"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition

Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media platforms: $Twitter$ and $Reddit$. Classification and frequency analysis show that for $Twitter$, typographic markers such as emoticons and emojis are the most discriminative markers to recognize ironic utterances, while for $Reddit$ the morphological markers (e.g., interjections, tag questions) are the most discriminative.Comment: ICWSM 201

Inducing Constraint-based Grammars using a Domain Ontology

This thesis presents a framework for domain specific text- to-knowledge acquisition, with focus on medical domain. The main challenge of this domain is the abundance of linguistic phenomena that require both syntactic and semantic information in order to “understand” the meaning of the text, and thus to acquire knowledge. Examples include prepositional phrases, coordinations, noun-noun compounds and nominalizations, phenomena which are not well covered by existing syntactic or semantic parsers

Parsing Preserving Techniques in Grammar Induction

In this paper we present the theoretical foundation of the search space for learning a class of constraint-based grammars, which preserve the parsing of representative examples. We prove that under several assumptions the search space is a complete grammar lattice, and the lattice top element is a grammar that can always be learned from a set of representative examples and a sublanguage used to reduce the grammar semantics. This complete grammar lattice guarantees convergence of solutions of any learning algorithm that obeys the given assumptions

A method for automatically building and evaluating dictionary resources

This paper describes a method toward automatically building dictionaries from text. We present DEFINDER, a rule-based system for extraction of definitions from on-line consumer-oriented medical articles. We provide an extensive evaluation on three dimensions: i) performance of the definition extraction technique in terms of precision and recall, ii) quality of the built dictionary as judged both by specialists and lay users, iii) coverage of existing on-line dictionaries. The corpus we used for the study is publicly available. A major contribution of the paper is the range of quantitative and qualitative evaluation methods

Evaluation of the DEFINDER System for Fully Automatic Glossary Construction

In this paper we present a quantitative and qualitative evaluation of DEFINDER, a rule-based system that mines consumer-oriented full text articles in order to extract definitions and the terms they define. The quantitative evaluation shows that in terms of precision and recall as measured against human performance, DEFINDER obtained 87% and 75% respectively, thereby revealing the incompleteness of existing resources and the ability of DEFINDER to address these gaps. Our basis for comparison is definitions from on-line dictionaries, including the UMLS Metathesaurus. Qualitative evaluation shows that the definitions extracted by our system are ranked higher in terms of user-centered criteria of usability and readability than are definitions from on-line specialized dictionaries. The output of DEFINDER can be used to enhance these dictionaries. DEFINDER output is being incorporated in a system to clarify technical terms for non-specialist users in understandable non-technical language

Evaluation of DEFINDER: A System to Mine Definitions from Consumer-oriented Medical Text

In this paper we present DEFINDER, a rule-based system that mines cons umer-oriented full text articles in order to extract definitions and the terms they define. This research is part of Digital Library Project at Columbia University, entitled PERSIVAL (PErsonalized Retrieval and Summarization of Image, Video and Language resources). One goal of the project is to present information to patients in language they can understand. A key component of this stage is to provide accurate and readable lay definitions for technical terms, which may be present in articles of intermediate complexity. The focus of this short paper is on quantitative and qualitative evaluation of the DEFINDER system. Our basis for comparison was definitions from Unified Medical Language System (UMLS), On-line Medical Dictionary (OMD) and Glossary of Popular and Technical Medical Terms (GPTMT). Quantitative evaluations show that DEFINDER obtained 87% precision and 75% recall and reveal the incompleteness of existing resources and the ability of DEFINDER to address gaps. Qualitative evaluation shows that the definitions extracted by our system are ranked higher in terms of user-based criteria of usability and readability than definitions from on-line specialized dictionaries. Thus the output of DEFINDER can be used to enhance existing specialized dictionaries, and also as a key feature in summarizing technical articles for non-specialist users