Training Corpus

The described tagger is b,'used on a hidden Markov model and uses tags composed of features such as part- of speech, gender, etc. 'l?he contextual probability of a tag (state …...

Lexical attributes, like syntactic (part-of-speech) and semantic (semantic category) attributes, are in most cases, ambiguous in every languages. Automatic resolution of ambiguity of these attributes can be achieved using different …...

Over the past few years, HNC has developed a neural network based, vector space approach to text retrieval. This approach, embodied in a system called MatchPlus, allows the user to …...

Recognizing shallow linguistic patterns, such as ba- sic syntactic relationships between words, is a com~ mon task in applied natural language and text pro- (:essing. Tile common practice for approaching …...

Extractive summarization techniques cannot generate document summaries shorter than a single sentence, some- thing that is often required. An ideal summarization system would under- stand each document and generate an …...

An approach to automatic detection of syllable boundaries is presented. We demonstrate the use of several manu- ally constructed grammars trained with a novel algorithm combining the advan- tages of …...

AbstractWe describe the automatic resolution of pronominal anaphora using KANT Controlled English (KCE) and the KANTOO English-to-Spanish MT system. Our algorithm is based on a robust, syntax-based approach that applies …...

AbstractThe reliable detection of sentence boundaries in running text is one of the first important steps in preparing an input document for translation. Although this is often neglected, it is …...

Language model based IR system proposed in recent 5 years has introduced the language model approach in the speech recognition area into the IR community and improves the performance of …...

In Statistics-Based Summarization - Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (K&M) present a noisy-channel model for sen- tence compression. The main difficulty in using this …...

This paper describes a system for the CoNLL-2005 Shared Task on Semantic Role Labeling. We trained two parsers with the training corpus in which the se- mantic argument information is …...

We present a study on how grammar binariza- tion empirically affects the efficiency of the CKY parsing. We argue that binarizations af- fect parsing efficiency primarily by affecting the number …...

We present a Hebrew to English transliter- ation method in the context of a machine translation system. Our method uses ma- chine learning to determine which terms are to be …...

In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Ma- chine Translation (SMT). SAMT …...

This paper introduces a new statistical pre- processing model for Rule-Based Machine Translation (RBMT) systems. We train a Statistical Machine Translation (SMT) system using monolingual corpora. This model can transform …...

. In this paper, we present Thai named entity recognition (NER) systems using supervised Conditional Random Fields (CRFs) with various answer patterns to find out whether different answer patterns would …...

AbstractVector space models benefit from using an outside corpus to train the model. It is, however, unclear what constitutes a good training corpus. We have investigated the effect on summary …...

AbstractWe adopt the corpus-informed approach to example sentence selections for the construction of a reference grammar. In the process, a database containing sentences that are carefully selected by linguistic experts …...

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. …...

This paper reports an approach to automatical- ly generate a lexical resource to support in- cremental semantic role labeling annotation in Portuguese. The data come from the corpus Propbank-Br (Propbank …...

AbstractAs data-driven approaches started to make their way into the Natural Language Generation (NLG) domain, the need for automation of corpus building and extension became apparent. Corpus creation and extension …...

AbstractThis paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort …...

Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the is- …...

Several studies on sentence processing sug- gest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing …...

AbstractNeural Machine Translation (NMT) has achieved promising results comparable with Phrase-Based Statistical Machine Translation (PBSMT). However, to train a neural translation engine, much more powerful machines are required than those …...

AbstractWord embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if …...

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains …...

A distributional semantics model — also known as word embeddings — is a major asset for any language as the research results reported in the literature have consistently shown that …...

AbstractThe goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined …...

AbstractRecurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words. We investigate to …...

AbstractCurrent state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. …...

AbstractMining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language …...

AbstractA sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the …...

AbstractBuilding on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a …...

AbstractMany automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts of biomedical …...

AbstractWe present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons …...

AbstractThis paper presents the system developed by the Sartipi-Sedighin team for SemEval 2023 Task 2, which is a shared task focused on multilingual complex named entity recognition (NER), or MultiCoNER …...