Articles

We present a comparison of different selec- tional preference models and evaluate them on an automatic verb classification task in German. We find that all the models we compare are effective for verb clustering; the best-performing model uses syntactic information to induce nouns classes from unlabelled data in an unsupervised …...

This paper introduces a model of multiple- instance learning applied to the predic- tion of aspect ratings or judgments of specific properties of an item from user- contributed texts such as product reviews. Each variable-length text is represented by several independent feature vectors; one word vector per sentence or paragraph. …...

Common-sense reasoning is important for AI applications, both in NLP and many vision and robotics tasks. We propose NaturalLI: a Natural Logic inference sys- tem for inferring common sense facts – for instance, that cats have tails or tomatoes are round – from a very large database of known facts. …...

Various studies highlighted that topic- based approaches give a powerful spo- ken content representation of documents. Nonetheless, these documents may con- tain more than one main theme, and their automatic transcription inevitably contains errors. In this study, we propose an orig- inal and promising framework based on a compact representation …...

We present an empirical study on the use of semantic information for Concept Seg- mentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative analyses out- put by a state-of-the-art CSL parser with tree structures, which we rerank with a classifier trained on two …...

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of shortest-path inference, on the as- sumption that the SRL model is restricted to arc-factored features of …...

Meaning Representation Graphs Nima Pourdamghani, Yang Gao, Ulf Hermjakob, Kevin Knight Information Sciences Institute Department of Computer Science University of Southern California {damghani,yanggao,ulf,knight}@isi.edu Abstract We align pairs of English sentences and corresponding Abstract Meaning Repre- sentations (AMR), at the token level. Such alignments will be useful for downstream extraction of …...

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across do- mains. Even in domain, errors are often made where syntactic information does not provide sufficient cues. In this pa- per, we mitigate …...

Language is given meaning through its correspondence with a world representa- tion. This correspondence can be at mul- tiple levels of granularity or resolutions. In this paper, we introduce an approach to multi-resolution language grounding in the extremely challenging domain of pro- fessional soccer commentaries. We define and optimize a …...

Much attention has been given to the impact of informativeness and similar- ity measures on distributional thesauri. We investigate the effects of context fil- ters on thesaurus quality and propose the use of cooccurrence frequency as a sim- ple and inexpensive criterion. For eval- uation, we measure thesaurus agreement with …...

This paper reports on the development of a hy- brid and simple method based on a machine learning classifier (Naive Bayes), Word Sense Disambiguation and rules, for the automatic assignment of WordNet Domains to nominal entries of a lexicographic dictionary, the Senso Comune De Mauro Lexicon. The system ob- tained …...

Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are still very incomplete, ne- cessitating the use of inference to fill in gaps. Prior work has shown how to make …...

Word-sense recognition and disambigua- tion (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well under- stood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with …...

The current approaches to Semantic Role Labeling (SRL) usually perform role clas- sification for each predicate separately and the interaction among individual predi- cate’s role labeling is ignored if there is more than one predicate in a sentence. In this paper, we prove that different predi- cates in a sentence …...

Text-based document geolocation is com- monly rooted in language-based infor- mation retrieval techniques over geodesic grids. These methods ignore the natural hierarchy of cells in such grids and fall afoul of independence assumptions. We demonstrate the effectiveness of using lo- gistic regression models on a hierarchy of nodes in the …...

The informal nature of social media text renders it very difficult to be automati- cally processed by natural language pro- cessing tools. Text normalization, which corresponds to restoring the non-standard words to their canonical forms, provides a solution to this challenge. We introduce an unsupervised text normalization approach that utilizes …...

We introduce the task of incremental se- mantic role labeling (iSRL), in which se- mantic roles are assigned to incomplete input (sentence prefixes). iSRL is the semantic equivalent of incremental pars- ing, and is useful for language model- ing, sentence completion, machine trans- lation, and psycholinguistic modeling. We propose an …...

PCFGs with latent annotations have been shown to be a very effective model for phrase structure parsing. We present a Bayesian model and algorithms based on a Gibbs sam- pler for parsing with a grammar with latent an- notations. For PCFG-LA, we present an ad- ditional Gibbs sampler algorithm to …...

Most existing systems for subcategoriza- tion frame (SCF) acquisition rely on su- pervised parsing and infer SCF distribu- tions at type, rather than instance level. These systems suffer from poor portability across domains and their benefit for NLP tasks that involve sentence-level process- ing is limited. We propose a new …...

Concept Embeddings from Multi-Modal Data: Since You Probably Can’t See What I Mean Felix Hill Computer Laboratory University of Cambridge felix.hill@cl.cam.ac.uk Anna Korhonen Computer Laboratory University of Cambridge anna.korhonen@cl.cam.ac.uk Abstract Models that acquire semantic represen- tations from both linguistic and percep- tual input are of interest to researchers in NLP …...