Part-of-speech (POS) tagging is a well-established technology for most Western European languages and a few other world languages, but it has not been evaluated on Igbo, an agglutinative African language. This article presents POS tagging experiments conducted using an Igbo corpus as a test bed for identifying the POS taggers and the Machine Learning (ML) methods that can achieve a good performance with the small dataset available for the language. Experiments have been conducted using different well-known POS taggers developed for English or European languages, and different training data styles and sizes. Igbo has a number of language-specific characteristics that present a challenge for effective POS tagging. One interesting case is the wide use of verbs (and nominalizations thereof) that have an inherent noun complement , which form “linked pairs” in the POS tagging scheme, but which may appear discontinuously. Another issue is Igbo’s highly productive agglutinative morphology, which can produce many variant word forms from a given root. This productivity is a key cause of the out-of-vocabulary (OOV) words observed during Igbo tagging. We report results of experiments on a promising direction for improving tagging performance on such morphologically-inflected OOV words.

Igbo, an African language with around 32 million speakers worldwide, is one of the many languages having few or none of the language processing resources needed for advanced language technology applications. In this article, we describe the approach taken to creating an initial set of resources for Igbo, including an electronic text corpus, a part-of-speech (POS) tagset, and a POS-tagged subcorpus. We discuss the approach taken in gathering texts, the preprocessing of these texts, and the development of the POS tagged corpus. We also discuss some of the problems encountered during corpus and tagset development and the solutions arrived at for these problems.

Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time – a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection – as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state-of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub-stories with high precision. This has resulted in an improvement of up to 60% in the F-score performance of HDP based sub-story detection approach compared to standard story detection approaches. A similar performance improvement is also seen using an information theoretic evaluation measure proposed for the sub-story detection task. Another contribution of this paper is in demonstrating that considering the conversational structures within the Twitter stream can bring up to 200% improvement in sub-story detection performance.

We present a supervised approach to automatically labelling topic clusters of reader comments to online news.We use a feature set that includes both features capturing properties local to the cluster and features that capture aspects from the news article and from comments outside the cluster.We evaluate the approach in an automatic and a manual, task-based setting.Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels.We illustrate how cluster labels can be used to generate cluster summaries and present two alternative summary formats: a pie chart summary and an abstractive summary.

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news.To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult.In this paper we propose one type of reader comment summary -the conversation overview summary -that aims to capture the key argumentative content of a reader comment conversation.We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpusthe first of its kind -of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.

Online commenting to news articles provides a communication channel between media professionals and readers offering a crucial tool for opinion exchange and freedom of expression.Currently, comments are detached from the news article and thus removed from the context that they were written for.In this work, we propose a method to connect readers' comments to the news article segments they refer to.We use similarity features to link comments to relevant article segments and evaluate both word-based and term-based vector spaces.Our results are comparable to state-of-theart topic modeling techniques when used for linking tasks.We demonstrate that article segments and comments representation are relevant to linking accuracy since we achieve better performances when similarity features are computed using similarity between terms rather than words.

BACKGROUND: The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. RESULTS: We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. CONCLUSION: We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types.

Most recent work on temporal relation extraction from text has addressed text drawn from the newswire domain and has attempted to extract all temporal relational information, as specified by proposed temporal annotation schemes such as TimeML. In this paper we explore the task of extracting restricted amounts of temporal information in support of an information extraction application in the medical domain, specifically that of extracting information about times of clinical investigations (X-rays, ultrasounds, etc.) from clinic letters. We describe the task, the corpus and evaluation data we have assembled, a baseline algorithm for extracting temporal relations between temporal expressions and clinical investigation events, and present evaluation results for the algorithm. Overall scores of precision 73.83% and recall 58.70% are promising for a simple baseline approach and suggest that extracting only a restricted subset of the temporal information available in a text may be a sensible way to proceed in the context of specific applications.

% '& ( *) ,+ , % -) ./ 0 21 43 # 5 5" # $ .6 & 7+ " 8 % :9 <; = !5 ?>A@ B -=0 *0 * ) C ED !F HG 'I KJ L L NM OJ P Q SR T 06 )7 ?0 @ 4 + A 3 B C 0 & 3 & D7 1 7 -E

We present a new chart parsing method for Lambek grammars, inspired by a method for D-Tree grammar parsing. The formulae of a Lambek sequent are firstly converted into rules of an indexed grammar formalism, which are used in an Earley-style predictive chart algorithm. The method is non-polynomial, but performs well for practical purposes---much better than previous chart methods for Lambek grammars.

Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. In this paper, we explore ways by which a treebank grammar can be reduced in size or ‘compacted’, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision.

Recent work has seen the emergence of a common framework for parsing categorial grammar (CG) formalisms that fall within the 'type-logical' tradition (such as the Lambek calculus and related systems), whereby some method of linear logic theorem proving is used in combination with a system of labelling that ensures only deductions appropriate to the relevant grammatical logic are allowed. The approaches realising this framework, however, have not so far addressed the task of incremental parsing --- a key issue in earlier work with 'flexible' categorial grammars. In this paper, the approach of (Hepple, 1996) is modified to yield a linear deduction system that does allow flexible deduction and hence incremental processing, but that hence also suffers the problem of 'spurious ambiguity'. This problem is avoided via normalisation.

Some problems are discussed that arise for incremental processing using certain flezible categorial grammars, which involve either undesirable parsing properties or failure to allow combinations useful to incrementality.We suggest a new calculus which, though 'designed' in relation to categorial interpretatious of some notions of dependency grammar, seems to provide a degree of flexibility that is highly appropriate for incremental interpretation.We demonstrate how this grammar may be used for efficient incremental parsing, by employing normalisation techniques.