Kk Luke

Functional anatomy of syntactic and semantic processing in language comprehension

A functional magnetic resonance imaging (fMRI) study was conducted to map syntactic and semantic processes onto the brain. Chinese‐English bilingual subjects performed two experimental tasks: a syntactic plausibility judgment task in which they decided whether a viewed verb phrase was syntactically legal, and a semantic plausibility judgment task in which they decided whether a viewed phrase was semantically acceptable. A font size judgment task was used as baseline. It is found that a large‐scale distributed neural network covering the left mid‐inferior frontal and mid‐superior temporal cortices was responsible for the processing of Chinese phrases. The right homologue areas of these left cortical sites were also active, although the brain activity was obviously left‐lateralized. Unlike previous research with monolingual English speakers that showed that distinct brain regions mediate syntactic and semantic processing of English, the cortical sites contributing to syntactic analysis of Chinese phrases coincided with the cortical sites relevant to semantic analysis. Stronger brain activity, however, was seen in the left middle frontal cortex for syntactic processing (relative to semantic processing), whereas for semantic processing stronger cortical activations were shown in the left inferior prefrontal cortex and the left mid‐superior temporal gyri. The overall pattern of results indicates that syntactic processing is less independent in reading Chinese. This is attributable to the linguistic nature of the Chinese language that semantics and syntax are not always clearly demarcated. Equally interesting, we discovered that when our bilingual subjects performed syntactic and semantic acceptability judgments of English phrases, they applied the cerebral systems underlying Chinese reading to the processing of English. Hum. Brain Mapping 16:133–145, 2002.

Language affects patterns of brain activation associated with perceptual decision

Well over half a century ago, Benjamin Lee Whorf [Carroll JB (1956) Language, Thought, and Reality: Selected Writings of Benjamin Lee Whorf (MIT Press, Cambridge, MA)] proposed that language affects perception and thought and is used to segment nature, a hypothesis that has since been tested by linguistic and behavioral studies. Although clear Whorfian effects have been found, it has not yet been demonstrated that language influences brain activity associated with perception and/or immediate postperceptual processes (referred hereafter as “perceptual decision”). Here, by using functional magnetic resonance imaging, we show that brain regions mediating language processes participate in neural networks activated by perceptual decision. When subjects performed a perceptual discrimination task on easy-to-name and hard-to-name colored squares, largely overlapping cortical regions were identified, which included areas of the occipital cortex critical for color vision and regions in the bilateral frontal gyrus. Crucially, however, in comparison with hard-to-name colored squares, perceptual discrimination of easy-to-name colors evoked stronger activation in the left posterior superior temporal gyrus and inferior parietal lobule, two regions responsible for word-finding processes, as demonstrated by a localizer experiment that uses an explicit color patch naming task. This finding suggests that the language-processing areas of the brain are directly involved in visual perceptual decision, thus providing neuroimaging support for the Whorf hypothesis.

Chinese named entity recognition using lexicalized HMMs

This paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we first employ a known-word bigram-based model to segment a sentence into a sequence of known words, and then apply the uniformly lexicalized HMMs to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity. Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. We have tested our system using different public corpora. The results show that lexicalized HMMs can substantially improve NER performance over standard HMMs. The results also indicate that character-based tagging (viz. the tagging based on pure single-character words) is comparable to and can even outperform the relevant known-word based tagging when a lexicalization technique is applied.

Neural Correlates of Nouns and Verbs in Early Bilinguals

Previous neuroimaging research indicates that English verbs and nouns are represented in frontal and posterior brain regions, respectively. For Chinese monolinguals, however, nouns and verbs are found to be associated with a wide range of overlapping areas without significant differences in neural signatures. This different pattern of findings led us to ask the question of where nouns and verbs of two different languages are represented in various areas in the brain in Chinese–English bilinguals. In this study, we utilized functional magnetic resonance imaging (fMRI) and a lexical decision paradigm involving Chinese and English verbs and nouns to address this question. We found that while Chinese nouns and verbs involved activation of common brain areas, the processing of English verbs engaged many more regions than did the processing of English nouns. Specifically, compared to English nouns, English verb presentation was associated with stronger activation of the left putamen and cerebellum, which are responsible for motor function, suggesting the involvement of the motor system in the processing of English verbs. Our findings are consistent with the theory that neural circuits for linguistic dimensions are weighted and modulated by the characteristics of a language.

Chinese unknown word identification using class-based LM

This paper presents a modified class-based LM approach to Chinese unknown word identification. In this work, Chinese unknown word identification is viewed as a classification problem and the part-of-speech of each unknown word is defined as its class. Furthermore, three types of features, including contextual class feature, word juncture model and word formation patterns, are combined in a framework of class-based LM to perform correct unknown word identification on a sequence of known words. In addition to unknown word identification, the class-based LM approach also provides a solution for unknown word tagging. The results of our experiments show that most unknown words in Chinese texts can be resolved effectively by the proposed approach.

A Two-stage Statistical Word Segmentation System for Chinese

In this paper we present a two-stage statistical word segmentation system for Chinese based on word bigram and word-formation models. This system was evaluated on Peking University corpora at the First International Chinese Word Segmentation Bakeoff. We also give results and discussions on this evaluation.

Turns and Increments: A Comparative Perspective

Recent years have seen a surge of interest in “increments” among students of conversational interaction. This article first outlines “incrementing” as an analytical problem (i.e., as turn constructional unit [TCU] extensions) by tracing its origins back to Sacks, Schegloff, and Jeffersons (1974) famous turn-taking article. Then, the article summarizes and reviews Schegloffs recent publications and presentations, which revisited this problem, as well as contributions on the same theme by scholars using data from a variety of languages and settings. It is suggested that authors have generally focused their analytic attention on utterances that contain structural “oddities” (i.e., oddities relative to the “canonical” structures of particular languages), which could, and do, vary tremendously across languages. A general account of TCU extensions can only be built on the basis of more data from a larger variety of languages, and it must be typologically informed.

Chinese unknown word identification as known word tagging

This work presents a tagging approach to Chinese unknown word identification based on lexicalized hidden Markov models (LHMMs). In this work, Chinese unknown word identification is represented as a tagging task on a sequence of known words by introducing word-formation patterns and part-of-speech. Based on the lexicalized HMMs, a statistical tagger is further developed to assign each known word an appropriate tag that indicates its pattern in forming a word and the part-of-speech of the formed word. The experimental results on the Peking University corpus indicate that the use of lexicalization technique and the introduction of part-of-speech are helpful to unknown word identification. The experiment on the SIGHAN-PK open test data also shows that our system can achieve state-of-art performance.

Automatic expansion of abbreviations in chinese news text

This paper presents an n-gram based approach to Chinese abbreviation expansion. In this study, we distinguish reduced abbreviations from non-reduced abbreviations that are created by elimination or generalization. For a reduced abbreviation, a mapping table is compiled to map each short-word in it to a set of long-words, and a bigram based Viterbi algorithm is thus applied to decode an appropriate combination of long-words as its full-form. For a non-reduced abbreviation, a dictionary of non-reduced abbreviation/full-form pairs is used to generate its expansion candidates, and a disambiguation technique is further employed to select a proper expansion based on bigram word segmentation. The evaluation on an abbreviation-expanded corpus built from the PKU corpus showed that the proposed system achieved a recall of 82.9% and a precision of 85.5% on average for different types of abbreviations in Chinese news text.

Chinese text chunking using lexicalized HMMs

This paper presents a lexicalized HMM-based approach to Chinese text chunking. To tackle the problem of unknown words, we formalize Chinese text chunking as a tagging task on a sequence of known words. To do this, we employ the uniformly lexicalized HMMs and develop a lattice-based tagger to assign each known word a proper hybrid tag, which involves four types of information: word boundary, POS, chunk boundary and chunk type. In comparison with most previous approaches, our approach is able to integrate different features such as part-of-speech information, chunk-internal cues and contextual information for text chunking under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. Our preliminary experiments on the PolyU Shallow Treebank show that the use of lexicalization technique can substantially improve the performance of a HMM-based chunking system.