Disorder named entity recognition (DNER) is a fundamental task of biomedical natural language processing, which has attracted plenty of attention. This task consists in extracting named entities of disorders such as diseases, symptoms, and pathological functions from unstructured text. The European Clinical Case Corpus (E3C) is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) of semantically annotated clinical case texts. The entities of type disorder in the clinical cases are annotated at both mention and concept level. At mention -level, the annotation identifies the entity text spans, for example, abdominal pain. At concept level, the entity text spans are associated with their concept identifiers in Unified Medical Language System, for example, C0000737. This corpus can be exploited as a benchmark for training and assessing information extraction systems. Within the context of the present work, multiple experiments have been conducted in order to test the appropriateness of the mention-level annotation of the E3C corpus for training DNER models. In these experiments, traditional machine learning models like conditional random fields and more recent multilingual pre-trained models based on deep learning were compared with standard baselines. With regard to the multilingual pre-trained models, they were fine-tuned (i) on each language of the corpus to test per-language performance, (ii) on all languages to test multilingual learning, and (iii) on all languages except the target language to test cross-lingual transfer learning. Results show the appropriateness of the E3C corpus for training a system capable of mining disorder entities from clinical case texts. Researchers can use these results as the baselines for this corpus to compare their own models. The implemented models have been made available through the European Language Grid platform for quick and easy access.

Comparable corpora serve as an important substitute for parallel resources in cases of under-resourced language pairs. Previous work mostly aims to find a better strategy to exploit existing comparable corpora, while ignoring the variety in corpus quality. The quality of comparable corpora affects a lot its usability in practice, a fact that has been justified by several studies. However, researchers have not been able to establish a widely accepted and fully validated framework to measure corpus quality. We will thus investigate in this paper a comprehensive methodology to deal with the quality of comparable corpora. To be exact, we will propose several comparability measures and a quantitative strategy to test those measures. Our experiments show that the proposed comparability measure can capture gold-standard comparability levels very well and is robust to the bilingual dictionary used. Moreover, we will show in the task of bilingual lexicon extraction that the proposed measure correlates well with the performance of the real world application.

Inside parsing is a best parse parsing method based on the Inside algorithm that is often used in estimating probabilistic parameters of stochastic context free grammars. It gives a best parse in O(N3G3) time where N is the input size and G is the grammar size. Earley algorithm can be made to return best parses with the same complexity in N.

By way of experiments, we show that Inside parsing can be more efficient than Earley parsing with sufficiently large grammar and sufficiently short input sentences. For instance, Inside parsing is better with sentences of 16 or less words for a grammar containing 429 states. In practice, parsing can be made efficient by employing the two methods selectively.

The redundancy of Inside algorithm can be reduced by the topdown filtering using the chart produced by Earley algorithm, which is useful in training the probabilistic parameters of a grammar. Extensive experiments on Penn Tree corpus show that the efficiency of Inside computation can be improved by up to 55%.

Convolutional sequence to sequence (CNN seq2seq) models have met success in abstractive summarization. However, their outputs often contain repetitive word sequences and logical inconsistencies, limiting the practicality of their application. In this paper, we find the reasons behind the repetition problem in CNN-based abstractive summarization through observing the attention map between the summaries with repetition and their corresponding source documents and mitigate the repetition problem. We propose to reduce the repetition in summaries by attention filter mechanism (ATTF) and sentence-level backtracking decoder (SBD), which dynamically redistributes attention over the input sequence as the output sentences are generated. The ATTF can record previously attended locations in the source document directly and prevent the decoder from attending to these locations. The SBD prevents the decoder from generating similar sentences more than once via backtracking at test. The proposed model outperforms the baselines in terms of ROUGE score, repeatedness, and readability. The results show that this approach generates high-quality summaries with minimal repetition and makes the reading experience better.

Natural Language Engineering really came about from a meeting between Roberto Garigliano (then of Durham University) and myself in his office in late 1992 or early 1993. I had returned to academia the previous year after a spell doing a variety of jobs in industry, and had become aware of Roberto and the Natural Language Group at Durham (just about 15 miles from the University of Sunderland where I was working). Roberto and I discussed several possible avenues of cooperation, including sponsorship by Durham of students on existing Sunderland masters degrees, a joint Durham/Sunderland specialist Masters in Language Engineering (which came to nothing) and a new journal focused on practical, engineering work in the language domain. Incidentally, one of the sponsored master’s students was Siobhan Devlin, now Head of Computing at Sunderland.

Determining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources.

Problems in the area of text and document processing can often be described as text rewriting tasks: given an input text, produce a new text by applying some fixed set of rewriting rules. In its simplest form, a rewriting rule is given by a pair of strings, representing a source string (the “original”) and its substitute. By a rewriting dictionary, we mean a finite list of such pairs; dictionary-based text rewriting means to replace in an input text occurrences of originals by their substitutes. We present an efficient method for constructing, given a rewriting dictionary D, a subsequential transducer that accepts any text t as input and outputs the intended rewriting result under the so-called “leftmost-longest match” replacement with skips, t'. The time needed to compute the transducer is linear in the size of the input dictionary. Given the transducer, any text t of length |t| is rewritten in a deterministic manner in time O(|t|+|t'|), where t' denotes the resulting output text. Hence the resulting rewriting mechanism is very efficient. As a second advantage, using standard tools, the transducer can be directly composed with other transducers to efficiently solve more complex rewriting tasks in a single processing step.

Extraction keyphrase systems traditionally use classification algorithms and do not consider the fact that part of the keyphrases may not be found in the text, reducing the accuracy of such algorithms a priori. In this work, we propose to improve the accuracy of these systems with inferential mechanisms that use a knowledge representation model, including symbolic models of knowledge bases and distributional semantics, to expand the set of keyphrase candidates to be submitted to the classification algorithm with terms that are not in the text (not-in-text terms). The basic assumption we have is that not-in-text terms have a semantic relationship with terms that are in the text. To represent this relationship, we have defined two new features to be represented as input to the classification algorithms. The first feature refers to the power of discrimination of the inferred not-in-text terms. The intuition behind this is that good candidates for a keyphrase are those that are deduced from various textual terms in a specific document and that are not often deduced in other documents. The other feature represents the descriptive strength of a not-in-text candidate. We argue that not-in-text keyphrases must have a strong semantic relationship with the text and that the power of this semantic relationship can be measured in a similar way as popular metrics like TFxIDF. The method proposed in this work was compared with state-of-the-art systems using five corpora and the results show that it has significantly improved automatic keyphrase extraction, dealing with the limitation of extracting keyphrases absent from the text.

This paper describes experiments in which I tried to distinguish between Flemish and Netherlandic Dutch subtitles, as originally proposed in the VarDial 2018 Dutch–Flemish Subtitle task. However, rather than using all data as a monolithic block, I divided them into two non-overlapping domains and then investigated how the relation between training and test domains influences the recognition quality. I show that the best estimate of the level of recognizability of the language varieties is derived when training on one domain and testing on another. Apart from the quantitative results, I also present a qualitative analysis, by investigating in detail the most distinguishing features in the various scenarios. Here too, it is with the out-of-domain recognition that some genuine differences between Flemish and Netherlandic Dutch can be found.

One of the key issues in spoken-language translation is how to deal with unrestricted expressions in spontaneous utterances. We have developed a paraphraser for use as part of a translation system, and in this paper we describe the implementation of a Chinese paraphraser for a Chinese-Japanese spoken-language translation system. When an input sentence cannot be translated by the transfer engine, the paraphraser automatically transforms the sentence into alternative expressions until one of these alternatives can be translated by the transfer engine. Two primary issues must be dealt with in paraphrasing: how to determine new expressions, and how to retain the meaning of the input sentence. We use a pattern-based approach in which the meaning is retained to the greatest possible extent without deep parsing. The paraphrase patterns are acquired from a paraphrase corpus and human experience. The paraphrase instances are automatically extracted and then generalized into paraphrase patterns. A total of 1719 paraphrase patterns obtained using this method and an implemented paraphraser were used in a paraphrasing experiment. The results showed that the implemented paraphraser generated 1.7 paraphrases on average for each test sentence and achieved an accuracy of 88%.

In this study, we investigate using unsupervised generative learning methods for subjectivity detection across different domains. We create an initial training set using simple lexicon information and then evaluate two iterative learning methods with a base naive Bayes classifier to learn from unannotated data. The first method is self-training, which adds instances with high confidence into the training set in each iteration. The second is a calibrated EM (expectation-maximization) method where we calibrate the posterior probabilities from EM such that the class distribution is similar to that in the real data. We evaluate both approaches on three different domains: movie data, news resource, and meeting dialogues, and we found that in some cases the unsupervised learning methods can achieve performance close to the fully supervised setup. We perform a thorough analysis to examine factors, such as self-labeling accuracy of the initial training set in unsupervised learning, the accuracy of the added examples in self-training, and the size of the initial training set in different methods. Our experiments and analysis show inherent differences across domains and impacting factors explaining the model behaviors.

This paper proposes a sequence-to-sequence model for data-to-text generation, called DM-NLG, to generate a natural language text from structured nonlinguistic input. Specifically, by adding a dynamic memory module to the attention-based sequence-to-sequence model, it can store the information that leads to generate previous output words and use it to generate the next word. In this way, the decoder part of the model is aware of all previous decisions, and as a result, the generation of duplicate words or incomplete semantic concepts is prevented. To improve the generated sentences quality by the DM-NLG decoder, a postprocessing step is performed using the pretrained language models. To prove the effectiveness of the DM-NLG model, we performed experiments on five different datasets and observed that our proposed model is able to reduce the slot error rate rate by 50% and improve the BLEU by 10%, compared to the state-of-the-art models.

Statistical Machine Translation (SMT) systems use various probabilistic and statistical Natural Language Processing (NLP) methods to automatically translate from one language to another language while retaining the originality of the context. This paper aims to discuss the development of bilingual SMT models for translating English into fifteen low-resource Indic languages (ILs) and vice versa. The process to build the SMT model is described and explained using a workflow diagram. Samanantar and OPUS corpus are utilized for training, and Flores200 corpus is used for fine-tuning and testing purposes. The paper also highlights various preprocessing methods used to deal with corpus noise. The Moses open-source SMT toolkit is being investigated for the system’s development. The impact of distance-based reordering and Morpho-syntactic Descriptor Bidirectional Finite-State Encoder (msd-bidirectional-fe) reordering on ILs is compared in the paper. This paper provides a comparison of SMT models with Neural Machine Translation (NMT) for ILs. All the experiments assess the translation quality using standard metrics such as BiLingual Evaluation Understudy, Rank-based Intuitive Bilingual Evaluation Score, Translation Edit Rate, and Metric for Evaluation of Translation with Explicit Ordering. From the result, it is observed that msd-bidirectional-fe reordering performs better than the distance-based reordering model for ILs. It is also noticed that even though the IL-English and English-IL systems are trained using the same corpus, the former performs better for all the evaluation metrics. The comparison between SMT and NMT shows that across various languages, SMT performs better in some cases, while NMT outperforms in others.