The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform

Randy Goebel - One of the best experts on this subject based on the ideXlab platform.

  • textual entailment in Legal bar exam Question answering using deep siamese networks
    International Symposium on Artificial Intelligence, 2017
    Co-Authors: Yao Lu, Randy Goebel
    Abstract:

    Every day a large volume of Legal documents are produced, and lawyers need support for their analysis, especially in corporate litigation. Typically, corporate litigation has the aim of finding evidence for or against the litigation claims. Identifying the critical Legal points within large volumes of Legal text is time consuming and costly, but recent advances in natural language processing and information extraction have provided new enthusiasm for improved automated management of Legal texts and the identification of Legal relationships. As a Legal information extraction example, we have constructed a Question answering system for Yes/No bar exam Questions. Here we introduce a Siamese deep Convolutional Neural Network for textual entailment in support of Legal Question answering. We have evaluated our system using the data from the competition on Legal information extraction/entailment (COLIEE). The competition focuses on the Legal information processing required to answer yes/no Questions from Legal bar exams, and it consists of two phases: Legal ad-hoc information retrieval (Phase 1), and textual entailment (Phase 2). We focus on Phase 2, which requires “Yes” or “No” answers to previously unseen queries. We do this by comparing the extracted meanings of queries and relevant articles. Our choice of features used for the semantic modeling focuses on word properties and negation. Experimental evaluation demonstrates the effectiveness of the Siamese Convolutional Neural Network, and our results show that our Siamese deep learning-based method outperforms the previous use of a single Convolutional Neural Network.

  • two step cascaded textual entailment for Legal bar exam Question answering
    International Conference on Artificial Intelligence and Law, 2017
    Co-Authors: Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and exploits semantic information using a logic-based representation. We have evaluated our system using the data from the competition on Legal information extraction/entailment (COLIEE)-2017. The competition focuses on the Legal information processing required to answer yes/no Questions from Japanese Legal bar exams, and it consists of two phases: ad hoc Legal information retrieval (Phase 1), and textual entailment (Phase 2). Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For this phase, we have used an information retrieval approach using TF-IDF combined with a simple language model. Phase 2 requires a yes/no decision for previously unseen queries, which we approach by comparing the approximate meanings of queries with relevant statutes. Our meaning extraction process uses a selection of features based on a kind of paraphrase, coupled with a condition/conclusion/exception analysis of articles and queries. We also extract and exploit negation patterns from the articles. We construct a logic-based representation as a semantic analysis result, and then classify Questions into easy and difficult types by analyzing the logic representation. If a Question is in our easy category, we simply obtain the entailment answer from the logic representation; otherwise we use an unsupervised learning method to obtain the entailment answer. Experimental evaluation shows that our result ranked highest in the Phase 2 amongst all COLIEE-2017 competitors.

  • JSAI-isAI Workshops - Applying a Convolutional Neural Network to Legal Question Answering
    New Frontiers in Artificial Intelligence, 2017
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and we describe a Legal Question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on Legal information extraction/entailment (COLIEE). The competition focuses on the Legal information processing related to answering yes/no Questions from Japanese Legal bar exams, and it consists of three phases: ad-hoc Legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering.

  • Question answering of bar exams by paraphrasing and Legal text analysis
    International Symposium on Artificial Intelligence, 2016
    Co-Authors: Ying Xu, Yao Lu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and exploits paraphrasing and sentence-level analysis of queries and Legal statutes. We have evaluated our system using the training data from the competition on Legal information extraction/entailment (COLIEE)-2016. The competition focuses on the Legal information processing required to answer yes/no Questions from Japanese Legal bar exams, and it consists of three phases: Legal ad-hoc information retrieval (Phase 1), textual entailment (Phase 2), and a combination of information retrieval and textual entailment (Phase 3). Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For this phase, we have used an information retrieval approach using TF-IDF and a Ranking SVM. Phase 2 requires decision on yes/no answer for previously unseen queries, which we approach by comparing the approximate meanings of queries with relevant articles. Our meaning extraction process uses a selection of features based on a kind of paraphrase, coupled with a condition/conclusion/exception analysis of articles and queries. We also identify synonym relations using word embedding, and detect negation patterns from the articles. Our heuristic selection of attributes is used to build an SVM model, which provides the basis for ranking a decision on the yes/no Questions. Experimental evaluation show that our method outperforms previous methods. Our result ranked highest in the Phase 3 in the COLIEE-2016 competition.

  • applying a convolutional neural network to Legal Question answering
    International Symposium on Artificial Intelligence, 2015
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and we describe a Legal Question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on Legal information extraction/entailment (COLIEE). The competition focuses on the Legal information processing related to answering yes/no Questions from Japanese Legal bar exams, and it consists of three phases: ad-hoc Legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering.

Adam Z. Wyner - One of the best experts on this subject based on the ideXlab platform.

  • A Semi-automated Ontology Construction for Legal Question Answering
    New Generation Computing, 2019
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Jeff Z. Pan, Adam Z. Wyner
    Abstract:

    The internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to a Legal Question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other Question statement (Q), so whether Bg entails Q based on the application of the Legal rules. The key contribution of this paper is the methodology and the semi-automated Legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • a methodology for a criminal law and procedure ontology for Legal Question answering
    International Semantic Technology Conference, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • JIST - A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering
    Semantic Technology, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

Biralatei Fawei - One of the best experts on this subject based on the ideXlab platform.

  • A Semi-automated Ontology Construction for Legal Question Answering
    New Generation Computing, 2019
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Jeff Z. Pan, Adam Z. Wyner
    Abstract:

    The internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to a Legal Question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other Question statement (Q), so whether Bg entails Q based on the application of the Legal rules. The key contribution of this paper is the methodology and the semi-automated Legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • a methodology for a criminal law and procedure ontology for Legal Question answering
    International Semantic Technology Conference, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • JIST - A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering
    Semantic Technology, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

Ying Xu - One of the best experts on this subject based on the ideXlab platform.

  • JSAI-isAI Workshops - Applying a Convolutional Neural Network to Legal Question Answering
    New Frontiers in Artificial Intelligence, 2017
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and we describe a Legal Question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on Legal information extraction/entailment (COLIEE). The competition focuses on the Legal information processing related to answering yes/no Questions from Japanese Legal bar exams, and it consists of three phases: ad-hoc Legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering.

  • Question answering of bar exams by paraphrasing and Legal text analysis
    International Symposium on Artificial Intelligence, 2016
    Co-Authors: Ying Xu, Yao Lu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and exploits paraphrasing and sentence-level analysis of queries and Legal statutes. We have evaluated our system using the training data from the competition on Legal information extraction/entailment (COLIEE)-2016. The competition focuses on the Legal information processing required to answer yes/no Questions from Japanese Legal bar exams, and it consists of three phases: Legal ad-hoc information retrieval (Phase 1), textual entailment (Phase 2), and a combination of information retrieval and textual entailment (Phase 3). Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For this phase, we have used an information retrieval approach using TF-IDF and a Ranking SVM. Phase 2 requires decision on yes/no answer for previously unseen queries, which we approach by comparing the approximate meanings of queries with relevant articles. Our meaning extraction process uses a selection of features based on a kind of paraphrase, coupled with a condition/conclusion/exception analysis of articles and queries. We also identify synonym relations using word embedding, and detect negation patterns from the articles. Our heuristic selection of attributes is used to build an SVM model, which provides the basis for ranking a decision on the yes/no Questions. Experimental evaluation show that our method outperforms previous methods. Our result ranked highest in the Phase 3 in the COLIEE-2016 competition.

  • applying a convolutional neural network to Legal Question answering
    International Symposium on Artificial Intelligence, 2015
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    Our Legal Question answering system combines Legal information retrieval and textual entailment, and we describe a Legal Question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on Legal information extraction/entailment (COLIEE). The competition focuses on the Legal information processing related to answering yes/no Questions from Japanese Legal bar exams, and it consists of three phases: ad-hoc Legal information retrieval, textual entailment, and a learning model-driven combination of the two phases. Phase 1 requires the identification of Japan civil law articles relevant to a Legal bar exam query. For that phase, we have implemented a combined TF-IDF and Ranking SVM information retrieval component. Phase 2 requires the system to answer “Yes” or “No” to previously unseen queries, by comparing extracted meanings of queries with relevant articles. Our training of an entailment model focuses on features based on word embeddings, syntactic similarities and identification of negation/antonym relations. We augment our textual entailment component with a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first to adapt deep learning for textual entailment. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms our baseline SVM-based supervised model and K-means clustering.

  • JSAI-isAI Workshops - Legal Question Answering Using Ranking SVM and Syntactic/Semantic Similarity
    New Frontiers in Artificial Intelligence, 2015
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    We describe a Legal Question answering system which combines Legal information retrieval and textual entailment. We have evaluated our system using the data from the first competition on Legal information extraction/entailment (COLIEE) 2014. The competition focuses on two aspects of Legal information processing related to answering yes/no Questions from Japanese Legal bar exams. The shared task consists of two phases: Legal ad hoc information retrieval and textual entailment. The first phase requires the identification of Japan civil law articles relevant to a Legal bar exam query. We have implemented two unsupervised baseline models (tf-idf and Latent Dirichlet Allocation (LDA)-based Information Retrieval (IR)), and a supervised model, Ranking SVM, for the task. The features of the model are a set of words, and scores of an article based on the corresponding baseline models. The results show that the Ranking SVM model nearly doubles the Mean Average Precision compared with both baseline models. The second phase is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. The features used for phase two are syntactic/semantic similarities and identification of negation/antonym relations. The results show that our method, combined with rule-based model and the unsupervised model, outperforms the SVM-based supervised model.

  • Legal Question answering using ranking svm and syntactic semantic similarity
    International Symposium on Artificial Intelligence, 2014
    Co-Authors: Ying Xu, Randy Goebel
    Abstract:

    We describe a Legal Question answering system which combines Legal information retrieval and textual entailment. We have evaluated our system using the data from the first competition on Legal information extraction/entailment (COLIEE) 2014. The competition focuses on two aspects of Legal information processing related to answering yes/no Questions from Japanese Legal bar exams. The shared task consists of two phases: Legal ad hoc information retrieval and textual entailment. The first phase requires the identification of Japan civil law articles relevant to a Legal bar exam query. We have implemented two unsupervised baseline models (tf-idf and Latent Dirichlet Allocation (LDA)-based Information Retrieval (IR)), and a supervised model, Ranking SVM, for the task. The features of the model are a set of words, and scores of an article based on the corresponding baseline models. The results show that the Ranking SVM model nearly doubles the Mean Average Precision compared with both baseline models. The second phase is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. The features used for phase two are syntactic/semantic similarities and identification of negation/antonym relations. The results show that our method, combined with rule-based model and the unsupervised model, outperforms the SVM-based supervised model.

Martin Kollingbaum - One of the best experts on this subject based on the ideXlab platform.

  • A Semi-automated Ontology Construction for Legal Question Answering
    New Generation Computing, 2019
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Jeff Z. Pan, Adam Z. Wyner
    Abstract:

    The internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to a Legal Question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other Question statement (Q), so whether Bg entails Q based on the application of the Legal rules. The key contribution of this paper is the methodology and the semi-automated Legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • a methodology for a criminal law and procedure ontology for Legal Question answering
    International Semantic Technology Conference, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

  • JIST - A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering
    Semantic Technology, 2018
    Co-Authors: Biralatei Fawei, Martin Kollingbaum, Adam Z. Wyner
    Abstract:

    The Internet and the development of the semantic web have created the opportunity to provide structured Legal data on the web. However, most Legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language Question. One approach is to develop systems of Legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable Legal rules. The purpose of the ontology is to provide reasoning support to an Legal Question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).