Articles

The 2016 Clinical TempEval challenge ad- dresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the docu- ment creation times …...

In this paper, we describe the KULeuven- LIIR system at the Clinical TempEval 2016 Shared Task for the narrative container re- lation sub-task (CR). Our approach is based on the cTAKES Temporal system (Lin et al., 2015). We explored extending this system with different features. Moreover, we provide an error …...

We submitted two systems to the SemEval- 2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes and Phase 2, where we predicted a relation between an event and its parent doc- ument creation time. For temporal …...

We propose a deep neural network based nat- ural language processing system for clinical information (such as time information, event spans, and their attributes) extraction from raw clinical notes and pathology reports. Our ap- proach uses the context words and their part- of-speech tags and shape information as fea- tures. …...

The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all sub- tasks using machine learning approaches with ClearTK (LIBLINEAR), CRF++ and CRF- suite packages. Our experiments show that CRF-based classifiers yield, in general, higher recall …...

This paper describes VUACLTL, the system the CLTL Lab submitted to the SemEval 2016 Task Clinical TempEval. The system is based on a purely data-driven approach based on a cascade of seven CRF classifiers which use generic features and little domain knowledge. The challenge consisted in six subtasks related to …...

In this paper, we demonstrate our approach for identification of events, time expressions and temporal relations among them. This work was carried out as part of SemEval-2016 Challenge Task 12: Clinical TempEval. The task comprises six sub-tasks: identification of event spans, time spans and their attributes, document time relation and …...

This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of 2016 Clinical TempEval challenge. Clinical TempEval 2016 addressed the problem of temporal reasoning in clinical domain by providing annotated clinical notes and pathol- ogy reports similar to Clinical TempEval chal- …...

Our experiments rely on a combination of machine-learning (CRF) and rule-based (Hei- delTime) systems. First, a CRF system iden- tifies both EVENTS and TIMEX3, along with polarity values for EVENT and types of TIMEX. Second, the HeidelTime tool identi- fies DOCTIME and TIMEX3 elements, and computes DocTimeRel for each EVENT …...

This paper introduces our Chinese semantic dependency parsing system for Task 9 of Se- mEval 2016. Our system has two components: a parser trained using the Berkeley Grammar Trainer on the Penn Chinese Treebank rean- notated in a Generalized Categorial Grammar, and a multinomial logistic regression classi- fier. We first …...

This paper describes our approach to partici- pate on SemEval2016 Task12: Clinical Tem- pEval. Our system was based on IBEnt, a framework to identify chemical entities and their relations in text using machine learning techniques. This system has two modules, one to identify chemical entities, and other to identify the …...

Extraction and interpretation of temporal information from clinical text is essential for clinical practitioners and researchers. SemEval 2016 Task 12 (Clinical TempEval) addressed this challenge using the THYME1 corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of …...

Semantic dependency parsing aims at ex- tracting arcs and semantic role labels for all words in a sentence. In this paper, we propose a semantic dependency parser which is based on Long Short-term Mem- ory and makes heavy use of embeddings of words and POS tags. We describe in detail …...

This paper is a description of our system de- veloped for SemEval-2016 Task 9: Chinese Semantic Dependency Parsing. We have built a transition-based dependency parser with on- line reordering, which is not limited to a tree structure and can produce 99.7% of the neces- sary dependencies while maintaining linear algorithm …...

We present improvements to the JAMR parser as part of the SemEval 2016 Shared Task 8 on AMR parsing. The major contributions are: improved concept coverage using external re- sources and features, an improved aligner, and a novel loss function for structured prediction called infinite ramp, which is a generalization …...

We describe the system used in our participa- tion in the AMR Parsing task for SemEval- 2016. Our parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for estimating AMR graph structure. 1...

Meaning Representation using Learning to Search Sudha Rao1,3∗, Yogarshi Vyas1,3∗, Hal Daum´e III1,3, Philip Resnik2,3 1Computer Science, 2Linguistics, 3UMIACS University of Maryland {raosudha,yogarshi,hal}@cs.umd.edu, resnik@umd.edu Abstract In this paper we describe our approach to the Abstract Meaning Representation (AMR) parsing shared task as part of SemEval 2016. We develop a novel …...

In this paper, we apply a synchronous-graph- grammar-based approach to SemEval-2016 Task 8, Meaning Representation Parsing. In particular, we learn Synchronous Hyperedge Replacement Grammar (SHRG) rules from aligned pairs of sentences and AMR graphs. Then we use Earley algorithm with cube- pruning for AMR parsing given new sentences and the …...

We participated in the shared task on mean- ing representation parsing (Task 8 at SemEval- 2016) with the aim of investigating whether we could use Boxer, an existing open-domain semantic parser, for this task. However, the meaning representations produced by Boxer, Discourse Representation Structures, are con- siderably different from Abstract …...

We develop a novel transition-based parsing algorithm for the abstract meaning representa- tion parsing task using exact imitation learn- ing, in which the parser learns a statistical model by imitating the actions of an expert on the training data. We then use the imita- tion learning algorithm DAGGER to improve …...