Shift-reduce parsing has been studied extensively for diverse grammars due to the simplicity and running efficiency. However, in the field of constituency parsing, shift-reduce parsers lag behind state-of-the-art parsers. In this paper we propose a semi-supervised approach for advancing shift-reduce constituency parsing. First, we apply the uptraining approach (Petrov, S. et al. 2010. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), Cambridge, MA, USA, pp. 705–713) to improve part-of-speech taggers to provide better part-of-speech tags to subsequent shift-reduce parsers. Second, we enhance shift-reduce parsing models with novel features that are defined on lexical dependency information. Both stages depend on the use of large-scale unlabeled data. Experimental results show that the approach achieves overall improvements of 1.5 percent and 2.1 percent on English and Chinese data respectively. Moreover, the final parsing accuracies reach 90.9 percent and 82.2 percent respectively, which are comparable with the accuracy of state-of-the-art parsers.

In Modern Standard Arabic texts are typically written without diacritical markings. The diacritics are important to clarify the sense and meaning of words. Lack of these markings may lead to ambiguity even for the natives. Often the natives successfully disambiguate the meaning through the context; however, many Arabic applications, such as machine translation, text-to-speech, and information retrieval, are vulnerable due to lack of diacritics. The process of automatically restoring diacritical marks is called diacritization or diacritic restoration. In this paper we discuss the properties of the Arabic language and the issues that are related to the lack of the diacritical marking. It will be followed by a survey of the recent algorithms that were developed to solve the diacritization problem. We also look into the future trend for researchers working in this area.

Arabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners’ errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate.

In this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

Events play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we propose event graphs as a novel way of structuring event-based information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing research, which has mainly focused on individual event extraction tasks, we describe a complete end-to-end system for event graph extraction from text. Our system is a three-stage pipeline that performs anchor extraction, argument extraction, and relation extraction (temporal relation extraction and event coreference resolution), each at a performance level comparable with the state of the art. We present EvExtra, a large newspaper corpus annotated with event mentions and event graphs, on which we train and evaluate our models. To measure the overall quality of the constructed event graphs, we propose two metrics based on the tensor product between automatically and manually constructed graphs. Finally, we evaluate the overall quality of event graphs with the proposed evaluation metrics and perform a headroom analysis of the system.

In almost every science fiction movie you’ll see people conversing with machines. Of course, the rise of intelligent personal assistants means you probably do this yourself already. This posting asks: what’s the difference? Also, recent news on Facebook acquisitions, spoken language translation, and sentiment analysis.

We describe an opinion mining system which classifies the polarity of Spanish texts. We propose an NLP approach that undertakes pre-processing, tokenisation and POS tagging of texts to then obtain the syntactic structure of sentences by means of a dependency parser. This structure is then used to address three of the most significant linguistic constructions for the purpose in question: intensification, subordinate adversative clauses and negation. We also propose a semi-automatic domain adaptation method to improve the accuracy of our system in specific application domains, by enriching semantic dictionaries using machine learning methods in order to adapt the semantic orientation of their words to a particular field. Experimental results are promising in both general and specific domains.

With NLP services now widely available via cloud APIs, tasks like named entity recognition and sentiment analysis are virtually commodities. We look at what's on offer, and make some suggestions for how to get rich.

In this paper, we propose an unsupervised and automated method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books and millions of tweets posted per day. We construct distributional-thesauri-based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we propose a split/join based approach to compare the sense clusters at two different time points to find if there is ‘birth’ of a new sense. The approach also helps us to find if an older sense was ‘split’ into more than one sense or a newer sense has been formed from the ‘join’ of older senses or a particular sense has undergone ‘death’. We use this completely unsupervised approach (a) within the Google books data to identify word sense differences within a media, and (b) across Google books and Twitter data to identify differences in word sense distribution across different media. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet.

The above obituary has been updated to include the name of the editor as the author. The publishers would like to apologise for this error.

Ruslan Mitkov, Executive Editor

Prof. Geoffrey Leech, an influential scholar who left an indelible mark across many linguistic disciplines and inspired a generation of linguists, died suddenly on 19 August 2014.