Сходство слов

We suggest a new goal and evaluation criterion for word similarity measures. The new criterion - meaning-entailing substitutability - fits the needs of semantic-oriented NLP applications and can be evaluated …...

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We ex- plore the use of Amazon’s Mechanical Turk system, a significantly cheaper …...

The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and para- phrases to word sense disambiguation …...

In this paper we explore the potential of quantum theory as a formal framework for capturing lexical meaning. We present a novel semantic space model that is syntacti- cally aware, …...

Semantic similarity is an essential com- ponent of many Natural Language Pro- cessing applications. However, prior meth- ods for computing semantic similarity of- ten operate at different levels, e.g., sin- …...

In this paper, we address the role of syntactic parsing for distributional similarity. On the one hand, we are exploring distributional similarities as an extrinsic test bed for unsupervised parsers. …...

We show that semantic relationships can be used to improve word alignment, in ad- dition to the lexical and syntactic features that are typically used. In this paper, we present …...

Many forms of word relatedness have been developed, providing different perspec- tives on word similarity. We introduce a Bayesian probabilistic tensor factoriza- tion model for synthesizing a single word vector …...

Vectorial representations of words derived from large current events datasets have been shown to perform well on word similarity tasks. This paper shows vectorial representa- tions derived from substantially smaller …...

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on En- glish. In this work, we innovatively …...

There are two main types of word repre- sentations: low-dimensional embeddings and high-dimensional distributional vec- tors, in which each dimension corresponds to a context word. In this paper, we ini- …...

In the last two years, there has been a surge of word embedding algorithms and research on them. However, evaluation has mostly been carried out on a narrow set of …...

We present CHARAGRAM embeddings, a sim- ple approach for learning character-based compositional models to embed textual se- quences. A word or sentence is represented using a character n-gram count vector, …...

We compare different word embeddings from a standard window based skipgram model, a skipgram model trained using dependency context features and a novel skipgram variant that utilizes additional information from …...

This paper describes our system developed for English Monolingual subtask (STS Core) of SemEval-2016 Task 1: “Semantic Textual Similarity: A Unified Framework for Seman- tic Processing and Evaluation”. We measure …...

Languages with rich inflectional morphol- ogy exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For in- stance, each count …...

Distributional semantic models can pre- dict many linguistic phenomena, including word similarity, lexical ambiguity, and se- mantic priming, or even to pass TOEFL synonymy and analogy tests (Landauer and Dumais, …...

Embedding words in a vector space has gained a lot of attention in recent years. While state- of-the-art methods provide efficient computa- tion of word similarities via a low-dimensional matrix …...

AbstractSememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people …...

AbstractIn this paper, we introduce WoRel, a model that jointly learns word embeddings and a semantic representation of word relations. The model learns from plain text sentences and their dependency …...

AbstractIn this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from …...

AbstractDistributed representations of words play a major role in the field of natural language processing by encoding semantic and syntactic information of words. However, most existing works on learning word …...

AbstractWe present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as …...

AbstractRecent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in …...

AbstractWord embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. …...

AbstractGraph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient …...

AbstractChinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings …...

AbstractWord similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a …...

AbstractIn this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of …...

AbstractWord embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to …...

AbstractWe look into the task of generalizing word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, …...

AbstractCross-lingual alignment of word embeddings are important in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches relying on learning structure-preserving transformations, using adversarial …...