共查询到20条相似文献，搜索用时 46 毫秒
1.
《Information processing & management》2022,59(4):102953
Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison. 相似文献
2.
Generating gene summaries from biomedical literature: A study of semi-structured summarization 总被引：3，自引：0，他引：3
Xu Ling Jing Jiang Xin He Qiaozhu Mei Chengxiang Zhai Bruce Schatz 《Information processing & management》2007,43(6):1777
Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene – first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best. 相似文献
3.
Danushka Bollegala Naoaki Okazaki Mitsuru Ishizuka 《Information processing & management》2010,46(1):89-109
Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task. 相似文献
4.
基于短语本位语法体系，本文提出了一个混合模板方法用于实现汉语口语对话系统语言生成器。在此混合模板方法中：定义短语模板用于生成汉语短语；定义句子模板用于生成基本的肯定陈述句；此外还定义了句型变换规则用于生成肯定陈述之外的汉语句型，比如否定句和疑问句等。实验表明，混合模板汉语句子生成器具有高可移植性和相对令人满意的语句生成效果，由此可认为该混合模板方法是可行的。 相似文献
5.
《Information processing & management》2019,56(6):102090
Recently, using a pretrained word embedding to represent words achieves success in many natural language processing tasks. According to objective functions, different word embedding models capture different aspects of linguistic properties. However, the Semantic Textual Similarity task, which evaluates similarity/relation between two sentences, requires to take into account of these linguistic aspects. Therefore, this research aims to encode various characteristics from multiple sets of word embeddings into one embedding and then learn similarity/relation between sentences via this novel embedding. Representing each word by multiple word embeddings, the proposed MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension. 相似文献
6.
Saliency and coverage are two of the most important issues in document summarization. In most summarization methods, the saliency issue is usually of top priority. Many studies are conducted to develop better sentence ranking methods to identify the salient sentences for summarization. It is also well acknowledged that sentence selection strategies are very important, which mainly aim at reducing the redundancy among the selected sentences to enable them to cover more concepts. In this paper, we propose a novel sentence selection strategy that follows a progressive way to select the summary sentences. We intend to ensure the coverage of the summary first by an intuitive idea, i.e., considering the uncovered concepts only when measuring the saliency of the sentences. Moreover, we consider the subsuming relationship between sentences to define a conditional saliency measure of the sentences instead of the general saliency measures used in most existing methods. Based on these ideas, a progressive sentence selection strategy is developed to discover the “novel and salient” sentences. Compared with traditional methods, the saliency and coverage issues are more integrated in the proposed method. Experimental studies conducted on the DUC data sets demonstrate the advantages of the progressive sentence selection strategy. 相似文献
7.
《Information processing & management》2022,59(3):102891
Syntax parse trees are a method of representing sentence structure and are often used to provide models with syntax information and enhance downstream task performance. Because grammar and syntax are inherently linked, the incorporation of syntax parse trees in GEC is a natural solution. In this work, we present a method of incorporating syntax parse trees for Grammatical Error Correction (GEC). Building off a strong sequence-to-sequence Transformer baseline, we present a unified parse integration method for GEC that allows for the use of both dependency and constituency parse trees, as well as their combination - a syntactic graph. Specifically, on the sentence encoder, we propose a graph encoder that can encode dependency trees and constituent trees at the same time, yielding two representations for terminal nodes (i.e., the token of the sentence) and non-terminal nodes. We next use two cross-attentions (NT-Cross-Attention and T-Cross-Attention) to aggregate these source syntactic representations to the target side for final corrections prediction. In addition to evaluating our models on the popular CoNLL-2014 Shared Task and JFLEG GEC benchmarks, we affirm the effectiveness of our proposed method by testing both varying levels of parsing quality and exploring the use of both parsing formalisms. With further empirical exploration and analysis to identify the source of improvement, we found that rich syntax information provided clear clues for GEC; a syntactic graph composed of multiple syntactic parse trees can effectively compensate for the limited quality and insufficient error correction capability of a single syntactic parse tree. 相似文献
8.
High quality summary is the target and challenge for any automatic text summarization. In this paper, we introduce a different hybrid model for automatic text summarization problem. We exploit strengths of different techniques in building our model: we use diversity-based method to filter similar sentences and select the most diverse ones, differentiate between the more important and less important features using the swarm-based method and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text features weights flexibly tolerated. The diversity-based method focuses to reduce redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We presented the proposed model in two forms. In the first form of the model, diversity measures dominate the behavior of the model. In the second form, the diversity constraint is no longer imposed on the model behavior. That means the diversity-based method works same as fuzzy swarm-based method. The results showed that the proposed model in the second form performs better than the first form, the swarm model, the fuzzy swarm method and the benchmark methods. Over results show that combination of diversity measures, swarm techniques and fuzzy logic can generate good summary containing the most important parts in the document. 相似文献
9.
The paper presents a study investigating the effects of incorporating novelty detection in automatic text summarisation. Condensing a textual document, automatic text summarisation can reduce the need to refer to the source document. It also offers a means to deliver device-friendly content when accessing information in non-traditional environments. An effective method of summarisation could be to produce a summary that includes only novel information. However, a consequence of focusing exclusively on novel parts may result in a loss of context, which may have an impact on the correct interpretation of the summary, with respect to the source document. In this study we compare two strategies to produce summaries that incorporate novelty in different ways: a constant length summary, which contains only novel sentences, and an incremental summary, containing additional sentences that provide context. The aim is to establish whether a summary that contains only novel sentences provides sufficient basis to determine relevance of a document, or if indeed we need to include additional sentences to provide context. Findings from the study seem to suggest that there is only a minimal difference in performance for the tasks we set our users and that the presence of contextual information is not so important. However, for the case of mobile information access, a summary that contains only novel information does offer benefits, given bandwidth constraints. 相似文献
10.
In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA. 相似文献
11.
Minh-Tien Nguyen Viet Cuong Tran Xuan Hoai Nguyen Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515
In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods. 相似文献
12.
《Information processing & management》2016,52(4):698-719
In this paper, we focus on the problem of automatically generating amplified scientific paper’s abstract which represents the most influential aspects of scientific paper. The influential aspects can be illustrated by the target scientific paper’s abstract and citation sentences discussing the target paper, which are provided in papers citing the target paper. In this paper, we extract representative sentences through data-weighted reconstruction approach(DWR) by jointly leveraging target scientific paper’s abstract and citation sentences’ content and structure. In our study, we make two-folded contributions.Firstly, sentence’s weight was learned by exploiting regularization for ranking on heterogeneous bibliographic network. Specially, Sentences-similar-Sentences relationship was identified by language modeling-based approach and added to the bibliographic network. Secondly, a data-weighted reconstruction objective function is optimized to select the most representative sentences which reconstructs the original sentence set with minimum error. In this process, sentences’ weight plays a critical role. Experimental evaluation over real dataset confirms the effectiveness of our approach. 相似文献
13.
Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel
We propose a social relation extraction system using dependency-kernel-based support vector machines (SVMs). The proposed system classifies input sentences containing two people’s names on the basis of whether they do or do not describe social relations between two people. The system then extracts relation names (i.e., social-related keywords) from sentences describing social relations. We propose new tree kernels called dependency trigram kernels for effectively implementing these processes using SVMs. Experiments showed that the proposed kernels delivered better performance than the existing dependency kernel. On the basis of the experimental evidence, we suggest that the proposed system can be used as a useful tool for automatically constructing social networks from unstructured texts. 相似文献
14.
Basant Agarwal Heri Ramampiaro Helge Langseth Massimiliano Ruocco 《Information processing & management》2018,54(6):922-937
This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches. 相似文献
15.
16.
The rise in the amount of textual resources available on the Internet has created the need for tools of automatic document summarization. The main challenges of query-oriented extractive summarization are (1) to identify the topics of the documents and (2) to recover query-relevant sentences of the documents that together cover these topics. Existing graph- or hypergraph-based summarizers use graph-based ranking algorithms to produce individual scores of relevance for the sentences. Hence, these systems fail to measure the topics jointly covered by the sentences forming the summary, which tends to produce redundant summaries. To address the issue of selecting non-redundant sentences jointly covering the main query-relevant topics of a corpus, we propose a new method using the powerful theory of hypergraph transversals. First, we introduce a new topic model based on the semantic clustering of terms in order to discover the topics present in a corpus. Second, these topics are modeled as the hyperedges of a hypergraph in which the nodes are the sentences. A summary is then produced by generating a transversal of nodes in the hypergraph. Algorithms based on the theory of submodular functions are proposed to generate the transversals and to build the summaries. The proposed summarizer outperforms existing graph- or hypergraph-based summarizers by at least 6% of ROUGE-SU4 F-measure on DUC 2007 dataset. It is moreover cheaper than existing hypergraph-based summarizers in terms of computational time complexity. 相似文献
17.
A challenge for sentence categorization and novelty mining is to detect not only when text is relevant to the user’s information need, but also when it contains something new which the user has not seen before. It involves two tasks that need to be solved. The first is identifying relevant sentences (categorization) and the second is identifying new information from those relevant sentences (novelty mining). Many previous studies of relevant sentence retrieval and novelty mining have been conducted on the English language, but few papers have addressed the problem of multilingual sentence categorization and novelty mining. This is an important issue in global business environments, where mining knowledge from text in a single language is not sufficient. In this paper, we perform the first task by categorizing Malay and Chinese sentences, then comparing their performances with that of English. Thereafter, we conduct novelty mining to identify the sentences with new information. Experimental results on TREC 2004 Novelty Track data show similar categorization performance on Malay and English sentences, which greatly outperform Chinese. In the second task, it is observed that we can achieve similar novelty mining results for all three languages, which indicates that our algorithm is suitable for novelty mining of multilingual sentences. In addition, after benchmarking our results with novelty mining without categorization, it is learnt that categorization is necessary for the successful performance of novelty mining. 相似文献
18.
《Information processing & management》2022,59(1):102734
Existing pseudo-relevance feedback (PRF) methods often divide an original query into individual terms for processing and select expansion terms based on the term frequency, proximity, position, etc. This process may lose some contextual semantic information from the original query. In this work, based on the classic Rocchio model, we propose a probabilistic framework that incorporates sentence-level semantics via Bidirectional Encoder Representations from Transformers (BERT) into PRF. First, we obtain the importance of terms at the term level. Then, we use BERT to interactively encode the query and sentences in the feedback document to acquire the semantic similarity score of a sentence and the query. Next, the semantic scores of different sentences are summed as the term score at the sentence level. Finally, we balance the term-level and sentence-level weights by adjusting factors and combine the terms with the top-k scores to form a new query for the next-round processing. We apply this method to three Rocchio-based models (Rocchio, PRoc2, and KRoc). A series of experiments are conducted based on six official TREC data sets. Various evaluation indicators suggest that the improved models achieve a significant improvement over the corresponding baseline models. Our proposed models provide a promising avenue for incorporating sentence-level semantics into PRF, which is feasible and robust. Through comparison and analysis of a case study, expansion terms obtained from the proposed models are shown to be more semantically consistent with the query. 相似文献
19.
Automated summaries help tackle the ever growing volume of information floating around. There are two broad categories: extract and abstract. In the former we retain the more important sentences more or less in their original structure, while the latter requires a fusion of multiple sentences and/or paraphrasing. This is a more challenging task than extract summaries. In this paper, we present a novel generic abstract summarizer for a single document in Arabic language. The system starts by segmenting the input text topic wise. Then, each textual segment is extractively summarized. Finally, we apply rule-based sentence reduction technique. The RST-based extractive summarizer is an enhanced version of the system in Azmi and Al-Thanyyan (2012). By controlling the size of the extract summary of each segment we can cap the size of the final abstractive summary. Both summarizers, the enhanced extractive and the abstractive, were evaluated. We tested our enhanced extractive summarizer on the same dataset in the aforementioned paper, using the measures recall, precision and Rouge. The results show noticeable improvement in the performance, specially the precision in shorter summaries. The abstractive summarizer was tested on a set of 150 documents, generating summaries of sizes 50%, 40%, 30% and 20% (of the original’s word count). The results were assessed by two human experts who graded them out of a maximum score of 5. The average score ranged between 4.53 and 1.92 for summaries at different granularities, with shorter summaries receiving the lower score. The experimental results are encouraging and demonstrate the effectiveness of our approach. 相似文献
20.
《Information processing & management》2020,57(6):102341
Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate performance. Hence, it seems vital to bridge the gap between short text units and conventional text processing methods.In this study, we propose a semantic method for implementing an extractive multi-document summarizer system by using a combination of statistical, machine learning based, and graph-based methods. It is a language-independent and unsupervised system. The proposed framework learns the semantic representation of words from a set of given documents via word2vec method. It expands each sentence through an innovative method with the most informative and the least redundant words related to the main topic of sentence. Sentence expansion implicitly performs word sense disambiguation and tunes the conceptual densities towards the central topic of each sentence. Then, it estimates the importance of sentences by using the graph representation of the documents. To identify the most important topics of the documents, we propose an inventive clustering approach. It autonomously determines the number of clusters and their initial centroids, and clusters sentences accordingly. The system selects the best sentences from appropriate clusters for the final summary with respect to information salience, minimum redundancy, and adequate coverage.A set of extensive experiments on DUC2002 and DUC2006 datasets was conducted for investigating the proposed scheme. Experimental results showed that the proposed sentence expansion algorithm and clustering approach could considerably enhance the performance of the summarization system. Also, comparative experiments demonstrated that the proposed framework outperforms most of the state-of-the-art summarizer systems and can impressively assist the task of extractive text summarization. 相似文献