This paper describes the XLING system participation in SemEval-2013 Crosslingual Word Sense Disambiguation task. The XLING system introduces a novel approach to skip the sense disambiguation step by matching query sentences to sentences in a parallel corpus using topic models; it returns the word alignments as the translation for the target polysemous words. Although, the topic-model base match...

In order to automatically identify noun synonyms, we propose a new idea which opposes classical polysemous representations of words to monosemous representations based on the “one sense per discourse” hypothesis. For that purpose, we apply the attributional similarity paradigm on two levels: corpus and document. We evaluate our methodology on well-known standard multiple choice synonymy questio...

This paper presents an approach that extends the standard approach used for bilingual lexicon extraction from comparable corpora. We focus on the unresolved problem of polysemous words revealed by the bilingual dictionary and introduce a use of a Word Sense Disambiguation process that aims at improving the adequacy of context vectors. On two specialized FrenchEnglish comparable corpora, empiric...

Recently, interpretable vector spaces have been introduced, vector spaces in which individual dimensions are meaningful. For polysemous words, interpretable vectors mix features from different senses. In this paper, we propose the task of computing contextspecific interpretable representations for individual senses of a word. Focusing on computing features that describe circumstances of an even...

Online shoppers depend on customer reviews when evaluating products or services. However, in the international online marketplace, reviews in a user’s language may not be available. Translation of online customer reviews is therefore an important service. A crucial aspect of this task is translating opinion words, key words that capture the reviewers’ sentiments. This is challenging because opi...

Abstract Experiences of racial discrimination can seem to be caused by one’s race, a combination social identities, or non-social features. In other words, intersectional attributionally ambiguous. This poses challenges for current understandings and measurement tools in public health research, such as the explanation disparities. Different kinds discriminatory experiences plausibly produce dif...

Linguists often represent the relationships between words in a collection of text as an undirected graph G = (V,E), were V is the vocabulary and vertices are adjacent in G if and only if the words they represent co-occur in a relevant pattern in the text. Ideally, the words with similar meanings give rise to the vertices of a component of the graph. However, many words have several distinct mea...

Many NLP applications require disambiguating polysemous words. Existing methods that learn polysemous word vector representations involve first detecting various senses and optimizing the sensespecific embeddings separately, which are invariably more involved than single sense learning methods such as word2vec. Evaluating these methods is also problematic, as rigorous quantitative evaluations i...

We present a pilot study of word-sense annotation using multiple annotators, relatively polysemous words, and a heterogenous corpus. Annotators selected senses for words in context, using an annotation interface that presented WordNet senses. Interannotator agreement (IA) results show that annotators agree well or not, depending primarily on the individual words and their general usage properti...

Most words in natural language are polysemous, that is, they can be used in more than one way. For example, paper can be used to refer to a substance made out of wood pulp or to a daily publication printed on that substance. Although virtually every sentence contains polysemy, there is little agreement as to how polysemy is represented in the mental lexicon. Do different uses of polysemous word...