Articles

This paper describes a complete event/time ordering system that annotates raw text with events, times, and the ordering relations be- tween them at the SemEval-2013 Task 1. Task 1 is a unique challenge because it starts from raw text, rather than pre-annotated text with known events and times. A working …...

We analyze the performance of SUTIME, a temporal tagger for recognizing and normal- izing temporal expressions, on TempEval-3 Task A for English. SUTIME is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. Testing on the TempEval-3 eval- uation corpus showed …...

In this paper, we present the JUCSE system, designed for the TempEval-3 shared task. The system extracts events and temporal infor- mation from natural text in English. We have participated in all the tasks of TempEval-3, namely Task A, Task B & Task C. We have primarily utilized the Conditional …...

We describe FSS-TimEx, a module for the recognition and normalization of temporal ex- pressions we submitted to Task A and B of the TempEval-3 challenge. FSS-TimEx was developed as part of a multilingual event ex- traction system, Nexus, which runs on top of the EMM news processing engine. It consists …...

In this paper we present our system for the SemEval 2013 Task 5a on semantic similar- ity of words and compositional phrases. Our system uses a dependency-based vector space model, in combination with a technique called latent vector weighting. The system computes the similarity between a particular noun in- stance …...

This paper describes a temporal expression identification and normalization system, Man- TIME, developed for the TempEval-3 chal- lenge. The identification phase combines the use of conditional random fields along with a post-processing identification pipeline, whereas the normalization phase is carried out using NorMA, an open-source rule-based tem- poral normalizer. We …...

This paper describes the approach of the Hochschule Hannover to the SemEval 2013 Task Evaluating Phrasal Semantics. In or- der to compare a single word with a two word phrase we compute various distributional sim- ilarities, among which a new similarity mea- sure, based on Jensen-Shannon Divergence with a correction …...

This paper describes the SemEval-2013 Task 5: “Evaluating Phrasal Semantics”. Its first subtask is about computing the semantic simi- larity of words and compositional phrases of minimal length. The second one addresses deciding the compositionality of phrases in a given context. The paper discusses the impor- tance and background of …...

In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in av- erage. This system consists of a SVM clas- sifier with features extracted from texts (and their translations SMT) …...

This paper presents the second round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2013. The task was designed to pro- mote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Par- ticipants were presented with …...

In this paper we present the results of exper- iments comparing (a) rich syntactic and se- mantic feature sets and (b) big context win- dows, for the TempEval time expression and event segmentation and classification tasks. We show that it is possible for models using only lexical features to approach …...

In this paper, we describe our participation in the TempEval-3 challenge. With our multi- lingual temporal tagger HeidelTime, we ad- dressed task A, the extraction and normaliza- tion of temporal expressions for English and Spanish. Exploiting HeidelTime’s strict sep- aration between source code and language- dependent parts, we tuned HeidelTime’s …...

We describe a supervised approach to predict- ing the set of all inflected forms of a lexical item. Our system automatically acquires the orthographic transformation rules of morpho- logical paradigms from labeled examples, and then learns the contexts in which those trans- formations apply using a discriminative se- quence model. …...

We introduce a novel algorithm for generat- ing referring expressions, informed by human and computer vision and designed to refer to visible objects. Our method separates abso- lute properties like color from relative proper- ties like size to stochastically generate a di- verse set of outputs. Expressions generated using this …...

This paper presents G-FLOW, a novel system for coherent extractive multi-document sum- marization (MDS).1 Where previous work on MDS considered sentence selection and or- dering separately, G-FLOW introduces a joint model for selection and ordering that balances coherence and salience. G-FLOW’s core rep- resentation is a graph that approximates the …...

Twitter offers an unprecedented advantage on live reporting of the events happening around the world. However, summarizing the Twit- ter event has been a challenging task that was not fully explored in the past. In this paper, we propose a participant-based event summa- rization approach that “zooms-in” the Twit- ter …...

We propose a supervised lexical substitu- tion system that does not use separate clas- sifiers per word and is therefore applicable to any word in the vocabulary. Instead of learning word-specific substitution patterns, a global model for lexical substitution is trained on delexicalized (i.e., non lexical) features, which allows to …...

In this paper, we present a novel method for the computation of compositionality within a distri- butional framework. The key idea is that com- positionality is modeled as a multi-way interac- tion between latent factors, which are automat- ically constructed from corpus data. We use our method to model the …...

We present a morphology-aware nonparamet- ric Bayesian model of language whose prior distribution uses manually constructed finite- state transducers to capture the word forma- tion processes of particular languages. This relaxes the word independence assumption and enables sharing of statistical strength across, for example, stems or inflectional paradigms in different …...

In this paper we introduce the task of unla- beled, optimal, data set selection. Given a large pool of unlabeled examples, our goal is to select a small subset to label, which will yield a high performance supervised model over the entire data set. Our first proposed method, based on …...