scholarly journals Accounting for Sentence Position and Legal Domain Sentence Embedding in Learning to Classify Case Sentences

2021 ◽  
Author(s):  
Huihui Xu ◽  
Jaromir Savelka ◽  
Kevin D. Ashley
Keyword(s):  
Text Classification ◽  
Full Text ◽  
Domain Knowledge ◽  
Case Law ◽  
General Purpose ◽  
Position Information ◽  
Domain Specific ◽  
Sentence Position ◽  
Sentence Classification ◽  
Legal Domain

In this paper, we treat sentence annotation as a classification task. We employ sequence-to-sequence models to take sentence position information into account in identifying case law sentences as issues, conclusions, or reasons. We also compare the legal domain specific sentence embedding with other general purpose sentence embeddings to gauge the effect of legal domain knowledge, captured during pre-training, on text classification. We deployed the models on both summaries and full-text decisions. We found that the sentence position information is especially useful for full-text sentence classification. We also verified that legal domain specific sentence embeddings perform better, and that meta-sentence embedding can further enhance performance when sentence position information is included.

A Generic Framework for Defining Domain-Specific Models

2011 ◽  
pp. 23-38 ◽  
Author(s):  
Arnor Solberg ◽  
Jon Oldevik ◽  
Audun Jensvoll
Keyword(s):  
Domain Knowledge ◽  
System Development ◽  
Product Family ◽  
General Purpose ◽  
Domain Specific ◽  
Model Based ◽  
Development Methodology ◽  
Generic Framework

How do you tailor a general-purpose system development methodology to appropriately fit the specific needs of your company and the actual domain or product-family you are working with? Moreover, how do you alter a general-purpose methodology to utilize the domain knowledge possessed by your company? This chapter describes a generic framework for tailoring general-purpose, model-based methodologies in order to deliver domain-specific models.

scholarly journals Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

scholarly journals Evaluating Word Similarity Measure of Embeddings Through Binary Classification

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
A. Aziz Altowayan ◽  
Lixin Tao
Keyword(s):  
Similarity Measure ◽  
Binary Classification ◽  
General Purpose ◽  
Feature Representation ◽  
Entity Recognition ◽  
Language Models ◽  
Data Set ◽  
Word Similarity ◽  
Domain Specific ◽  
Retrieval Rate

We consider the following problem: given neural language models (embeddings) each of which is trained on an unknown data set, how can we determine which model would provide a better result when used for feature representation in a downstream task such as text classification or entity recognition? In this paper, we assess the word similarity measure through analyzing its impact on word embeddings learned from various datasets and how they perform in a simple classification task. Word representations were learned and assessed under the same conditions. For training word vectors, we used the implementation of Continuous Bag of Words described in [1]. To assess the quality of the vectors, we applied the analogy questions test for word similarity described in the same paper. Further, to measure the retrieval rate of an embedding model, we introduced a new metric (Average Retrieval Error) which measures the percentage of missing words in the model. We observe that scoring a high accuracy of syntactic and semantic similarities between word pairs is not an indicator of better classification results. This observation can be justified by the fact that a domain-specific corpus contributes to the performance better than a general-purpose corpus. For reproducibility, we release our experiments scripts and results.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

scholarly journals Domain-specific Evaluation Dataset Generator for Multilingual Text Analysis

2019 ◽  
pp. 140-147
Author(s):  
Emrah Inan ◽  
Vahab Mostafapour ◽  
Fatif Tekbacak
Keyword(s):  
Text Analysis ◽  
General Purpose ◽  
Entity Linking ◽  
Named Entity ◽  
Domain Specific ◽  
Benchmark Datasets ◽  
Concise Information ◽  
Multilingual Text ◽  
The Given ◽  
Specific Evaluation

Web enables to retrieve concise information about specific entities including people, organizations, movies and their features. Additionally, large amount of Web resources generally lies on a unstructured form and it tackles to find critical information for specific entities. Text analysis approaches such as Named Entity Recognizer and Entity Linking aim to identify entities and link them to relevant entities in the given knowledge base. To evaluate these approaches, there are a vast amount of general purpose benchmark datasets. However, it is difficult to evaluate domain-specific approaches due to lack of evaluation datasets for specific domains. This study presents WeDGeM that is a multilingual evaluation set generator for specific domains exploiting Wikipedia category pages and DBpedia hierarchy. Also, Wikipedia disambiguation pages are used to adjust the ambiguity level of the generated texts. Based on this generated test data, a use case for well-known Entity Linking systems supporting Turkish texts are evaluated in the movie domain.

scholarly journals Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

2018 ◽  
Vol 6 ◽  
pp. 269-285 ◽  
Author(s):  
Andrius Mudinas ◽  
Dell Zhang ◽  
Mark Levene
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Two Phase ◽  
Transductive Learning ◽  
Domain Specific ◽  
Sentiment Lexicon ◽  
Supervised Learning Algorithms

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.

scholarly journals Dependability Modeling and Assessment in UML-Based Software Development

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Simona Bernardi ◽  
José Merseguer ◽  
Dorina C. Petriu
Keyword(s):  
Software Development ◽  
Formal Model ◽  
General Purpose ◽  
Software Modeling ◽  
Model Driven ◽  
Domain Specific ◽  
Software Model ◽  
Software Models ◽  
Assessment Approach ◽  
System Properties

Assessment of software nonfunctional properties (NFP) is an important problem in software development. In the context of model-driven development, an emerging approach for the analysis of different NFPs consists of the following steps: (a) to extend the software models with annotations describing the NFP of interest; (b) to transform automatically the annotated software model to the formalism chosen for NFP analysis; (c) to analyze the formal model using existing solvers; (d) to assess the software based on the results and give feedback to designers. Such a modeling→analysis→assessment approach can be applied to any software modeling language, be it general purpose or domain specific. In this paper, we focus on UML-based development and on the dependability NFP, which encompasses reliability, availability, safety, integrity, and maintainability. The paper presents the profile used to extend UML with dependability information, the model transformation to generate a DSPN formal model, and the assessment of the system properties based on the DSPN results.

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