Cosine Similarity

Several classification and routing methods were im- plemented and compared. The experiments used FBIS documents from four categories, and the measures used were the ff.idf and Cosine similarity measures, and …...

We are presenting a working system for automated news analysis that ingests an average total of 7600 news articles per day in five languages. For each language, the system detects …...

State-of-the-art story link detection systems, that is, systems that determine whether two sto- ries are about the same event or linked, are usu- ally based on the cosine-similarity measured between …...

New Event Detection (NED) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. We compare two stories by finding three cosine simi- larities based …...

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of …...

We present two systems that pick the ten most appropriate substitutes for a marked word in a test sentence. The first system scores candidates based on how frequently their local …...

Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the ex- isting approaches, the cosine measure of the term vectors representing the …...

To date, few attempts have been made to develop and validate methods for au- tomatic evaluation of linguistic quality in text summarization. We present the first systematic assessment of several …...

AbstractGraph-based similarity over WordNet has been previously shown to perform very well on word similarity. This paper presents a study of the performance of such a graph-based algorithm when using …...

This paper presents a simple and effi- cient algorithm for approximate dictio- nary matching designed for similarity measures such as cosine, Dice, Jaccard, and overlap coefficients. We propose this algorithm, …...

We describe an approach for generating a ranked list of candidate document transla- tion pairs without the use of bilingual dic- tionary or machine translation system. We developed this approach …...

We present larger-scale evidence overturning pre- vious results, showing that among the many al- ternative phrasal lexical similarity measures based on word vectors, the Jaccard coefficient most in- creases the …...

Noticing that different information sources of- ten provide complementary coverage of word sense and meaning, we propose a simple and yet effective strategy for measuring lexical se- mantics. Our model …...

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 …...

Existing vector space models typically map synonyms and antonyms to similar word vec- tors, and thus fail to represent antonymy. We introduce a new vector space representation where antonyms lie …...

In this paper, we describe the system architec- ture used in the Semantic Textual Similarity (STS) task 6 pilot challenge. The goal of this challenge is to accurately identify five …...

This paper describes the system used by the LIPN team in the Semantic Textual Similarity task at *SEM 2013. It uses a support vector re- gression model, combining different text …...

This paper describes the XLING system partici- pation in SemEval-2013 Crosslingual Word Sense Disambiguation task. The XLING system introduces a novel approach to skip the sense disambiguation step by matching …...

This paper describes an approach to im- plementing a tool for evaluating seman- tic similarity. We investigated the poten- tial benefits of (1) using text summarisa- tion to narrow down …...

The most widely used similarity measure in the field of natural language processing may be co- sine similarity. However, in the context of Twitter, the large scale of massive tweet …...

This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The sys- tem should read a given question and identify good, potentially …...

In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval- 2015 Task …...

AbstractWe propose a novel neural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent …...

We present a matrix factorization model for learning cross-lingual representations for sentences. Using sentence-aligned corpora, the proposed model learns distributed repre- sentations by factoring the given data into language-dependent factors …...

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 …...

We discuss an approximate similarity search for word embeddings, which is an operation to approximately find em- beddings close to a given vector. We compared several metric-based search al- gorithms …...

AbstractSolving word analogies became one of the most popular benchmarks for word embeddings on the assumption that linear relations between word pairs (such as king:man :: woman:queen) are indicative of …...

AbstractWord embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for …...

AbstractIn this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory …...

AbstractFigurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits …...

AbstractA common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have …...

We started to construct a database of synonymous expressions of Japanese “Verb + Verb” compounds semi-automatically. Japanese is known to be rich in compound verbs consisting of two verbs joined …...

AbstractThe task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) …...

AbstractThis paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing human-elicited semantic property dataset with gold …...

AbstractMachine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this …...

AbstractIn multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response …...

AbstractBERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that …...

AbstractThe prevalent way to estimate the similarity of two documents based on word embeddings is to apply the cosine similarity measure to the two centroids obtained from the embedding vectors …...

AbstractMining parallel sentences from comparable corpora is important. Most previous work relies on supervised systems, which are trained on parallel data, thus their applicability is problematic in low-resource scenarios. Recent …...

AbstractThis paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is …...

AbstractBilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not …...

AbstractWe present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has …...

AbstractWord embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some …...

AbstractThis paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch. In a first step, lists of potential …...

AbstractWe apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic …...

AbstractOne key principle for assessing textual similarity is measuring the degree of semantic overlap between texts by considering the word alignment. Such alignment-based approaches are both intuitive and interpretable; however, …...

AbstractAlthough exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there …...

AbstractSimilarity measures are a vital tool for understanding how language models represent and process language. Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used …...

AbstractThe task of natural language inference (NLI), to decide if a hypothesis entails or contradicts a premise, received considerable attention in recent years. All competitive systems build on top of …...

AbstractThis work is about finding the similarity between a pair of news articles. There are seven different objective similarity metrics provided in the dataset for each pair and the news …...

AbstractWe present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each …...