“Tennis Problem” tries to link racquet, ball and net that are in a situational association relationship, and find the semantic and reasoning relationship among them. This is a worldwide problem in natural language processing in the construction of related language knowledge or resources. Aiming at solving the “tennis problem”, this paper reviews several mainstream systems of linguistic lexical and conceptual knowledge base (including WordNet, VerbNet, FrameNet, ConceptNet, etc.), illustrates their limitations on solving this problem, and explains on why they cannot solve it. This paper furcher proposes that the descriptive system of knowledge based on the theory of generative lexicon, i.e., the qualia structure of nouns, can be adopted, and that the qualia structure and relevant syntactic combinations can be used to build a noun-based or entity-based lexical network. This conceptual network may make up for the inadequacy of the above-mentioned systems of knowledge base and provide a knowledge base of lexical concepts for natural language processing.

This paper intends to construct the framework of a voluntary speech neural prosthesis based on phoneme imagery EEG signals to make brain-computer interface (BCI) speech production more natural and fluent. EEG signals are recorded in three healthy subjects while they are imagining both the vocalization and places of articulation of four vowels and four consonants in Mandarin Chinese as well as a no imagination state as control. To process the EEG data, this study performs spectral, temporal, and spatial analyses to extract the optimal phoneme imagery features for pairwise classification by SVM between every two tasks. The results reveale that the phoneme imagery effect is demonstrated in the frequency range of 2~10Hz, the time interval of 300~500ms after the stimuli onset, and the spatial patterns with strong activities mainly covering the sensorimotor cortical region. Besides, this study also find a high correlation of the pairwise classification accuracies with the Jaccard distances between the experimental stimuli based on the binary descriptions of articulation control. This experiment confirms the hypothesis that phoneme imagery can be characterized as a complex motor imagery task and that, with the highest classification accuracy between speech imagery and non-imagery tasks reaching up to 83% averaged across subjects, scalp-level speech motor imagery signals probably possess an unfulfilled potential to control a neural utterance synthesizer for communication BCIs.

Network structure has been wildely applied in language studies with the coming of the big data era. Since language is a multi-level system of symbols, different language units will exhibit networks of different structure and function. This paper surveys the construction methods for the word co-occurrence network (on the basis of the adjacency of words), the syntactic network (on the basis of syntactic theory-dependency grammar) and the semantic network (on the basis of conceptual relation) for the same text. It is revealed that the syntactic network's diameter and average path length are much smaller than those of the co-occurrence network, and the content words in the semantic network occupy central node locations. This suggests that the linguistic theory is to be applied in the network analysis, and will contribute to better explain the differences of various language networks.