Yanhui Gu

TOAST: a topic-oriented tag-based recommender system

Social Annotation Systems have emerged as a popular application with the advance of Web 2.0 technologies. Tags generated by users using arbitrary words to express their own opinions and perceptions on various resources provide a new intermediate dimension between users and resources, which deemed to convey the user preference information. Using clustering for topic extraction and incorporating it with the capture of user preference and resource affiliation is becoming an effective practice in tag-based recommender systems. In this paper, we aim to address these challenges via a topic graph approach. We first propose a Topic Oriented Graph (TOG), which models the user preference and resource affiliation on various topics. Based on the graph, we devise a Topic-Oriented Tag-based Recommendation System (TOAST) by using the preference propagation on the graph. We conduct experiments on two real datasets to demonstrate that our approach outperforms other state-of-the-art algorithms.

Exploration on efficient similar sentences extraction

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them.

An enhanced short text categorization model with deep abundant representation

Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.

A graph-based approach for semantic similar word retrieval

Semantic relatedness or semantic similarity between words is an important basic issue for many Natural Language Processing (NLP) applications, such as sentence retrieval, word sense disambiguation, question answering, and so on. This research issue attracts many researchers, but most of studies focus on improving the effectiveness, i.e., applying kinds of techniques to improve precision (effectiveness) but not efficiency. To tackle the problem, we propose to address the efficiency issue, that how to efficiently find top-k most semantic similar words to the query for a given dataset. This issue is very important for real applications especially for current big data. Efficient graph-based approaches on searching top-k semantic similar words are proposed in this paper. The results demonstrate that the proposed model can perform significantly better than baseline method.

Performance Evaluation of Similar Sentences Extraction

Similar sentence extraction is an important issue because it is the basis of many applications. In this paper, we conduct comprehensive experiments on evaluating the performance of similar sentence extraction in a general framework. The effectiveness and the efficiency issues are explored on three real datasets, with different factors considered, i.e., size of data, top-k value. Moreover, the WordNet is taken into account as an additional semantic resource and incorporated into the framework. We thoroughly explore the performance of the updated framework to study the similar sentence extraction.

An Effective Joint Framework for Document Summarization

Document summarization is an important research issue and has attracted much attention from the academe. The approaches for document summarization can be classified as extractive and abstractive. In this work, we introduce an effective joint framework that integrates extractive and abstractive summarization models, which is much closer to the way human write summaries (first underlining important information). Preliminary experiments on real benchmark dataset demonstrate that our model is competitive with the state-of-the-art methods.

A Story Coherence based Neural Network Model for Predicting Story Ending.

Predicting the ending of a story is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). Although several studies have addressed this issue, the performance remains unsatisfactory due to ineffectiveness of story comprehension. In this paper, we propose to construct a story coherence based neural network model (SCNN) with well-designed optimizations. The preliminary evaluation demonstrates the effectiveness of our model which is superior to that of state-of-the-art approaches.

An effective approach for automatic interpretation of Chinese nominal compounds

Purpose Automatic interpretation of Nominal Compounds is a crucial issue for many applications, for example, sentence understanding, machine translation, question-answering system and so forth. Many automatic interpretation models of Nominal Compounds use the strategies based on verbs or rules to obtain the interpretation of compounds. However, the performances of these models are still limited. The purpose of this paper is to propose an effective approach for automatic interpretation of Chinese nominal compounds. Design/methodology/approach The authors propose a top-down and bottom-up model based on rules and large-scale corpus for automatic interpretation of Nominal Compounds. Findings Experimental results demonstrate that the proposed model outperforms the state-of-the-art automatic interpretation model. Originality/value The paper is an up-to-date study of automatic interpretation for Nominal Compounds. It can help people understand the meaning of Nominal Compounds in reading. With a better understanding of Nominal Compounds, we can discover more hidden knowledge in them.

Syntactic Categorization and Semantic Interpretation of Chinese Nominal Compounds

The development in society and technology generates more Nominal Compounds to represent new concepts in various domains. Earlier literature in linguistic studies has gathered and established several syntactic categories of Nominal Compounds, which can be used for automatic syntactic categorization of these compounds. This paper is focused on Nominal Compounds of head-modifier construction because experiments show that most Nominal Compounds are head-modifier constructions. Based on the combination of templates and word similarity, this paper proposes an algorithm for automatic semantic interpretation which improves the recall ratio while maintaining the precision ratio. The results of syntactic categorization and automatic semantic interpretation of the Nominal Compounds are also applied in dependency parsing and machine translation.

Research on interpretation of nominal compound

Nominal compounds which constituted of two nouns together are very common in reading materials or web pages. The interpretation of these compounds can help us know the meaning of a text or sentences. Traditional approaches utilized the method based on verbs and rules to obtain the interpretation of compounds with low recall. So we investigate an interpretation method based on similarity which makes use of the interpretation templates and similar words to achieve the automatic interpretation. Experimental results show that our method can interpret these nominal compounds with a relatively high precision (84.91%), give an increase of 10.48% in recall than the general method, which contributes to the overall nominal compound recall improvement significantly.