Deep Parsing at the CLEF2014 IE Task
نویسندگان
چکیده
We present an information extraction system for patient records which has been submitted to the ShARe/CLEF eHealth Evaluation Lab 2014 Task 2. The task was information extraction from clinical text in terms of a disease/disorder template filling process. The system uses a lexicalized parser to annotate grammatical relations between diseases, disorders, and other constituents on a sentence level. Grammatical pattern matching rules are applied in order to annotate the specifics of individual disease/disorder cases. High accuracy is most important for clinical decision support; the comparative results suggest that a deep parsing approach is suitable for this task, as we achieved acc = 0.822 and acc = 0.804 for the two runs of the system.
منابع مشابه
An improved joint model: POS tagging and dependency parsing
Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of-Speech (POS) tagging is a prerequisite for dependency parsing. Generally, dependency parsers do the POS tagging task along with dependency parsing in a pipeline mode. Unfortunately, in pipel...
متن کاملThe CLaC Discourse Parser at CoNLL-2016
This paper describes our submission (CLaC) to the CoNLL-2016 shared task on shallow discourse parsing. We used two complementary approaches for the task. A standard machine learning approach for the parsing of explicit relations, and a deep learning approach for non-explicit relations. Overall, our parser achieves an F1score of 0.2106 on the identification of discourse relations (0.3110 for exp...
متن کاملTALP at GeoQuery 2007: Linguistic and Geographical Analysis for Query Parsing
This paper describes our experiments on the Geographical Query Parsing pilot-task for English at GeoCLEF 2007. Our system uses some modules of a Geographical Information Retrieval system presented at GeoCLEF 2006 [3] and modified for GeoCLEF 2007. The system uses deep linguistic analysis and Geographical Knowledge to perform the task.
متن کاملLearning Representations for Text-level Discourse Parsing
In the proposed doctoral work we will design an end-to-end approach for the challenging NLP task of text-level discourse parsing. Instead of depending on mostly hand-engineered sparse features and independent components for each subtask, we propose a unified approach completely based on deep learning architectures. To train more expressive representations that capture communicative functions an...
متن کاملتأثیر ساختواژهها در تجزیه وابستگی زبان فارسی
Data-driven systems can be adapted to different languages and domains easily. Using this trend in dependency parsing was lead to introduce data-driven approaches. Existence of appreciate corpora that contain sentences and theirs associated dependency trees are the only pre-requirement in data-driven approaches. Despite obtaining high accurate results for dependency parsing task in English langu...
متن کامل