Distributional Semantics

We propose an approach to adjective-noun composition (AN) for corpus-based distribu- tional semantics that, building on insights from theoretical linguistics, represents nouns as vectors and adjectives as data-induced (lin- ear) …...

We describe TINE, a new automatic evalua- tion metric for Machine Translation that aims at assessing segment-level adequacy. Lexical similarity and shallow-semantics are used as indicators of adequacy between machine …...

Adjectival modification, particularly by ex- pressions that have been treated as higher- order modifiers in the formal semantics tradi- tion, raises interesting challenges for semantic composition in distributional semantic mod- …...

The paper presents a wordnet expan- sion algorithm, which is based on lexico- semantic relations extracted from large text corpora. We do not assume that the extracted relation instances (i.e. …...

Coreference resolution systems rely heav- ily on string overlap (e.g., Google Inc. and Google), performing badly on mentions with very different words (opaque mentions) like Google and the search giant. …...

This paper explores to what extent lemmati- sation, lexical resources, distributional seman- tics and paraphrases can increase the accuracy of supervised models for dialogue manage- ment. The results suggest that …...

In this article, we describe a new approach to distributional semantics. This approach relies on a generative model of sentences with latent variables, which takes the syntax into account by …...

. In the course of the last two decades, numerous algorithms have sprouted up that successfully capture the semantics of single words by looking at their distribution in text, and …...

Context-predicting models (more com- monly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the litera- ture …...

AbstractAutomatic synonyms and semantically related word extraction is a challenging task, useful in many NLP applications such as question answering, search query expansion, text summarization, etc. While different studies addressed …...

Derivationally related lemmas like friendN – friendlyA – friendshipN are derived from a common stem. Frequently, their meanings are also systematically related. However, there are also many examples of derivationally …...

We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between …...

Users combine attributes and types to describe and classify entities into cate- gories. These categories are fundamen- tal for organising knowledge in a decen- tralised way acting as tags and …...

Givenness (Schwarzschild, 1999) is one of the central notions in the formal pragmatic literature discussing the organization of dis- course. In this paper, we explore where distributional semantics can help …...

AbstractThe aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that …...

Recent models in distributional seman- tics consider derivational patterns (e.g., use → use + f ul) as the result of a compositional process, where base term and affix are combined. …...

AbstractIn distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are …...

AbstractProduct classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification …...

In this paper we present a word decom- pounding method that is based on distribu- tional semantics. Our method does not re- quire any linguistic knowledge and is initial- ized …...

We introduce a system focused on solving SemEval 2016 Task 2 – Interpretable Seman- tic Textual Similarity. The system explores machine learning and rule-based approaches to the task. We focus …...

AbstractThis paper describes Luminoso’s participation in SemEval 2017 Task 2, “Multilingual and Cross-lingual Semantic Word Similarity”, with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that …...

We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora. These features are extracted …...

AbstractWe present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality. In contrast to most previous compositionality datasets we also consider syntactically complex constructions and publish …...

AbstractThe interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary …...

AbstractWe consider the problem of generating natural language given a communicative goal and a world description. We ask the question: is it possible to combine complementary meaning representations to scale …...

AbstractThe power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts. This idea is specifically invoked by noting that “you shall know …...