Hun Choi

Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., "eat the apple"). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb's modification of the DO noun's activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.

Communication through spoken language is a central human capacity, involving a wide range of complex computations that incrementally interpret each word into meaningful sentences. However, surprisingly little is known about the spatiotemporal properties of the complex neurobiological systems that support these dynamic predictive and integrative computations. Here, we focus on prediction, a core incremental processing operation guiding the interpretation of each upcoming word with respect to its preceding context. To investigate the neurobiological basis of how semantic constraints change and evolve as each word in a sentence accumulates over time, in a spoken sentence comprehension study, we analyzed the multivariate patterns of neural activity recorded by source-localized electro/magnetoencephalography (EMEG), using computational models capturing semantic constraints derived from the prior context on each upcoming word. Our results provide insights into predictive operations subserved by different regions within a bi-hemispheric system, which over time generate, refine, and evaluate constraints on each word as it is heard.

Spoken sentence comprehension is a rapid, incremental process which involves anticipating and integrating upcoming words into a developing representation. We use state-of-art computational models of verb subcategorization information and semantic selectional preferences to explore the dynamic neurocomputational processes involved in incremental integration of the semantic and syntactic properties of words in sentences. Our models measured prediction: information about syntactic and semantic properties of subsequent input, given the preceding context; and integration: the difficulty of integrating this subsequent input, given these predictions. We aimed to determine how quickly syntactic and semantic information is reflected in the dynamics of neural activity, and to distinguish whether information relevant to processing the subsequent input is activated early, or becomes active only when needed to facilitate integration of subsequent input. In an EMEG study, participants listened to 200 sentences which varied in complement structure following subject and verb (e.g. ['The student (subject NP) designed (verb) the experiment (complement)']). To model verb syntactic preferences, we used VALEX, a corpora-derived database providing syntactic frames for 6,397 English verbs (Korhonen et al., 2006). For the semantic preference model, we used the Latent Dirichlet Allocation (LDA) approach of topic-modelling (O’Seaghdha & Korhonen, 2014). This model combined topic distributions associated with direct object continuations given in a pre-test. We also modelled syntactic and semantic prediction error as the difference between the actual continuation and prior belief reflected in the syntactic and semantic prediction distributions. Representational similarity analysis (Kriegeskorte et al., 2008) related our computational models to the spatio-temporal dynamics of source-space signals in the language network. Consistent with claims that syntactic processing involves a left-lateralized fronto-temporal system (Tyler & Marslen-Wilson, 2008; Hagoort, 2013), verb subcategorisation information activated left fronto-temporal areas from 200ms after verb-onset. We found a significant subcategorisation prediction-error effect in L-BA45 150ms after the onset of the verb’s complement reflecting the difficulty of syntactic integration. Activation of the semantic preferences of verbs occurred remarkably early in bilateral inferior frontal areas –soon after verb-onset and before the verb’s complement structure had been determined. These early frontal effects may show how the subject NP constrains the verb such that the prediction of object nouns may begin before the verb is fully identified. Or the verb may be identified sooner given the context of the subject NP. Hence, this frontal activation may be related to the complexity of activating lexical-semantics by pre-activating a direct object frame. Finally, semantic prediction-error effects for the complement noun, reflecting integration difficulty for the noun, occurred in the left posterior middle and inferior temporal gyri around 300ms after complement noun onset. These results show that the left-syntactic and bilateral-semantic networks in the brain rapidly activate relevant syntactic and semantic information, flexibly pre-activating likely verb complements (i.e. direct object) and incrementally integrating the syntax and semantics of the complement for faster and more accurate comprehension.

Clustering is one of the well-known unsupervised learning methods that groups data into homogeneous clusters, and has been successfully used in various applications. Fuzzy C-Means(FCM) is one of the representative methods in fuzzy clustering. In FCM, however, cluster centers tend leaning to high density area because the sum of Euclidean distances in FCM forces high density clusters to make more contribution to clustering result. In this paper, proposed is an enhanced clustering method that modified the FCM objective function with additional terms, which reduce clustering errors due to density difference among clusters. Introduced are two terms, one of which keeps the cluster centers as far away as possible and the other makes cluster centers to be located in high density regions. The proposed method converges more to real centers than FCM, which can be verified with experimental results.

코로나 판데믹으로 인한 비대면 서비스로의 전환 가속화 현상으로 인해 헬스케어 산업에서의 인공지능과 빅데이터 처리 기술에 대한 실질 수요가 본격적으로 부상하기 시작했다. 본 논문에서는 독립형 소프트웨어에 의해 구동되는 의료기기 산업에서 인공지능 관련 기술 및 빅데이터 처리 기술의 활용 수준과 효과, 현 시점까지의 한계 요소들을 파악하기 위해 다수의 해외 사례와 국내 사례를 연구 분석하였다. 먼저 의료기기에 활용되고 있는 인공지능 기술들의 원리와 수준에 따라 유형을 분류하고 의료 분야에서의 적용 가능성을 제시하였다. 다음으로는 의료 빅데이터 분석 기법과 국내외 의료 빅데이터 이용 현황을 요약한 후 기술 융합 이슈를 점검하였다. 이어서 임상의사 결정지원 소프트웨어, 의료영상 진단 보조 소프트웨어 등 의료용 빅데이터를 활용하는 독립형 소프트웨어의 개념과 필요성, 활용 사례들을 분석, 제시하였다. 마지막으로 인공지능 기반 독립형 소프트웨어 의료기기의 국내외 개발 사례를 분석하고 향후 시장전망을 고찰하였다.