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Many of the kinds of language model used in speech understanding suffer from imper- fect modeling of intra-sentential contextual influences. I argue that this problem can be addressed by clustering …...

In this paper, we propose a statistical approach for clustering of artMes us- ing on-line dictionary definitions. One of the characteristics of our approach is that every sense of word …...

In this paper, we study the problem of "learning new compositions of words from given ones with a specific syntactic struc- ture, e.g., A-N or V-N structures. We first cluster …...

We present a corpus-based study of the se- quential ordering among premodifiers in noun phrases. This information is important for the fluency of generated text in practical appli- cations. We …...

Language models for speech recognition con- centrate solely on recognizing the words that were spoken. In this paper, we advocate re- defining the speech recognition problem so that its goal …...

In this paper are described experiments on un- supervised learning of the domain lexicon and relevant phrase fragments from a dialog cor- pus. Suggested approach is based on using do- …...

Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked lists are combined together by various means. The motivation is that different IR systems …...

We describe an approach to improve the bilingual cooccurrence dictionary that is used for word alignment, and evaluate the improved dictionary using a version of the Competitive Linking algorithm. We …...

Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated cor- pus data. We describe a new approach which involves clustering subcategoriza- tion frame (SCF) …...

We cluster verbs into lexical semantic classes, using a general set of noisy features that cap- ture syntactic and semantic properties of the verbs. The feature set was previously shown …...

The work presented in this paper is the first step in a project which aims to cluster and sum- marise electronic discussions in the context of help-desk applications. The eventual …...

Clustering is an optimization procedure that partitions a set of elements to optimize some criteria, based on a fixed distance metric de- fined between the elements. Clustering ap- proaches have …...

This paper presents an approach for Mul- tilingual Document Clustering in compa- rable corpora. The algorithm is of heuris- tic nature and it uses as unique evidence for clustering the …...

In this paper a novel solution to auto- matic and unsupervised word sense induc- tion (WSI) is introduced. It represents an instantiation of the ‘one sense per colloca- tion’ observation …...

. 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 …...

Clustering is crucial for many NLP tasks and applications. However, evaluating the results of a clustering algorithm is hard. In this paper we focus on the evaluation setting in which …...

Example-Based Machine Translation (EBMT), like other corpus based methods, requires substantial parallel training data. One way to reduce data requirements and improve translation quality is to generalize parts of the …...

AbstractIn this paper we present a fairy tale corpus that was semantically organized and tagged. The proposed method uses latent semantic mapping to represent the stories and a top-n item-to-item …...

AbstractThe identification of rare and novel senses is a challenge in lexicography. In this paper, we present a new method for finding such senses using a word aligned multilingual parallel …...

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 …...

The problem of language model adaptation in statistical machine translation is consid- ered. A mixture of language models is employed, which is obtained by clustering the bilingual training data. Unsupervised …...

In this paper we propose a completely un- supervised method for open-domain en- tity extraction and clustering over query logs. The underlying hypothesis is that classes defined by mining search …...

This paper describes how to cluster to- gether the phrases of a phrase-based sta- tistical machine translation (SMT) sys- tem, using information in the phrase table itself. The clustering is …...

Clustering is a central technique in NLP. Consequently, clustering evaluation is of great importance. Many clustering algo- rithms are evaluated by their success in tagging corpus tokens. In this paper …...

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candi- date slot fillers to identify meaningful tem- plate slots. We …...

The paper presents results of clustering terms extracted from economic articles in Polish Wikipedia. First, we describe the method of automatic term extraction supported by linguistic knowledge. Then, we define …...

AbstractThe European Commission's (EC) Directorate General for Translation, together with the EC's Joint Research Centre, is making available a large translation memory (TM; i.e. sentences and their professionally produced translations) …...

We propose a novel probabilistic technique for modeling and extracting salient struc- ture from large document collections. As in clustering and topic modeling, our goal is to provide an organizing …...

AbstractOperational intelligence applications in specific domains are developed using numerous natural language processing technologies and tools. A challenge for this integration is to take into account the limitations of each …...

_______________________________________________________ Speech Prosody of Dialectal French: Data and Hypotheses This paper contrasts the prosody of 6 varieties of French spoken in three different areas: France (Paris and Lyon), Belgium (Tournai …...

Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In …...

To solve data sparsity problem, recently there has been a trend in discriminative methods of NLP to use representations of lexical items learned from unlabeled data as features. In this …...

Unsupervised CRF for knowledge discovery Knowledge discovery aims at bringing out coherent groups of entities. They are usually based on clustering ; the challenge is then to define a notion …...

Despite considerable research on the topic of Arabic Named Entity Recognition (NER), almost all efforts focus on a traditional set of semantic classes, features and token representations. In this work, …...

With the rapid development of web-based services, concerns about user privacy have height- ened. The privacy policies of online websites, which serve as a legal agreement between service providers and …...

AbstractThis paper presents a system for suggesting a ranked list of appropriate vacancy descriptions to job seekers in a job board web site. In particular our work has explored the …...

Keyphrases have found wide ranging application in NLP and IR tasks such as document sum- marization, indexing, labeling, clustering and classification. In this paper we pose the problem of extracting …...

AbstractIdentifying and linking named entities across information sources is the basis of knowledge acquisition and at the heart of Web search, recommendations, and analytics. An important problem in this context …...

Analyzing arguments in user-generated Web discourse has recently gained atten- tion in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learn- ing, are usually domain …...

In recent years, theoretical and computa- tional linguistics has paid much attention to linguistic items that form scales. In NLP, much research has focused on order- ing adjectives by intensity …...

AbstractScripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a …...

AbstractDetecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have …...

AbstractRich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. …...

We propose a pipeline through which to derive clusters of dialects, given a mixed corpus composed of different dialects, when their standard counterpart is sufficiently resourced. The test case is …...

Low-resource languages often suffer from a lack of high-coverage lexical resources. In this paper, we propose a method to generate cognate tables by clustering words from existing lexical resources. We …...

We present the crowdsourcing platform Donnez Votre Français à la Science (DFS, or “Give your French to Science”), which aims to collect linguistic data and document language use, with a …...

Corpora of manuscripts of the same ancient text often preserve many variants. This is so because upon copying over long copy chains errors and editorial changes have been repeatedly made …...

AbstractTask extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks …...

AbstractLiterary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains …...

AbstractProducing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam …...

AbstractIn this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper’s contributions include a method for handling any sized input and …...

The present paper explored the focusing topics change and language change in Report on the Work of the Gov- ernment by Premiers of the People’s Republic of China (hereinafter Report …...

AbstractAutomatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language …...

Argumentation is an important tool within hu- man interaction, not only in law and politics but also for discussing issues, expressing and exchanging opinions and coming to decisions in our …...

AbstractDue to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to …...

AbstractSentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various …...

AbstractLanguages are dynamic systems: word usage may change over time, reflecting various societal factors. However, all languages do not evolve identically: the impact of an event, the influence of a …...

AbstractRecent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new …...

AbstractIn real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as …...

AbstractSimilar judgments search is an important task in legal practice, from which valuable legal insights can be obtained. Issues are disputes between both parties in civil litigation, which represents the …...

Abstractk-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT). It aims to alleviate the performance degradation of advanced MT …...

AbstractMultilingual Neural Machine Translation has been showing great success using transformer models. Deploying these models is challenging because they usually require large vocabulary (vocab) sizes for various languages. This limits …...

AbstractWe apply computational stylometric techniques to an 18th century Dutch chronicle to determine which fragments of the manuscript represent the author’s own original work and which show signs of external …...

AbstractIntent Detection is a crucial component of Dialogue Systems wherein the objective is to classify a user utterance into one of multiple pre-defined intents. A pre-requisite for developing an effective …...

AbstractRecent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated …...

AbstractNew intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service …...

AbstractThis paper proposes a novel approach to French patent classification leveraging data-centric strategies. We compare different approaches for the two deepest levels of the IPC hierarchy: the IPC group and …...

AbstractUnsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. …...

AbstractText embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on …...

AbstractThis work presents an unsupervised method of selecting filters and threshold values for the OpusFilter parallel corpus cleaning toolbox. The method clusters sentence pairs into noisy and clean categories and …...