Semantic Similarity

This paper presents a method to construct Japanese KATAKANA variant list from large corpus. Our method is useful for information retrieval, information extrac- tion, question answering, and so on, because …...

. In this paper, we test a working hypothesis that there is a significant relationship between semantic similarity and subcategorization frames in English. This paper is under the assumption that …...

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychol- ogy. Accurate measurement of semantic similarity between words …...

We propose a unified approach to web search query alterations in Japanese that is not lim- ited to particular character types or ortho- graphic similarity between a query and its …...

Automatic Term Recognition systems extract domain-specific terms from text corpora. Un- fortunately current systems fail to capture the whole of the domain covered by a corpus. To address this problem, …...

We propose to analyse semantic similar- ity in comparable text by matching syn- tactic trees and labeling the alignments according to one of five semantic simi- larity relations. We present …...

AbstractThis paper presents a new algorithm for automatic summarization of specialized texts combining terminological and semantic resources: a term extractor and an ontology. The term extractor provides the list of …...

This paper describes a feasibility study of n-gram-based evaluation metrics for automatic keyphrase extraction. To ac- count for near-misses currently ignored by standard evaluation metrics, we adapt various evaluation metrics …...

Current vector-space models of lexical seman- tics create a single “prototype” vector to rep- resent the meaning of a word. However, due to lexical ambiguity, encoding word mean- ing with …...

This paper applies sentence compression models for the task of query-focused multi-document summarization in order to investigate if sentence compression improves the overall summariza- tion performance. Both compression and summarization …...

This paper describes Stanford University’s submission to SemEval 2012 Semantic Tex- tual Similarity (STS) shared evaluation task. Our proposed metric computes probabilistic edit distance as predictions of semantic sim- ilarity. …...

We propose a new measure of semantic similarity between words in context, which exploits the syntactic/semantic structure of the context surrounding each target word. For a given pair of target …...

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- …...

We propose a method for acquiring plain lexical paraphrase using a Japanese dictionary in order to achieve lexical simplification for children. The proposed method extracts plain words that are the …...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted …...

Unsupervised selection of semantic relations for improving a distributional thesaurus Work about distributional thesauri has shown that the relations in these thesauri are mainly reliable for high frequency words. In …...

In this paper we describe the ASAP sys- tem (Automatic Semantic Alignment for Phrases)1 which participated on the Task 1 at the SemEval-2014 contest (Marelli et al., 2014a). Our assumption …...

The paper describes the approaches taken by the NTNU team to the SemEval 2014 Semantic Textual Similarity shared task. The solutions combine measures based on lexical soft cardinality and character …...

AbstractWe analyze in this paper a number of data sets proposed over the last decade or so for the task of paraphrase identification. The goal of the analysis is to …...

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Seman- tic Similarity in Twitter. Our paraphrase de- tection system makes use of phrase-structure parse tree embeddings that …...

We present ExB Themis – a word alignment- based semantic textual similarity system de- veloped for SemEval-2015 Task 2: Semantic Textual Similarity. It combines both string and semantic similarity measures …...

This paper describes the system developed by our team (HLTC-HKUST) for task 1 of Se- mEval 2015 workshop about paraphrase clas- sification and semantic similarity in Twitter. We trained a …...

Similarity between natural language texts, sentences in terms of meaning, known as tex- tual entailment, is a generic problem in the area of computational linguistics. In the last few years …...

This paper describes MITRE’s participation in the Paraphrase and Semantic Similar- ity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similar- ity and second in Paraphrase …...

In this paper, we describe the TATO sys- tem which participated in the SemEval-2015 Task 2a: “Semantic Textual Similarity (STS) for English”. Our system is trained on pub- lished datasets …...

AbstractEstimating similarities at different levels of linguistic units, such as words, sub-phrases and phrases, is helpful for measuring semantic similarity of an entire bilingual phrase. In this paper, we propose …...

This paper describes our system submission to the SemEval 2016 English Semantic Textual Similarity (STS) shared task. The proposed system is based on the compositional text sim- ilarity model, which …...

We present the system developed at FBK for the SemEval 2016 Shared Task 2 ”Inter- pretable Semantic Textual Similarity” as well as the results of the submitted runs. We use …...

This paper describes our participation in the SemEval-2016 Task 1: Semantic Textual Sim- ilarity (STS). We developed three methods for the English subtask (STS Core). The first method is unsupervised …...

AbstractWe describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, …...

This paper describes our IR (Information Re- trieval) based method for SemEval 2016 task 1, Semantic Textual Similarity (STS). The main feature of our approach is to extend a conventional …...

This paper describes our proposed solutions designed for a STS core track within the Se- mEval 2016 English Semantic Textual Sim- ilarity (STS) task. Our method of similar- ity detection …...

AbstractEmojis allow us to describe objects, situations and even feelings with small images, providing a visual and quick way to communicate. In this paper, we analyse emojis used in Twitter …...

AbstractThis article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a …...

AbstractThis paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, …...

AbstractThis paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use …...

AbstractShared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages. In our system submission, we employ multilingual word representations, …...

AbstractThis paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato …...

AbstractWith the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To …...

AbstractThis paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a …...

AbstractApplications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based …...

AbstractSemantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task …...

AbstractEvaluation discrepancy and overcorrection phenomenon are two common problems in neural machine translation (NMT). NMT models are generally trained with word-level learning objective, but evaluated by sentence-level metrics. Moreover, the …...

AbstractTo date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a …...

AbstractIn natural language processing, the performance of a semantic similarity task relies heavily on the availability of a large corpus. Various monolingual corpora are available (mainly English); but multilingual resources …...

The prevalence of the web has brought about the construction of many large-scale, automatically segmented and tagged corpora, which inevitably introduces errors due to automation and are likely to have …...

AbstractTraditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this …...

AbstractAdvanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks. However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. …...

AbstractThis paper presents the process of compiling a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. Word embeddings …...

AbstractLanguage Models have long been a prolific area of study in the field of Natural Language Processing (NLP). One of the newer kinds of language models, and some of the …...

AbstractUnsupervised commonsense question answering is appealing since it does not rely on any labeled task data. Among existing work, a popular solution is to use pre-trained language models to score …...

AbstractWe introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method …...

AbstractSocial networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any …...

AbstractPre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, retaining, and applying …...

AbstractFact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore …...

AbstractEvaluating the quality of generated text is difficult, since traditional NLG evaluation metrics, focusing more on surface form than meaning, often fail to assign appropriate scores. This is especially problematic …...

Automatic Minuting consists of automati- cally creating minutes from multiparty meet- ing transcripts. In this paper, we solve two rel- evant problems of this domain (1) given a pair of …...

AbstractDistance metric learning has become a popular solution for few-shot Named Entity Recognition (NER). The typical setup aims to learn a similarity metric for measuring the semantic similarity between test …...

AbstractKnowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector …...

AbstractApplying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance. However, recent work has argued that the gains produced …...

AbstractAs pre-trained models have shown successful performance in program language processing as well as natural language processing, adversarial attacks on these models also attract attention. However, previous works on black-box …...

AbstractRecent breakthroughs in NLP research, such as the advent of Transformer models have indisputably contributed to major advancements in several tasks. However, few works research robustness and explainability issues of …...

AbstractUnsupervised extractive summarization has recently gained importance since it does not require labeled data. Among unsupervised methods, graph-based approaches have achieved outstanding results. These methods represent each document by a …...

Semantic similarity systems assess to what ex- tent two words, phrases, sentences are simi- lar in their meaning. In this task, rule-based and neural network systems achieve the best results. …...

AbstractPretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the …...

AbstractMany text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous work has developed various metrics that often …...