Unsupervised Approach

. This paper presents an unsupervised approach for dialogue act (DA) classification. We used a latent variable model to compress the dimensions of the feature vector. We introduced a paraphraser …...

We propose a novel unsupervised approach for distinguishing literal and non-literal use of idiomatic expressions. Our model com- bines an unsupervised and a supervised classifier. The former bases its decision …...

We present an unsupervised approach to re- constructing ancient word forms. The present work addresses three limitations of previous work. First, previous work focused on faith- fulness features, which model …...

We propose an unsupervised approach uti- lizing only raw corpora to enhance mor- phological alignment involving highly in- flected languages. Our method focuses on closed-class morphemes, modeling their influence on …...

We describe a new unsupervised approach for synonymy discovery by aligning para- phrases in monolingual domain corpora. For that purpose, we identify phrasal terms that convey most of the concepts …...

In this paper, we propose an unsupervised approach to automatically synthesize Wikipedia articles in multiple languages. Taking an existing high-quality version of any entry as content guideline, we extract keywords …...

We report a wide range of comparative experiments establishing for the first time contrastive foundations for a completely unsupervised approach to bilingual grammar induction that is cognitively oriented toward early …...

This paper describes the participation of the SINAI research group in the 2013 edi- tion of the International Workshop Se- mEval. The SINAI research group has submitted two systems, which …...

Creating a language-independent meaning representation would benefit many cross- lingual NLP tasks. We introduce the first un- supervised approach to this problem, learn- ing clusters of semantically equivalent English and …...

We propose a simple unsupervised ap- proach to detecting non-compositional components in multiword expressions based on Wiktionary. The approach makes use of the definitions, synonyms and trans- lations in Wiktionary, …...

We describe UMBC’s systems developed for the SemEval 2014 tasks on Multi- lingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual …...

We propose an unsupervised approach to constructing templates from a large collec- tion of semantic category names, and use the templates as the semantic representa- tion of categories. The main …...

Inference about whether a word in one text has similar meaning to another word in the other text is an essential task in order to understand whether two texts have …...

Sentiment analysis has become a well- established task in Natural Language Pro- cessing. As such, a high variety of methods have been proposed to tackle it, for different types of …...

AbstractThis paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our …...

AbstractMost existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such …...

AbstractWord sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it …...

AbstractPhonological processes are context-dependent sound changes in natural languages. We present an unsupervised approach to learning human-readable descriptions of phonological processes from collections of related utterances. Our approach builds upon …...

AbstractSingle document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an …...

AbstractText classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of …...

AbstractTraditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader’s interest and text …...

AbstractWe study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with …...

AbstractApproaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the …...

AbstractRecent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used …...

. This paper presents an unsupervised approach for natural language generation within the framework of question-answering systems. This approach addresses the issue of generating answers that are usually too short …...

AbstractMulti-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work …...

AbstractIn SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised …...