Articles

Recently, there has been renewed interest in semantic dependency parsing, among which one of the paradigms focuses on parsing di- rected acyclic graphs (DAGs). Considera- tion of the decoding problem in natural lan- guage semantic dependency parsing as find- ing a maximum spanning DAG of a weighted directed graph carries …...

Semantic role labeling has become a key mod- ule for many language processing applica- tions such as question answering, information extraction, sentiment analysis, and machine translation. To build an unrestricted semantic role labeler, the first step is to develop a com- prehensive proposition bank. However, creat- ing such a bank …...

Hypernymy relation acquisition has been widely investigated, especially because tax- onomies, which often constitute the backbone structure of semantic resources are structured using this type of relations. Although lots of approaches have been dedicated to this task, most of them analyze only the written text. However relations between not necessar- …...

With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in …...

We describe a topic model based approach for selectional preference. Using the topic fea- tures generated by an LDA model on the ex- tracted predicate-arguments over the Chinese Gigaword corpus, we show improvement to our state-of-the-art Chinese SRL system by 2.34 F1 points on arguments of nominal pred- icates, 0.40 …...

This paper reports an approach to automatical- ly generate a lexical resource to support in- cremental semantic role labeling annotation in Portuguese. The data come from the corpus Propbank-Br (Propbank of Brazilian Portu- guese) and from the lexical resource of Eng- lish Propbank, as both share the same structure. In …...

The automatic induction of scripts (Schank and Abelson, 1977) has been the focus of many recent works. In this paper, we employ a variety of these methods to learn Schank and Abelson’s canonical restaurant script, us- ing a novel dataset of restaurant narratives we have compiled from a website called …...

An English entity linking (EL) workflow is presented, which combines the annotations of five public open source EL services. The an- notations are combined through a weighted voting scheme inspired by the ROVER meth- od, which had not been previously tested on EL outputs. The combined results improved over each …...

Complex interactions among the meanings of words are important factors in the function that maps word meanings to phrase meanings. Recently, compositional distributional seman- tics models (CDSM) have been designed with the goal of emulating these complex interac- tions; however, experimental results on the ef- fectiveness of CDSM have been …...

A major problem in research on Textual Entail- ment (TE) is the high implementation effort for TE systems. Recently, interoperable standards for annotation and preprocessing have been proposed. In contrast, the algorithmic level remains unstandardized, which makes compo- nent re-use in this area very difficult in prac- tice. In this …...

The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically clas- sifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexi- cal relation holding between them. Even when …...

The increasing amount of online content mo- tivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The pro- posed methods are based on the hierarchical combination of single-document summaries, and achieves state of the art results. 1...

We investigate from the competence stand- point two recent models of lexical semantics, algebraic conceptual representations and con- tinuous vector models. Characterizing what it means for a speaker to be competent in lexical semantics remains perhaps the most significant stumbling block in reconciling the two main threads of semantics, Chomsky’s …...

Based on the hypothesis that frame-semantic parsing and event extraction are structurally identical tasks, we retrain SEMAFOR, a state- of-the-art frame-semantic parsing system to predict event triggers and arguments. We de- scribe how we change SEMAFOR to be better suited for the new task and show that it per- forms …...

The Practical Lexical Function model (PLF) is a recently proposed compositional distribu- tional semantic model which provides an el- egant account of composition, striking a bal- ance between expressiveness and robustness and performing at the state-of-the-art. In this paper, we identify an inconsistency in PLF be- tween the objective function …...

We induce semantic association networks from translation relations in parallel corpora. The resulting semantic spaces are encoded in a single reference language, which ensures cross-language comparability. As our main contribution, we cluster the obtained (cross- lingually comparable) lexical semantic spaces. We find that, in our sample of languages, lexical semantic …...

In this paper, we propose the use of word sense disambiguation and latent semantic features to automatically identify a person’s perspective from his/her written text. We run an Ama- zon Mechanical Turk experiment where we ask Turkers to answer a set of constrained and open-ended political questions drawn from the …...

Keyphrase extraction is a fundamental technique in natural language processing. It enables documents to be mapped to a concise set of phrases that can be used for indexing, clustering, ontology building, auto-tagging and other information organization schemes. Two major families of unsupervised keyphrase extraction algorithms may be characterized as statistical …...

This paper addresses the question of how doc- ument classifiers can exploit implicit infor- mation about document similarity to improve document classifier accuracy. We infer doc- ument similarity using simple n-gram over- lap, and demonstrate that this improves over- all document classification performance over two datasets. As part of this, …...

Named entity disambiguation is the task of linking entity mentions to their intended refer- ent, as represented in a Knowledge Base, usu- ally derived from Wikipedia. In this paper, we combine local mention context and global hy- perlink structure from Wikipedia in a proba- bilistic framework. Our results show that …...