Language tools that help people with their writing are now usually included in today's word processors. Although these various tools provide increasing support to native speakers of a language, they are much less useful to non-native speakers who are writing in their second language (e.g. French speakers writing in English). Real errors may go undetected and potential errors or non-errors that are flagged by the system may be taken to be genuine errors by the non-native speaker. In this paper, we present the prototype of an English writing tool which is aimed at helping speakers of French write in English. We first discuss the kind of problems non-native speakers have when writing in a second language. We then explain how we collected a corpus of errors which we used to build a typology of errors needed in the various stages of the project. This is followed by an overview of the prototype which contains a number of writing aids (dictionaries, on-line grammar helps, verb conjugator, etc.) and two checking tools: a problem word highlighter which lists all the potentially difficult words that cannot be dealt with correctly by the system (false friends, confusions, etc.) and a grammar checker which detects and corrects morphological and syntactic errors. We describe in detail the automata formalism we use to extract linguistic information, test syntactic environments and detect and correct errors. Finally, we present a first evaluation of the correction capacity of our grammar checker as compared to that of commercially available systems.

We compare the word sense disambiguation systems submitted for the English-all-words task in SENSEVAL-2. We give several performance measures for the systems, and analyze correlations between system performance and word features. A decision tree learning algorithm is employed to discover the situations in which systems perform particularly well, and the resulting decision tree is examined. We investigate using a decision tree based on the SENSEVAL systems to (i) filter out senses unlikely to be correct, and to (ii) combine WSD systems. Some combinations created in this way outperform the best SENSEVAL system.

Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open-text generation. However, controlling the generated text’s properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for topical language generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and TLG probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document’s natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.

The objective of this work is to set a corpus-driven methodology to quantify automatically diachronic language distance between chronological periods of several languages. We apply a perplexity-based measure to written text representing different historical periods of three languages: European English, European Portuguese, and European Spanish. For this purpose, we have built historical corpora for each period, which have been compiled from different open corpus sources containing texts as close as possible to its original spelling. The results of our experiments show that a diachronic language distance based on perplexity detects the linguistic evolution that had already been explained by the historians of the three languages. It is remarkable to underline that it is an unsupervised multilingual method which only needs a raw corpora organized by periods.

Most of the morphological properties of derivational Arabic words are encapsulated in their corresponding morphological patterns. The morphological pattern is a template that shows how the word should be decomposed into its constituent morphemes (prefix + stem + suffix), and at the same time, marks the positions of the radicals comprising the root of the word. The number of morphological patterns in Arabic is finite and is well below 1000. Due to these properties, most of the current analysis algorithms concentrate on discovering the morphological pattern of the input word as a major step in recognizing the type and category of the word. Unfortunately, this process is non-determinitic in the sense that the underlying search process may sometimes associate more than one morphological pattern with the given word, all of them satisfying the major lexical constraints. One solution to this problem is to use a collection of connectionist pattern associaters that uniquely associate each word with its corresponding morphological pattern. This paper describes an LVQ-based learning pattern association system that uniquely maps a given Arabic word to its corresponding morphological pattern, and therefore deduces its morphological properties. The system consists of a collection of hetroassociative models that are trained using the LVQ algorithm plus a collection of autoassociative models that have been trained using backpropagation. Experimental results have shown that the system is fairly accurate and very easy to train. The LVQ algorithm has been chosen because it is very easy to train and the implied training time is very small compared to that of backpropagation.

We investigate the use of instance-based ranking methods for surface realization in natural language generation. Our approach to instance-based natural language generation (IBNLG) employs two components: a rule system that ‘overgenerates’ a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to examples taken from a training corpus. We develop an efficient search technique for identifying the optimal candidate based on a novel extension of the A* algorithm. The rule system is produced automatically from a semantically annotated fragment of the Penn Treebank II containing management succession texts. We detail the annotation scheme and grammar induction algorithm and evaluate the efficiency and output of the generator. We also discuss issues such as input coverage (completeness) and fluency that are relevant to surface generation in general.

We describe a system that automatically learns effective and engaging dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feedback. Besides the more usual clarification and verification components of dialogue, this library contains various social elements like greetings, apologies, small talk, relational questions and jokes. We tested the method through an experimental dialogue system that encourages take-up of exercise and shows that the learned dialogue policy performs as well as one built by human experts for this system.

Statistical, linguistic, and heuristic clues can be used for the alignment of words and multi-word units in parallel texts. This article describes the clue alignment approach and the optimization of its parameters using a genetic algorithm. Word alignment clues can come from various sources such as statistical alignment models, co-occurrence tests, string similarity scores and static dictionaries. A genetic algorithm implementing an evolutionary procedure can be used to optimize the parameters necessary for combining available clues. Experiments on English/Swedish bitext show a significant improvement of about 6% in F-scores compared to the baseline produced by statistical word alignment.Most of the work described in this paper was carried out at the Department of Linguistics and Philology at Uppsala University. I would like to acknowledge technical and scientific support by people at the department in Uppsala.

Comparable corpora are key translation resources for both languages and domains with limited linguistic resources. The existing approaches for building comparable corpora are mostly based on ranking candidate documents in the target language for each source document using a cross-lingual retrieval model. These approaches also exploit other evidence of document similarity, such as proper names and publication dates, to build more reliable alignments. However, the importance of each evidence in the scores of candidate target documents is determined heuristically. In this paper, we employ a learning to rank method for ranking candidate target documents with respect to each source document. The ranking model is constructed by defining each evidence for similarity of bilingual documents as a feature whose weight is learned automatically. Learning feature weights can significantly improve the quality of alignments, because the reliability of features depends on the characteristics of both source and target languages of a comparable corpus. We also propose a method to generate appropriate training data for the task of building comparable corpora. We employed the proposed learning-based approach to build a multi-domain English–Persian comparable corpus which covers twelve different domains obtained from Open Directory Project. Experimental results show that the created alignments have high degrees of comparability. Comparison with existing approaches for building comparable corpora shows that our learning-based approach improves both quality and coverage of alignments.

Charles Wayne restarted funding in speech and language in the mid-1980s after a funding winter brought on by Pierce’s glamour-and-deceit criticisms in the ALPAC report and ‘Whither Speech Recognition’. Wayne introduced a new glamour-and-deceit-proof idea, an emphasis on evaluation. No other sort of program could have been funded at the time, at least in America. One could argue that Wayne has been so successful that the program no longer needs him to continue on. These days, shared tasks and leaderboards have become common place in speech and language (and vision and machine learning) research. That said, I am concerned that the community may not appreciate what it has got until it’s gone. Wayne has been doing much more than merely running competitions, but he did what he did in such a subtle Columbo-like way. Going forward, government funding is being eclipsed by consumer markets. Those of us with research to sell need to find more and more ways to be relevant to potential sponsors given this new world order.

This paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.

This paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine.

Compound noun segmentation is one of the crucial problems in Korean language processing because a series of nouns in Korean may appear without space in real text, which makes it difficult to identify its morphological constituents. This paper presents an effective method of Korean compound noun segmentation based on lexical data extracted from a corpus. The segmentation consists of two tasks: First, it uses a Hand-Build Segmentation Dictionary (HBSD) to segment compound nouns which frequently occur or need an exceptional process. Second, a segmentation algorithm using data from a corpus is proposed, where simple nouns and their frequencies are stored in a Simple Noun Dictionary (SND) for segmentation. The analysis is executed based on modified tabular parsing using min-max operation. Our experiments have shown a very effective accuracy rate of about 97.29%, which turns out to be very effective.

This paper presents two novel ideas of improving the Machine Translation (MT) quality by applying the word-level quality prediction for the second pass of decoding. In this manner, the word scores estimated by word confidence estimation systems help to reconsider the MT hypotheses for selecting a better candidate rather than accepting the current sub-optimal one. In the first attempt, the selection scope is limited to the MT N-best list, in which our proposed re-ranking features are combined with those of the decoder for re-scoring. Then, the search space is enlarged over the entire search graph, storing many more hypotheses generated during the first pass of decoding. Over all paths containing words of the N-best list, we propose an algorithm to strengthen or weaken them depending on the estimated word quality. In both methods, the highest score candidate after the search becomes the official translation. The results obtained show that both approaches advance the MT quality over the one-pass baseline, and the search graph re-decoding achieves more gains (in BLEU score) than N-best List Re-ranking method.

Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module that combines them under the constraints imposed by the analyzer. We explore several approaches to classifier combination, as well as a back-off mechanism that relies on a large unannotated corpus. Our best result, around 83 percent accuracy, compares favorably with the state of the art on this task.

ANVIL is an information retrieval system using natural language processing techniques, intended for retrieval of captioned images. It extracts dependency structures from the image captions and user queries, and then applies a high accuracy matching algorithm which recursively explores the dependency structures to determine their similarity. A further algorithm allows additional contextual information to be extracted following a successful match, with the intention of helping users understand and organise the retrieval results. ANVIL was developed to high engineering standards, and as well as looking at the research aspects of the system, we also look at some of the design and development issues. English and Japanese versions of the system have been developed.

The paper describes SENSE, a word sense disambiguation system which makes use of multidimensional analogy-based proportions to infer the most likely sense of a word given its context. Architecture and functioning of the system are illustrated in detail. Results of different experimental settings are given, showing that the system, in spite its conservative bias, successfully copes with the problem of training data sparseness.

The noun compound – a sequence of nouns which functions as a single noun – is very common in English texts. No language processing system should ignore expressions like steel soup pot cover if it wants to be serious about such high-end applications of computational linguistics as question answering, information extraction, text summarization, machine translation – the list goes on. Processing noun compounds, however, is far from trouble-free. For one thing, they can be bracketed in various ways: is it steel soup, steel pot, or steel cover? Then there are relations inside a compound, annoyingly not signalled by any words: does potcontainsoup or is it for cookingsoup? These and many other research challenges are the subject of this special issue.

Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.