Articles

We present a system submitted in the Semantic Textual Similarity (STS) task at the Second Joint Conference on Lexical and Computa- tional Semantics (*SEM 2013). Given two short text fragments, the goal of the system is to determine their semantic similarity. Our sys- tem makes use of three different measures …...

This paper describes methods that were sub- mitted as part of the *SEM shared task on Semantic Textual Similarity. Multiple kernels provide different views of syntactic structure, from both tree and dependency parses. The kernels are then combined with simple lex- ical features using Gaussian process regres- sion, which is …...

We describe a number of techniques for auto- matically deriving lists of common and proper nouns, and show that the distinction between the two can be made automatically using a vector space model learning algorithm. We present a direct evaluation on the British Na- tional Corpus, and application based evalua- …...

The paper outlines the work carried out at NTNU as part of the *SEM’13 shared task on Semantic Textual Similarity, using an ap- proach which combines shallow textual, dis- tributional and knowledge-based features by a support vector regression model. Feature sets include (1) aggregated similarity based on named entity recognition …...

This paper presents the UNITOR system that participated in the *SEM 2013 shared task on Semantic Textual Similarity (STS). The task is modeled as a Support Vector (SV) regression problem, where a similarity scoring function between text pairs is acquired from examples. The proposed approach has been implemented in a …...

In this paper we describe KnCe2013-CORE, a system to compute the semantic similarity of two short text snippets. The system com- putes a number of features which are gath- ered from different knowledge bases, namely WordNet, Wikipedia and Wiktionary. The similarity scores derived from these features are then fed into …...

This paper describes the participation of iKer- nels system in the Semantic Textual Similar- ity (STS) shared task at *SEM 2013. Different from the majority of approaches, where a large number of pairwise similarity features are used to learn a regression model, our model directly encodes the input texts into …...

This paper describes our system submitted to *SEM 2013 Semantic Textual Similarity (STS) core task which aims to measure semantic si- milarity of two given text snippets. In this shared task, we propose an interpolation STS model named Model_LIM integrating Fra- meNet parsing information, which has a good performance with …...

In Semantic Textual Similarity (STS), sys- tems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 be- ing the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in …...

Wikipedia articles are annotated by volunteer contributors with numerous links that connect words and phrases to relevant titles. Links to general senses of a word are used concur- rently with links to more specific senses, with- out being distinguished explicitly. We present an approach to training coarse to fine grained …...

We combine logical and distributional rep- resentations of natural language meaning by transforming distributional similarity judg- ments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judg- ing sentence similarity and recognizing tex- tual entailment by appropriately adapting the MLN implementation of logical …...

The development of compositional distribu- tional models of semantics reconciling the em- pirical aspects of distributional semantics with the compositional aspects of formal seman- tics is a popular topic in the contemporary lit- erature. This paper seeks to bring this rec- onciliation one step further by showing how the mathematical …...

We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the correspond- ing three runs. All of them shared a word sim- ilarity feature that combined LSA word sim- ilarity and WordNet knowledge. The first, which achieved the best mean score …...

We present an approach which uses the sim- ilarity in semantic structure of bilingual par- allel sentences to bootstrap a pair of seman- tic role labeling (SRL) models. The setting is similar to co-training, except for the inter- mediate model required to convert the SRL structure between the two annotation …...

Implicit arguments are a discourse-level phe- nomenon that has not been extensively stud- ied in semantic processing. One reason for this lies in the scarce amount of annotated data sets available. We argue that more data of this kind would be helpful to improve exist- ing approaches to linking implicit …...

Social scientists are increasingly using the vast amount of text available on social me- dia to measure variation in happiness and other psychological states. Such studies count words deemed to be indicators of happiness and track how the word frequencies change across locations or time. This word count ap- proach …...

Short answer questions for reading compre- hension are a common task in foreign lan- guage learning. Automatic short answer scor- ing is the task of automatically assessing the semantic content of a student’s answer, mark- ing it e.g. as correct or incorrect. While pre- vious approaches mainly focused on compar- …...

Automatic metaphor identification and inter- pretation in text have been traditionally con- sidered as two separate tasks in natural lan- guage processing (NLP) and addressed in- dividually within computational frameworks. However, cognitive evidence suggests that hu- mans are likely to perform these two tasks si- multaneously, as part of a …...

This paper explores two hypotheses regarding vector space models that predict the compo- sitionality of German noun-noun compounds: (1) Against our intuition, we demonstrate that window-based rather than syntax-based distri- butional features perform better predictions, and that not adjectives or verbs but nouns rep- resent the most salient part-of-speech. Our …...

We propose an unsupervised method for au- tomatically calculating word usage similar- ity in social media data based on topic mod- elling, which we contrast with a baseline dis- tributional method and Weighted Textual Ma- trix Factorization. We evaluate these meth- ods against a novel dataset made up of human …...