance: Bidirectional associations between stimulus speed and its psychological distance and construal level.

Based on the cognitive-ecological approach and on logical-functional principles, in 12 studies (11 preregistered), we examine the novel hypotheses that psychological distance and construal level (CL) are associated in people's minds with stimulus speed: the psychologically distant/abstract is slow, and the psychologically close/concrete is fast. The findings support our expectations. Study Set I examined the association between psychological distance and speed. Findings show that psychological distance is implicitly and explicitly associated with speed (Study 1), that psychological distance is seen as compatible with slow and proximity with fast (Study 2), that stimulus psychological distance affects its perceived speed (Study 3), and that stimulus speed affects its psychological distance (Study 4). Study Set II examined the association between construal level and speed. Findings show that construal level is explicitly associated with speed (Study 5), that abstract is seen as compatible with slow and concrete with fast (Study 6), that natural language word distribution structures reflect an association between abstractness and speed (Study 7), that construal level affects speed (Study 8), and that speed affects stimulus construal level (Study 9). Study Set III examined implications for communication and person perception. Findings suggest that slow-paced (vs. fast-paced) speech is associated with larger perceived spatial and social distance between speaker and audience and larger audiences (Studies 10a, 10b) and that people infer an expansive (contractive) regulatory scope from slow-paced (fast-paced) spoken messages (Study 11). We elaborate on possible mechanisms and their theoretical and practical implications in domains including decision making and urban design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language

Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.

Термінологія морфологічного середовища для моделювання технічних систем

Unlike the words of the language of common goals, the appearance of the term goes through a more complicated path and is connected with a new phenomenon of reality, so it is not spontaneous. The term, unlike the words of natural language, always describes a single, strictly defined set of material objects or their interactions and relationships. Each term has a strict definition that conditions such unity, and to understand a term it is necessary to know both its own definition and the definitions of all the terms used in its definition, down to the basic, undefined, concepts. The appearance of the term, unlike the words of the language of common goals, goes through a more complicated path and is not spontaneous, as it is connected with a new phenomenon of reality, the nomination of which is one of the links of the process of cognition. The rapid formation of new disciplines entails the need for its own terminology.
 The article analyses term formation for the direction of modelling technical systems using the morphological environment. Various ways of forming terms are considered, the main of which are: formation by word-forming derivative - creation of new words from morphemes available in the language according to known (usually productive) models; by means of a semantic derivative, in other words, the formation on the basis of the similarity of a newly called phenomenon with a phenomenon already known in an already existing word of another meaning; by borrowing words from other languages. An example of creating a thesaurus is given. The ways of forming terms are considered: by way of word-forming derivative; by way of semantic derivative; due to the borrowing of words from other languages, the algorithm and peculiarities of the implementation of each method are analysed.
 The authors give the classification of terms and the scheme of the process of formation of terms, also the algorithm of using related terms between related disciplines is given, it is emphasized that the possibility of using terms between related disciplines is observed.
 An interdisciplinary approach is implemented where the methodology and theoretical foundations of different sciences, in particular disciplines far from each other, are jointly used to study the same phenomenon. An interdisciplinary approach creates the possibility of using terms between related disciplines. The process of such use is shown.

Quantitative methods for group bibliotherapy research: a pilot study

Background Bibliotherapy is under-theorized and under-tested: Its purposes and implementations vary widely, and the idea that ‘reading is good for you’ is often more assumed than demonstrated. One obstacle to developing robust empirical and theoretical foundations for bibliotherapy is the absence of analytical methods capable of providing sensitive yet replicable insights into complex textual material. This pilot study offers a proof-of-concept for new quantitative methods including VAD (valence–arousal–dominance) modelling of emotional variance and doc2vec modelling of linguistic similarity. Methods VAD and doc2vec modelling were used on conjunction with qualitative coding to analyse transcripts of reading-group discussions plus the literary texts being discussed, from two reading groups each meeting weekly for six weeks (including 9 participants [5 researchers (3 authors, 2 collaborators), 4 others] in Group 1, and 8 participants [2 authors, 6 others] in Group 2). Results In-text–discussion similarity was inversely correlated with emotional volatility in the group discussions (arousal: r = -0.25; p = ns; dominance: r = 0.21; p = ns; valence: r = -0.28; p = ns). Enjoyment or otherwise of the texts was less significant than other factors in shaping the significance and potential benefits of participation. (Texts with unpleasant or disturbing content that strongly shaped subsequent discussions of these texts were still able to sponsor ‘healthy’ discussions of this content.) Conclusions Our methods and findings offer for the field of bibliotherapy research both new possibilities for hypotheses to test, and viable ways of testing them. In particular, the use of natural language processing methods and word norm data offer valuable complements to intuitive human judgement and self-report when assessing the impact of literary materials. We also share observations on facilitation protocols, interpretative practices, and how our group reading model differs from other trials of group reading for wellbeing.

Grasping Virtual Objects Benefits Lower Aptitude Learners’ Acquisition of Foreign Language Vocabulary

Theories of embodied cognition describe language acquisition and representation as dependent on sensorimotor experiences that are collected during learning. Whereas native language words are typically acquired through sensorimotor experiences, foreign language (L2) words are often learned by reading, listening or repeating bilingual word lists. Recently, grasping referent objects has been demonstrated to serve as a useful sensorimotor strategy for L2 vocabulary learning. The effects of grasping virtual objects, however, remain unknown. In a virtual reality cave, we trained adult participants (N = 46) having low language aptitude and high language aptitude on novel L2 words under three conditions. In an audiovisual (baseline) condition, participants were presented with written and spoken L2 words. In an audiovisual observation condition, participants additionally saw virtual visual objects that corresponded to the meanings of L2 words. In an audiovisual, an observation, and a grasping condition, participants were asked to grasp the virtual objects. Participants’ word learning was assessed in free and cued recall tests administered immediately after training. Relative to baseline learning, simply viewing virtual objects during learning benefitted both groups. As expected, grasping virtual objects was found to benefit vocabulary retention in low language aptitude learners. Interestingly, this benefit was not observed in high language aptitude learners. Language learning aptitude scores correlated with vocabulary learning outcomes in both audiovisual learning conditions, but not in the sensorimotor condition, suggesting that grasping altered the typical relationship between aptitude and language learning performance. The findings are interpreted in terms of differences in the extent to which procedural and declarative memory systems are accessed in low language aptitude and high language aptitude learners during sensorimotor-based vocabulary learning. Additionally, the results suggest that simulated interactions without tangible feedback can benefit learning. This outcome expands our understanding of how physical experience extends cognition and paves the way for the application of sensorimotor enrichment strategies to virtual environments.

Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis.

Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible. In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs. We assessed the text's readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis. The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7%) and web pages (399/612, 65.2%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores. Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia.

Exploring the feasibility of a mental health application (JoyPopTM) for Indigenous youth.

The purpose of the current study was to explore the acceptability and feasibility of a resilience-focused mobile application, JoyPop™, for use with Indigenous youth. A Haudenosaunee community-based research advisory committee co-developed the research project, in accordance with OCAP™ principles. Adopting a mixed-method approach, five youths from an immersion school used the JoyPop™ app for four consecutive weeks, as well as completed pre-test questions and weekly usage surveys. Most participants also completed post-test questions and a semi-structured interview. Based on a semi-structured interview protocol, youth responded to questions, and the most common themes were categorized to capture the experience of using the app. All youth reported a positive impression, used the app daily, found it easy to navigate, and indicated that they would recommend it to a friend. All features were uniformly positively endorsed. There were features that youth used most often (Deep Breathing, "SquareMoves" game, and Art features) and moderately (Rate My Mood, Journaling, and SleepEase). The social connection feature, Circle of Trust, was least utilized, with youth reporting a preference for in-person problem-solving. The drop-down menu of crisis helplines was not used. Youth recommended more gaming options. In terms of cultural resonance, appreciation for the app's use of water sounds in the SleepEase feature was expressed, as was cultural consistency with the "Good Mind" perspective. Recommendations included additional nature sounds, Indigenous design elements, the inclusion of Native language words, and traditional stories. The JoyPop™ app was positively received by Six Nations youth, and ways to ensure its cultural appropriateness were identified. Moving forward, it is recommended that Indigenous designers create a new version with community design co-creation. Additional research with various groups of Indigenous youth is warranted as a pan-Indigenous approach is not recommended.

On regular copying languages

This paper proposes a formal model of regular languages enriched with unbounded copying. We augment finite-state machinery with the ability to recognize copied strings by adding an unbounded memory buffer with a restricted form of first-in-first-out storage. The newly introduced computational device, finite-state buffered machines (FS-BMs), characterizes the class of regular languages and languages de-rived from them through a primitive copying operation. We name this language class regular copying languages (RCLs). We prove a pumping lemma and examine the closure properties of this language class. As suggested by previous literature (Gazdar and Pullum 1985, p.278), regular copying languages should approach the correct characteriza-tion of natural language word sets.

Improving Indic code-mixed to monolingual translation using Mixed Script Augmentation, Generation &amp; Transfer Learning

The use of code-mixed languages (written in Roman character) on social media platforms is prevalent in multilingual nations. Translation from code-mixed to monolingual is necessary for social media analysis, content filtering, and targeted advertising. Training translation models from scratch is difficult due to the scarcity of available code-mixed resources and the extremely noisy nature of real-time code-mixed sentences. At the moment, multilingual state-of-the-art language models are routinely used for multilingual applications. However, multilingual models are ineffective in handling code-mixed sentences as it is usually written in Roman script but contain words from at least two languages. In the paper, two data augmentation techniques are proposed to improve code-mixed to monolingual translation, one based on script augmentation and the other on code-mixed sentence generation. The proposed approach converts the code-mixed sentences into ‘Mixed Script form’ that restore the native language words in the sentences with corresponding native language scripts. The novelty of the work is that the multilingual language models include each language’s linguistic competence, preserving context in the monolingual sentences, not possible in the earlier models. Using an mT5 model, denoising and mixed-script switching are performed, followed by monolingual translation with another mT5 model. Code-mixed sentences are generated by employing a simple code-mixed sentence generating technique using monolingual parallel inputs. Two different Indic language sets, namely Hindi-English and Bengali-English are applied and in each case, the proposed approach outperforms straight uni-script (Roman) code-mixed to monolingual translation.

Learning the language of QCD jets with transformers

Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics, which involves working with a high-dimensional space. We draw an analogy between sentences and words in natural language and jets and their constituents in high-energy physics. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets. Since transformers allow easy sampling from learned densities, we exploit their generative capability to assess the quality of the density estimate. Our results indicate that the generated data samples closely resemble the original data, as evidenced by the excellent agreement of distributions such as particle multiplicity or jet mass. Furthermore, the generated samples are difficult to distinguish from the original data, even by a powerful supervised classifier. Given their exceptional data processing capabilities, transformers could potentially be trained directly on the massive LHC data sets to learn the probability densities in high-energy jet physics.

Computational literature – creation under the auspices of AI and GPT models

In this article is presented the impact of GPT natural language processing models and the evolution of AI on the computational literature. Analyzing the structuring and functioning of these neural networks based on observation, learning (pre-training) and NLP mechanisms, we present the features of GPT models, the range of tasks they can perform, the advantages and risks of their implementation in literary creation. By processing enormous amounts of text to learn how the relationships between natural language words are structured, GPT models can generate both scientific and literary texts. An example of this is the work generated by GPT-3: 1 the Road by Ross Goodwin and Sunspring by Oscar Sharp and Ross Goodwin, presented in this study. The implementation of GPT models in the creative process is also manifested through a set of tools such as: Talk to Transformer, GPT-3 Creative Fiction, Copy.ai, AI Dungeon, etc. Researchers such as Thomas Hornigold, Mark Riedl and others warn that GPT models, although well-versed in various fields, cannot simulate human emotional intelligence, creativity and narrative intelligence, remaining creative tools but not perfect creators.

METHODOLOGY FOR CLASSIFYING MONEY LAUNDERING RISKS TYPOLOGIES

The method of classifying the typologies of money laundering and terrorist financing risks in big data from a variety of data sources of organizational systems is investigated. The main provisions of this technique are theoretically substantiated, reference models and calculation formulas are given. An algebraic approach for the synthesis of the method is proposed, a theoretical justification of the feasibility of its use is given. The algebraic approach to the synthesis of the methodology is implemented on the basis of category theory and the theory of algorithms, in particular the theory of Markov algorithms, which is considered outside of normal algorithms. The N-scheme of the Markov algorithm is considered, which was developed to replace its well-known g-scheme and provides processing of words in natural, artificial and combined languages, which can be represented in an ordered and extended Markov alphabet A±2, which takes into account many sources of big data. Methods for inputting incomplete, heterogeneous (heterogeneous) and non-representative input data samples are given. A method is proposed for converting input data samples into cycles of the Big Data Transfer Protocol. A reference model of the legal economic activity of the organizational system has been synthesized. This reference model is subjected to analysis and signs of illegal economic activity of mutually interacting organizational systems are highlighted. A reference model of the system for classifying the risks of laundering proceeds from crime and financing of terrorism has been developed. To combat money laundering, the N-scheme of the Markov algorithm for one data source and the Markov system, consisting of such schemes, for a variety of big data sources are theoretically substantiated. It is concluded that the synthesized Markov system provides a decomposition of words and their morphisms, contains a common data input control channel, and provides a classification of money laundering risks.

Semantic Representations during Language Comprehension Are Affected by Context.

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. fMRI was used to record human brain activity while four subjects (two female) read words in four conditions that vary in context: narratives, isolated sentences, blocks of semantically similar words, and isolated words. We then compared the signal-to-noise ratio (SNR) of evoked brain responses, and we used a voxelwise encoding modeling approach to compare the representation of semantic information across the four conditions. We find four consistent effects of varying context. First, stimuli with more context evoke brain responses with higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared with stimuli with little context. Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. In individual subjects, only natural language stimuli consistently evoke widespread representation of semantic information. Third, context affects voxel semantic tuning. Finally, models estimated using stimuli with little context do not generalize well to natural language. These results show that context has large effects on the quality of neuroimaging data and on the representation of meaning in the brain. Thus, neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.SIGNIFICANCE STATEMENT Context is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. Here, we examined whether the results of neuroimaging studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuro-imaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.