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Eric Brill introduced transformation-based learning and showed that it can do part-of- speech tagging with fairly high accuracy. The same method can be applied at a higher level of textual …...

In this paper we discuss cascaded Memory- Based grammatical relations assignment. In the first stages of the cascade, we find chunks of sev- eral types (NP,VP,ADJP,ADVP,PP) and label them with …...

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this …...

Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (l~mshaw and Marcus, 1995) have introduced a "convenient" data rep- resentation for …...

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- …...

This paper describes an alternative trans- lation model based on a text chunk un- der the framework of statistical machine translation. The translation model sug- gested here first performs chunking. …...

Named Entity (NE) extraction is an important subtask of document processing such as in- formation extraction and question answering. A typical method used for NE extraction of Japanese texts is …...

. We present a method of chunking in Korean texts using conditional random fields (CRFs), a recently introduced probabilistic model for labeling and segmenting sequence of data. In agglutinative languages …...

We describe a word alignment platform which ensures text pre-processing (to- kenization, POS-tagging, lemmatization, chunking, sentence alignment) as re- quired by an accurate word alignment. The platform combines two different …...

We describe two methods to improve SMT accuracy using shallow syntax information. First, we use chunks to refine the set of word alignments typically used as a starting point in …...

. Alternating Structure Optimization (ASO) is a recently proposed linear Multi- task Learning algorithm. Although its effective has been verified in both semi-supervised as well as supervised methods, yet they …...

AbstractWe present a method for acquiring reliable predicate-argument structures from raw corpora for automatic compilation of case frames. Such lexicon compilation requires highly reliable predicate-argument structures to practically contribute to …...

AbstractIn this paper, we present the results of an experiment with utilizing a stochastic morphosyntactic tagger as a pre-processing module of a rule-based chunker and partial parser for Croatian in …...

AbstractWe present a partial dependency parser for Irish. Constraint Grammar (CG) based rules are used to annotate dependency relations and grammatical functions. Chunking is performed using a regular-expression grammar which …...

If we take an existing supervised NLP sys- tem, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown …...

. Analogy has been proposed as a possible principle for example-based machine translation. For such a framework to work properly, the training data should contain a large number of analogies …...

This paper shows that the performance of history-based models can be significantly im- proved by performing lookahead in the state space when making each classification deci- sion. Instead of simply …...

In this paper, we describe a novel approach to cascaded learning and inference on sequences. We propose a weakly joint learning model on cascaded inference on sequences, called multilayer sequence …...

AbstractThis article describes the collecting, processing and validation of a large balanced corpus for Romanian. The annotation types and structure of the corpus are briefly reviewed. It was constructed at …...

Studies of spontaneous conversational speech grounded on large and richly annotated cor- pora are still rare due to the scarcity of such resources. Comparative studies based on such resources are …...

We describe in this paper how different learning strategies can be applied on the same NLP task, namely chunking. The reference corpus is extracted from the French Treebank, the symbolic …...

We present an unsupervised learning model that induces phrasal inversion transduction grammars by introducing a minimum conditional description length (CDL) principle to drive search over a space defined by two …...

Symbolic and statistical learning for chunking : comparison and combinations We describe in this paper how to use grammatical inference algorithms for chunking, then compare and combine them to CRFs …...

In this paper we present our system de- veloped for the SemEval 2016 Task 2 - Interpretable Semantic Textual Similarity along with the results obtained for our sub- mitted runs. …...

AbstractWeighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, …...

AbstractWe propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to …...

AbstractPre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level …...

AbstractWe investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct …...

AbstractSpan identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of …...

AbstractContextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing …...

AbstractThe traditional evaluation of labeled spans with precision, recall, and F1-score has undesirable effects due to double penalties. Annotations with incorrect label or boundaries count as two errors instead of …...