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4th workshop on graph-based methods for natural language processing, Textgraphs 2009

作者： Wieling, Martijn Nerbonne, John University of Groningen Netherlands

ISBN: (纸本)193243254X

In this study we used bipartite spectral graph partitioning to simultaneously cluster varieties and sound correspondences in Dutch dialect data. While clustering geographical varieties with respect to their pronunciation is not new, the simultaneous identification of the sound correspondences giving rise to the geographical clustering presents a novel opportunity in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of aggregate differences. The determination of the significant sound correspondences which co-varied with cluster membership was carried out on a post hoc basis. Bipartite spectral graph clustering simultaneously seeks groups of individual sound correspondences which are associated, even while seeking groups of sites which share sound correspondences. We show that the application of this method results in clear and sensible geographical groupings and discuss the concomitant sound correspondences.

关键词： graph theory

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PGL at Textgraphs 2020 Shared Task: Explanation Regeneration using language and graph Learning methods 14

PGL at TextGraphs 2020 Shared Task: Explanation Regeneration...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Li, Weibin Lu, Yuxiang Huang, Zhengjie Liu, Jiaxiang Su, Weiyue Feng, Shikun Sun, Yu Baidu Inc. China

ISBN: (纸本)9781952148422

This paper describes the system designed by the Baidu PGL Team which achieved the first place in the Textgraphs 2020 Shared Task. The task focuses on generating explanations for elementary science questions. Given a question and its corresponding correct answer, we are asked to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question. Then, we adopt a re-ranking approach based on a pre-trained language model to rank the candidate explanations. To further improve the rankings, we also develop an architecture consisting both powerful pre-trained transformers and GNNs to tackle the multi-hop inference problem. The official evaluation shows that, our system can outperform the second best system by 1.91 points. © COLING *** rights reserved.

关键词： Knowledge based systems

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KagNet: Knowledge-Aware graph Networks for Commonsense Reasoning 9

KagNet: Knowledge-Aware Graph Networks for Commonsense Reaso...

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Lin, Bill Yuchen Chen, Xinyue Chen, Jamin Ren, Xiang Univ Southern Calif Comp Sci Dept Los Angeles CA 90007 USA Shanghai Jiao Tong Univ Comp Sci Dept Shanghai Peoples R China

ISBN: (纸本)9781950737901

Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KAGNE T, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for BERT-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning. We open-source our code1 to the community for future research in knowledge-aware commonsense reasoning.

关键词： Knowledge graph

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Parameter free hierarchical graph-based clustering for analyzing continuous word embeddings 11

Parameter free hierarchical graph-based clustering for analy...

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Trost, Thomas A. Klakow, Dietrich Saarland University Saarbrücken Germany

ISBN: (纸本)9781945626609

Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity. © 2017 Association for Computational Linguistics

关键词： Embeddings

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graph-based Keyword Planning for Legal Clause Generation from Topics 4

Graph-based Keyword Planning for Legal Clause Generation fro...

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4th natural Legal language processing workshop, NLLP 2022, co-located with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Joshi, Sagar Balaji, Sumanth Garimella, Aparna Varma, Vasudeva IIIT Hyderabad India Adobe Research United States

ISBN: (纸本)9781959429180

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts. © 2022 Association for Computational Linguistics.

关键词： Pipelines

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A graphical framework for contextual search and name disambiguation in email 1

A graphical framework for contextual search and name disambi...

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1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Minkov, Einat Cohen, William W. Ng, Andrew Y. Language Technologies Inst Carnegie Mellon University PittsburghPA15213 United States Machine Learning Dept Carnegie Mellon University PittsburghPA15213 United States Computer Science Dept Stanford University StanfordCA94305 United States

Similarity measures for text have historically been an important tool for solving information retrieval problems. In this paper we consider extended similarity metrics for documents and other objects embedded in graphs, facilitated via a lazy graph walk. We provide a detailed instantiation of this framework for email data, where content, social networks and a timeline are integrated in a structural graph. The suggested framework is evaluated for the task of disambiguating names in email documents. We show that reranking schemes based on the graph-walk similarity measures often outperform baseline methods, and that further improvements can be obtained by use of appropriate learning methods. © 2006 Association for Computational Linguistics

关键词： Electronic mail

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LexRank: graph-based lexical centrality as salience in text summarization

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2004年第0期22卷 457-479页

作者： Erkan, G Radev, DR Univ Michigan Dept EECS Ann Arbor MI 48109 USA Univ Michigan Sch Informat Ann Arbor MI 48109 USA

We introduce a stochastic graph-based method for computing relative importance of textual units for natural language processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.

关键词： CACNA1C gene texts TBXAS1 gene graph representations THRESHOLDING METHOD natural language processing

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workshop on Unsupervised Learning in NLP at the 2011 Conference on Empirical methods in natural language processing, EMNLP 2011 - proceedings

Workshop on Unsupervised Learning in NLP at the 2011 Confere...

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1st workshop on Unsupervised Learning in NLP at the 2011 Conference on Empirical methods in natural language processing, EMNLP 2011

ISBN: (纸本)1937284131

The proceedings contain 13 papers. The topics discussed include: structured databases of named entities from Bayesian nonparametrics;unsupervised cross-lingual lexical substitution;reducing the size of the representation for the uDOP-estimate;evaluating unsupervised learning for natural language processing tasks;unsupervised language-independent name translation mining from Wikipedia infoboxes;twitter polarity classification with label propagation over lexical links and the follower graph;unsupervised concept annotation using latent Dirichlet allocation and segmental methods;and unsupervised alignment for segmental-based language understanding.

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Spatial language understanding with multimodal graphs using declarative learning based programming 2

Spatial language understanding with multimodal graphs using ...

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2nd workshop on Structured Prediction for natural language processing, SPNLP 2017, held in conjunction with the Conference on Empirical methods in natural language processing, EMNLP 2017

作者： Kordjamshidi, Parisa Rahgooy, Taher Manzoor, Umar Computer Science Department Tulane University New Orleans United States

ISBN: (纸本)9781945626937

This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in the framework of declarative learning-based programming (DeLBP). The DeLBP framework facilitates combining modalities and representing various data in a unified graph. The learning and inference models exploit the structure of the unified graph as well as the global first order domain constraints beyond the data to predict the semantics which forms a structured meaning representation of the spatial context. Continuous representations are used to relate the various elements of the graph originating from different modalities. We improved over the state-of-the-art results on SpRL. © EMNLP *** right reserved.

关键词： Semantics

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Reducing Knowledge Noise for Improved Semantic Analysis in Biomedical natural language processing Applications 5

Reducing Knowledge Noise for Improved Semantic Analysis in B...

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5th workshop on Clinical natural language processing, ClinicalNLP 2023. held at ACL 2023

作者： Naseem, Usman Thapa, Surendrabikram Zhang, Qi Hu, Liang Masood, Anum Nasim, Mehwish University of Sydney Australia Virginia Tech United States Tongji University China DeepBlue Academy of Sciences China Norwegian University of Science and Technology Norway University of Western Australia Australia Flinders University Australia

ISBN: (纸本)9781959429883

graph-based techniques have gained traction for representing and analyzing data in various natural language processing (NLP) tasks. Knowledge graph-based language representation models have shown promising results in leveraging domain-specific knowledge for NLP tasks, particularly in the biomedical NLP field. However, such models have limitations, including knowledge noise and neglect of contextual relationships, leading to potential semantic errors and reduced accuracy. To address these issues, this paper proposes two novel methods. The first method combines knowledge graphbased language model with nearest-neighbor models to incorporate semantic and category information from neighboring instances. The second method involves integrating knowledge graph-based language model with graph neural networks (GNNs) to leverage feature information from neighboring nodes in the graph. Experiments on relation extraction (RE) and classification tasks in English and Chinese language datasets demonstrate significant performance improvements with both methods, highlighting their potential for enhancing the performance of language models and improving NLP applications in the biomedical domain. © 2023 Association for Computational Linguistics.

关键词： Semantics

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