Automatically Identifying Key Sentences in Biomedical Abstracts Using Semi-Supervised Learning
ثبت نشده
چکیده
منابع مشابه
Using MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles
Biomedical abbreviations and acronyms are widely used in biomedical literature. Since many of them represent important content in biomedical literature, information retrieval and extraction benefits from identifying the meanings of those terms. On the other hand, many abbreviations and acronyms are ambiguous, it would be important to map them to their full forms, which ultimately represent the ...
متن کاملمقایسه روشهای مختلف یادگیری ماشین در خلاصهسازی استخراجی گفتار به گفتار فارسی بدون استفاده از رونوشت
In this paper, extractive speech summarization using different machine learning algorithms was investigated. The task of Speech summarization deals with extracting important and salient segments from speech in order to access, search, extract and browse speech files easier and in a less costly manner. In this paper, a new method for speech summarization without using automatic speech recognitio...
متن کاملA semi-supervised learning framework for biomedical event extraction based on hidden topics
OBJECTIVES Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based syst...
متن کاملSemi-supervised Semantic Role Labeling via Graph-Alignment
Semantic roles, which constitute a shallow form of meaning representation, have attracted increasing interest in recent years. Various applications have been shown to benefit from this level of semantic analysis, and a large number of publications has addressed the problem of semantic role labeling, i.e., the task of automatically identifying semantic roles in arbitrary sentences. A major limit...
متن کاملAutomatic Annotation Techniques for Supervised and Semi-supervised Query-focused Summarization
In this paper, we study one semi-supervised and several supervised methods for extractive query-focused multi-document summarization. Traditional approaches to multidocument summarization are either unsupervised or supervised. The unsupervised approaches use heuristic rules to select the most important sentences, which are hard to generalize. On the other hand, huge amount of annotated data is ...
متن کامل