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1.
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2.
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   

3.
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   

4.
Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.  相似文献   

5.
Existing unsupervised keyphrase extraction methods typically emphasize the importance of the candidate keyphrase itself, ignoring other important factors such as the influence of uninformative sentences. We hypothesize that the salient sentences of a document are particularly important as they are most likely to contain keyphrases, especially for long documents. To our knowledge, our work is the first attempt to exploit sentence salience for unsupervised keyphrase extraction by modeling hierarchical multi-granularity features. Specifically, we propose a novel position-aware graph-based unsupervised keyphrase extraction model, which includes two model variants. The pipeline model first extracts salient sentences from the document, followed by keyphrase extraction from the extracted salient sentences. In contrast to the pipeline model which models multi-granularity features in a two-stage paradigm, the joint model accounts for both sentence and phrase representations of the source document simultaneously via hierarchical graphs. Concretely, the sentence nodes are introduced as an inductive bias, injecting sentence-level information for determining the importance of candidate keyphrases. We compare our model against strong baselines on three benchmark datasets including Inspec, DUC 2001, and SemEval 2010. Experimental results show that the simple pipeline-based approach achieves promising results, indicating that keyphrase extraction task benefits from the salient sentence extraction task. The joint model, which mitigates the potential accumulated error of the pipeline model, gives the best performance and achieves new state-of-the-art results while generalizing better on data from different domains and with different lengths. In particular, for the SemEval 2010 dataset consisting of long documents, our joint model outperforms the strongest baseline UKERank by 3.48%, 3.69% and 4.84% in terms of F1@5, F1@10 and F1@15, respectively. We also conduct qualitative experiments to validate the effectiveness of our model components.  相似文献   

6.
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   

7.
The technologies for single- and multi-document summarization that are described and evaluated in this article can be used on heterogeneous texts for different summarization tasks. They refer to the extraction of important sentences from the documents, compressing the sentences to their essential or relevant content, and detecting redundant content across sentences. The technologies are tested at the Document Understanding Conference, organized by the National Institute of Standards and Technology, USA in 2002 and 2003. The system obtained good to very good results in this competition. We tested our summarization system also on a variety of English Encyclopedia texts and on Dutch magazine articles. The results show that relying on generic linguistic resources and statistical techniques offer a basis for text summarization.  相似文献   

8.
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   

9.
A bottom-up approach to sentence ordering for multi-document summarization   总被引：1，自引：0，他引：1  
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   

10.
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   

11.
【目的/意义】金融领域实体关系抽取是构造金融知识库的基础,对金融领域的文本信息利用具有重要作 用。本文提出金融领域实体关系联合抽取模型,增加了对金融文本复杂重叠关系的识别,可以有效避免传统的流 水线模型中识别错误在不同任务之间的传递。【方法/过程】本文构建了高质量金融文本语料,提出一种新的序列 标注模式和实体关系匹配规则,在预训练语言模型BERT（Bidirectional Encoder Representations from Transformers） 的基础上结合双向门控循环单元 BiGRU（Bidirectional Gated Recurrent Units）与条件随机场 CRF（Conditional Random Field）构建了端到端的序列标注模型,实现了实体关系的联合抽取。【结果/结论】针对金融领域文本数据 进行实验,实验结果表明本文提出的联合抽取模型在关系抽取以及重叠关系抽取上的F1值分别达到了0.627和 0.543,初步验证了中文语境下本文模型对金融领域实体关系抽取的有效性。【创新/局限】结合金融文本特征提出 了新的序列标注模式并构建了基于BERT的金融领域实体关系联合抽取模型,实现了对金融文本中实体间重叠关 系的识别。  相似文献   

12.
Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification.  相似文献   

13.
The purpose of extractive speech summarization is to automatically select a number of indicative sentences or paragraphs (or audio segments) from the original spoken document according to a target summarization ratio and then concatenate them to form a concise summary. Much work on extractive summarization has been initiated for developing machine-learning approaches that usually cast important sentence selection as a two-class classification problem and have been applied with some success to a number of speech summarization tasks. However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance. Furthermore, training the summarizer by improving the associated classification accuracy does not always lead to better summarization evaluation performance. In view of such phenomena, we present in this paper an empirical investigation of the merits of two schools of training criteria to alleviate the negative effects caused by the aforementioned problems, as well as to boost the summarization performance. One is to learn the classification capability of a summarizer on the basis of the pair-wise ordering information of sentences in a training document according to a degree of importance. The other is to train the summarizer by directly maximizing the associated evaluation score or optimizing an objective that is linked to the ultimate evaluation. Experimental results on the broadcast news summarization task suggest that these training criteria can give substantial improvements over a few existing summarization methods.  相似文献   

14.
Coreference resolution of geological entities is an important task in geological information mining. Although the existing generic coreference resolution models can handle geological texts, a dramatic decline in their performance can occur without sufficient domain knowledge. Due to the high diversity of geological terminology, coreference is intricately governed by the semantic and expressive structure of geological terms. In this paper, a framework CorefRoCNN based on RoBERTa and convolutional neural network (CNN) for end-to-end coreference resolution of geological entities is proposed. Firstly, the fine-tuned RoBERTa language model is used to transform words into dynamic vector representations with contextual semantic information. Second, a CNN-based multi-scale structure feature extraction module for geological terms is designed to capture the invariance of geological terms in length, internal structure, and distribution. Thirdly, we incorporate the structural feature and word embedding for further determinations of coreference relations. In addition, attention mechanisms are used to improve the ability of the model to capture valid information in geological texts with long sentence lengths. To validate the effectiveness of the model, we compared it with several state-of-the-art models on the constructed dataset. The results show that our model has the optimal performance with an average F1 value of 79.78%, which is a 1.22% improvement compared to the second-ranked method.  相似文献   

15.
The problem of quality estimation of crowdsourced work is of great importance. Although a variety of aggregation methods have been proposed to find high-quality structured claims in multiple-choice crowdsourcing tasks such as item labeling, they do not apply to more general tasks, such as article writing and brand design with unstructured submissions. One possibility to tackle this problem is to ask another set of crowd workers to review and grade each submission, essentially transforming unstructured submissions into structured ratings. Nevertheless, such an approach incurs unnecessary monetary cost and delay. In this paper, we address this problem by exploiting task requesters’ historical feedback and directly modeling the submission quality. We propose two embedding-based methods where the first one learns worker embedding and the second one learns both worker embedding and meta information embedding, with additional consideration of neighborhood similarity. Experimental results on three large-scale crowdsourcing data sets demonstrate that our embedding-based feature-learning methods perform much better than feature-engineering methods that use popular learning-to-rank algorithms. At the same time, our methods do not require additional crowdsourced grading.  相似文献   

16.
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   

17.
This investigation deals with the problem of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatically identifying the language of a given text. Although there exists several methods in the literature, their performances are not so convincing in practice.In this contribution, we propose two statistical approaches: the high frequency approach and the nearest prototype approach. In the first one, 5 algorithms of language identification are proposed and implemented, namely: character based identification (CBA), word based identification (WBA), special characters based identification (SCA), sequential hybrid algorithm (HA1) and parallel hybrid algorithm (HA2). In the second one, we use 11 similarity measures combined with several types of character N-Grams.For the evaluation task, the proposed methods are tested on forum datasets containing 32 different languages. Furthermore, an experimental comparison is made between the proposed approaches and some referential language identification tools such as: LIGA, NTC, Google translate and Microsoft Word. Results show that the proposed approaches are interesting and outperform the baseline methods of language identification on forum texts.  相似文献   

18.
Fact verification aims to retrieve relevant evidence from a knowledge base, e.g., Wikipedia, to verify the given claims. Existing methods only consider the sentence-level semantics for evidence representations, which typically neglect the importance of fine-grained features in the evidence-related sentences. In addition, the interpretability of the reasoning process has not been well studied in the field of fact verification. To address such issues, we propose an entity-graph based reasoning method for fact verification abbreviated as RoEG, which generates the fine-grained features of evidence at the entity-level and models the human reasoning paths based on an entity graph. In detail, to capture the semantic relations of retrieved evidence, RoEG introduces the entities as nodes and constructs the edges in the graph based on three linking strategies. Then, RoEG utilizes a selection gate to constrain the information propagation in the sub-graph of relevant entities and applies a graph neural network to propagate the entity-features for reasoning. Finally, RoEG employs an attention aggregator to gather the information of entities for label prediction. Experimental results on a large-scale benchmark dataset FEVER demonstrate the effectiveness of our proposal by beating the competitive baselines in terms of label accuracy and FEVER Score. In particular, for a task of multiple-evidence fact verification, RoEG produces 5.48% and 4.35% improvements in terms of label accuracy and FEVER Score against the state-of-the-art baseline. In addition, RoEG shows a better performance when more entities are involved for fact verification.  相似文献   

19.
Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.  相似文献   

20.
As an emerging task in opinion mining, End-to-End Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract all the aspect-sentiment pairs mentioned in a pair of sentence and image. Most existing methods of MABSA do not explicitly incorporate aspect and sentiment information in their textual and visual representations and fail to consider the different contributions of visual representations to each word or aspect in the text. To tackle these limitations, we propose a multi-task learning framework named Cross-Modal Multitask Transformer (CMMT), which incorporates two auxiliary tasks to learn the aspect/sentiment-aware intra-modal representations and introduces a Text-Guided Cross-Modal Interaction Module to dynamically control the contributions of the visual information to the representation of each word in the inter-modal interaction. Experimental results demonstrate that CMMT consistently outperforms the state-of-the-art approach JML by 3.1, 3.3, and 4.1 absolute percentage points on three Twitter datasets for the End-to-End MABSA task, respectively. Moreover, further analysis shows that CMMT is superior to comparison systems in both aspect extraction (AE) and sentiment classification (SC), which would move the development of multimodal AE and SC algorithms forward with improved performance.  相似文献   

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