Lidong Bing

Abstractive Multi-Document Summarization via Phrase Selection and Merging

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.

Towards a unified solution: data record region detection and segmentation

Although the task of data record extraction from Web pages has been studied extensively, yet it fails to handle many pages due to their complexity in format or layout. In this paper, we propose a unified method to tackle this task by addressing several key issues in a uniform manner. A new search structure, named as Record Segmentation Tree (RST), is designed, and several efficient search pruning strategies on the RST structure are proposed to identify the records in a given Web page. Another characteristic of our method which is significantly different from previous works is that it can effectively handle complicated and challenging data record regions. It is achieved by generating subtree groups dynamically from the RST structure during the search process. Furthermore, instead of using string edit distance or tree edit distance, we propose a token-based edit distance which takes each DOM node as a basic unit in the cost calculation. Extensive experiments are conducted on four data sets, including flat, nested, and intertwine records. The experimental results demonstrate that our method achieves higher accuracy compared with three state-of-the-art methods.

Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning

We develop a new framework to achieve the goal of Wikipedia entity expansion and attribute extraction from the Web. Our framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities. One characteristic of our framework is to conduct discovery and extraction from desirable semi-structured data record sets which are automatically collected from the Web. A semi-supervised learning model with Conditional Random Fields is developed to deal with the issues of extraction learning and limited number of labeled examples derived from the seed entities. We make use of a proximate record graph to guide the semi-supervised learning process. The graph captures alignment similarity among data records. Then the semi-supervised learning process can leverage the unlabeled data in the record set by controlling the label regularization under the guidance of the proximate record graph. Extensive experiments on different domains have been conducted to demonstrate its superiority for discovering new entities and extracting attribute content.

Improving Distant Supervision for Information Extraction Using Label Propagation Through Lists

Because of polysemy, distant labeling for information extraction leads to noisy training data. We describe a procedure for reducing this noise by using label propagation on a graph in which the nodes are entity mentions, and mentions are coupled when they occur in coordinate list structures. We show that this labeling approach leads to good performance even when off-the-shelf classifiers are used on the distantly-labeled data.

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weighted-memory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.

Normalizing web product attributes and discovering domain ontology with minimal effort

We have developed a framework aiming at normalizing product attributes from Web pages collected from different Web sites without the need of labeled training examples. It can deal with pages composed of different layout format and content in an unsupervised manner. As a result, it can handle a variety of different domains with minimal effort. Our model is based on a generative probabilistic graphical model incorporated with Hidden Markov Models (HMM) considering both attribute names and attribute values to extract and normalize text fragments from Web pages in a unified manner. Dirichlet Process is employed to handle the unlimited number of attributes in a domain. An unsupervised inference method is proposed to predict the unobservable variables. We have also developed a method to automatically construct a domain ontology using the normalized product attributes which are the output of the inference on the graphical model. We have conducted extensive experiments and compared with existing works using prouct Web pages collected from real-world Web sites in three different domains to demonstrate the effectiveness of our framework.

Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context

An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data.

Unsupervised Extraction of Popular Product Attributes from E-Commerce Web Sites by Considering Customer Reviews

We develop an unsupervised learning framework for extracting popular product attributes from product description pages originated from different E-commerce Web sites. Unlike existing information extraction methods that do not consider the popularity of product attributes, our proposed framework is able to not only detect popular product features from a collection of customer reviews but also map these popular features to the related product attributes. One novelty of our framework is that it can bridge the vocabulary gap between the text in product description pages and the text in customer reviews. Technically, we develop a discriminative graphical model based on hidden Conditional Random Fields. As an unsupervised model, our framework can be easily applied to a variety of new domains and Web sites without the need of labeling training samples. Extensive experiments have been conducted to demonstrate the effectiveness and robustness of our framework.

Ontology enhancement and concept granularity learning: keeping yourself current and adaptive

As a well-known semantic repository, WordNet is widely used in many applications. However, due to costly edit and maintenance, WordNets capability of keeping up with the emergence of new concepts is poor compared with on-line encyclopedias such as Wikipedia. To keep WordNet current with folk wisdom, we propose a method to enhance WordNet automatically by merging Wikipedia entities into WordNet, and construct an enriched ontology, named as WorkiNet. WorkiNet keeps the desirable structure of WordNet. At the same time, it captures abundant information from Wikipedia. We also propose a learning approach which is able to generate a tailor-made semantic concept collection for a given document collection. The learning process takes the characteristics of the given document collection into consideration and the semantic concepts in the tailor-made collection can be used as new features for document representation. The experimental results show that the adaptively generated feature space can outperform a static one significantly in text mining tasks, and WorkiNet dominates WordNet most of the time due to its high coverage.

Adaptive Concept Resolution for document representation and its applications in text mining

It is well-known that synonymous and polysemous terms often bring in some noise when we calculate the similarity between documents. Existing ontology-based document representation methods are static so that the selected semantic concepts for representing a document have a fixed resolution. Therefore, they are not adaptable to the characteristics of document collection and the text mining problem in hand. We propose an Adaptive Concept Resolution (ACR) model to overcome this problem. ACR can learn a concept border from an ontology taking into the consideration of the characteristics of the particular document collection. Then, this border provides a tailor-made semantic concept representation for a document coming from the same domain. Another advantage of ACR is that it is applicable in both classification task where the groups are given in the training document set and clustering task where no group information is available. The experimental results show that ACR outperforms an existing static method in almost all cases. We also present a method to integrate Wikipedia entities into an expert-edited ontology, namely WordNet, to generate an enhanced ontology named WordNet-Plus, and its performance is also examined under the ACR model. Due to the high coverage, WordNet-Plus can outperform WordNet on data sets having more fresh documents in classification.