arXiv subjects

Yi Luan

Publications and source records attributed to Yi Luan.

25 records · Page 2Linked to original sources

arXiv2018

Information Extraction from Scientific Literature for Method Recommendation

As a research community grows, more and more papers are published each year. As a result there is increasing demand for improved methods for finding relevant papers, automatically understanding the key ideas and recommending potential methods for a target problem. Despite advances in search engines, it is still hard to identify new technologies according to a researcher's need. Due to the large variety of domains and extremely limited annotated resources, there has been relatively little work on leveraging natural language processing in scientific recommendation. In this proposal, we aim at making scientific recommendations by extracting scientific terms from a large collection of scientific papers and organizing the terms into a knowledge graph. In preliminary work, we trained a scientific term extractor using a small amount of annotated data and obtained state-of-the-art performance by leveraging large amount of unannotated papers through applying multiple semi-supervised approaches. We propose to construct a knowledge graph in a way that can make minimal use of hand annotated data, using only the extracted terms, unsupervised relational signals such as co-occurrence, and structural external resources such as Wikipedia. Latent relations between scientific terms can be learned from the graph. Recommendations will be made through graph inference for both observed and unobserved relational pairs.

cs.IR↗

arXiv2018

Monolingual sentence matching for text simplification

This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network structure to model similarity between two sentences. Due to the limitation of available parallel corpora, the model is trained in a semi-supervised way, by using the output of a knowledge-based high performance aligning system. We apply the resulting similarity score to rescore the knowledge-based output, and adapt the model by a small hand-aligned dataset. Experiments show that both rescoring and adaptation improve the performance of knowledge-based method.

cs.CL↗

arXiv2018

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.

cs.CL↗

arXiv2018

Scientific Relation Extraction with Selectively Incorporated Concept Embeddings

This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. We extend the end-to-end relation extraction model of (Miwa and Bansal) with enhancements such as a character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).

cs.IR↗

arXiv2017

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.

cs.CL↗

arXiv2017

Scientific Information Extraction with Semi-supervised Neural Tagging

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.

cs.CL↗

arXiv2016

LSTM based Conversation Models

In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.

cs.CL↗