Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery

Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.Comment: EMNLP'1

On the classification and dispersability of circulant graphs with two jump lengths

In this paper, we give the classification of circulant graphs $C(\mathbb{Z}_{n},S)$ with $|S|=2$ and completely solve the dispersability of circulant graphs $C(\mathbb{Z}_{n},\{1, k\})$

Security enhancement using a novel two-slot cooperative NOMA scheme

In this letter, we propose a novel cooperative non-orthogonal multiple access (NOMA) scheme to guarantee the secure transmission of a specific user via two time slots. During the first time slot, the base station (BS) transmits the superimposed signal to the first user and the relay via NOMA. Meanwhile, the signal for the first user is also decoded at the second user from the superimposed signal due to its high transmit power. In the second time slot, the relay forwards the signal to the second user while the BS retransmits the signal for the first user as interference to disrupt the eavesdropping. Due to the fact that the second user has obtained the signal for the first user in the first slot, the interference can be eliminated at the second user. To measure the performance of the proposed cooperative NOMA scheme, the outage probability for the first user and the secrecy outage probability for the second user are analyzed. Simulation results are presented to show the effectiveness of the proposed scheme