Articles

CLaC Labs participated in two shared tasks for SemEval2015, Task 10 (subtasks B and E) and Task 11. The underlying system con- figuration is nearly identical and consists of two major components: a large Twitter lex- icon compiled from tweets that carry cer- tain selected hashtags (assumed to guaran- tee …...

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and …...

This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a process to ini- tialize the parameter weights of the convolu- tional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. …...

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analy- sis in Twitter. This was the most popular sen- timent analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competi- tion …...

Sentiment analysis tends to focus on the po- larity of words, combining their values to de- tect which portion of a text is opinionated. CLIPEval wants to promote a more holistic approach, looking at psychological researches that frame the connotations of words as the emotional values activated by them. The …...

We describe a system for semantic role label- ing adapted to a dependency parsing frame- work. Verb arguments are predicted over nodes in a dependency parse tree instead of nodes in a phrase-structure parse tree. Our system par- ticipated in SemEval-2015 shared Task 15, Subtask 1: CPA parsing and achieved …...

This paper describes the Duluth systems that participated in Task 15 of SemEval 2015. The goal of the task was to automatically construct dictionary entries (via a series of three sub- tasks). Our systems participated in subtask 2, which involved automatically clustering the contexts in which a target word occurs …...

This paper describes our participation in task 14 of SemEval 2015. This task focuses on the analysis of clinical texts and includes: (i) the recognition of the span of a disorder men- tion and (ii) its normalization to a unique concept identifier in the UMLS/SNOMED- CT terminology. We propose a …...

Natural language processing and text analy- sis methods offer the potential of uncovering hidden associations from large amounts of un- processed texts. The SemEval-2015 Analy- sis of Clinical Text task aimed at fostering re- search on the application of these methods in the clinical domain. The proposed task con- sisted …...

We implemented an end-to-end system for dis- order identification and slot filling. For iden- tifying spans for both disorders and their at- tributes, we used a linear chain conditional random field (CRF) approach coupled with cTAKES for pre-processing. For combining disjoint disorder spans, finding relations be- tween attributes and disorders, …...

This paper describes the approach used by ezDI at the SemEval 2015 Task-14: ”Anal- ysis of Clinical Text”. The task was di- vided into two embedded tasks. Task-1 re- quired determining disorder boundaries (in- cluding the discontiguous ones) from a given set of clinical notes and normalizing the dis- orders …...

This paper describes a system developed for the disorder identification subtask within task 14 of SemEval 2015. The developed sys- tem is based on a chain of two modules, one for recognition and another for normaliza- tion. The recognition module is based on an adapted version of the Stanford NER …...

We describe the performance of UtahPOET on SemEval 2015 Task 14. UtahPOET is a cognitively inspired system designed to ex- tract semantic content from general clinical texts. We find that our system performs much better on the context slot-filling aspects of Tasks 2A and 2B than the disorder CUI map- …...

Unstructured clinical notes are rich sources for valuable patient information. Information extraction techniques can be employed to extract this valuable information, which in turn can be used to discover new knowledge. Named entity recognition and normalization are the basic tasks involved in information extraction. In this paper, identification of disorder …...

Medical texts are filled with mentions of dis- eases, disorders, and other clinical conditions, with many different surface forms relating to the same condition. We describe MINERAL, a system for extraction and normalization of dis- ease mentions in clinical text, with which we participated in the Task 14 of SemEval …...

In this paper the system that was developed by Team UWM for the Task 14 of SemEval 2015 competition is described. Task 14 included two tasks: Task 1 was identification of disorder mentions and their normalization, and Task 2 was identification of the following attributes for disorder mentions: the CUI …...

This paper describes clinical disorder recognition and encoding system submit- ted by IHS R&D Belarus team at the SemEval-2015 shared task related to analysis of clinical texts. Our system is based on IHS Goldfire Linguistic Proces- sor and uses a rich set of lexical, syntac- tic and semantic features. The …...

In this paper we describe our entry to the Se- mEval 2015 clinical text analysis task. We participated only in the disorder attribute de- tection task 2a. Our main goal was to as- sess how well an information extraction sys- tem originally developed for a different task and domain can …...

This paper describes the participation of the UNIBA team in the Task 13 of SemEval-2015 about Multilingual All-Words Sense Disam- biguation and Entity Linking. We propose an algorithm able to disambiguate both word senses and named entities by combining the simple Lesk approach with information com- ing from both a …...

SUDOKU’s submissions to SemEval Task 13 treats Word Sense Disambiguation and Entity Linking as a deterministic problem that ex- ploits two key attributes of open-class words as constraints – their degree of polysemy and their part of speech. This is an extension and further validation of the results achieved by …...