21,454 research outputs found
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
Case Law has a significant impact on the proceedings of legal cases.
Therefore, the information that can be obtained from previous court cases is
valuable to lawyers and other legal officials when performing their duties.
This paper describes a methodology of applying discourse relations between
sentences when processing text documents related to the legal domain. In this
study, we developed a mechanism to classify the relationships that can be
observed among sentences in transcripts of United States court cases. First, we
defined relationship types that can be observed between sentences in court case
transcripts. Then we classified pairs of sentences according to the
relationship type by combining a machine learning model and a rule-based
approach. The results obtained through our system were evaluated using human
judges. To the best of our knowledge, this is the first study where discourse
relationships between sentences have been used to determine relationships among
sentences in legal court case transcripts.Comment: Conference: 2018 International Conference on Advances in ICT for
Emerging Regions (ICTer
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Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition
The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, with possible malicious intent. It is, therefore, important to
develop methods to assess or verify the integrity of these multimedia packages.
Using computer vision and natural language processing methods to directly
compare the image (or video) and the associated caption to verify the integrity
of a media package is only possible for a limited set of objects and scenes. In
this paper, we present a novel deep learning-based approach for assessing the
semantic integrity of multimedia packages containing images and captions, using
a reference set of multimedia packages. We construct a joint embedding of
images and captions with deep multimodal representation learning on the
reference dataset in a framework that also provides image-caption consistency
scores (ICCSs). The integrity of query media packages is assessed as the
inlierness of the query ICCSs with respect to the reference dataset. We present
the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media
packages from Flickr, which we make available to the research community. We use
both the newly created dataset as well as Flickr30K and MS COCO datasets to
quantitatively evaluate our proposed approach. The reference dataset does not
contain unmanipulated versions of tampered query packages. Our method is able
to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO,
respectively, for detecting semantically incoherent media packages.Comment: *Ayush Jaiswal and Ekraam Sabir contributed equally to the work in
this pape
EXPLOITING N-GRAM IMPORTANCE AND ADDITIONAL KNOWEDGE BASED ON WIKIPEDIA FOR IMPROVEMENTS IN GAAC BASED DOCUMENT CLUSTERING
This paper provides a solution to the issue: “How can we use Wikipedia based concepts in document\ud
clustering with lesser human involvement, accompanied by effective improvements in result?” In the\ud
devised system, we propose a method to exploit the importance of N-grams in a document and use\ud
Wikipedia based additional knowledge for GAAC based document clustering. The importance of N-grams\ud
in a document depends on several features including, but not limited to: frequency, position of their\ud
occurrence in a sentence and the position of the sentence in which they occur, in the document. First, we\ud
introduce a new similarity measure, which takes the weighted N-gram importance into account, in the\ud
calculation of similarity measure while performing document clustering. As a result, the chances of topical similarity in clustering are improved. Second, we use Wikipedia as an additional knowledge base both, to remove noisy entries from the extracted N-grams and to reduce the information gap between N-grams that are conceptually-related, which do not have a match owing to differences in writing scheme or strategies. Our experimental results on the publicly available text dataset clearly show that our devised system has a significant improvement in performance over bag-of-words based state-of-the-art systems in this area
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
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