Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Semi-supervised prediction of protein interaction sentences exploiting semantically encoded metrics

Protein-protein interaction (PPI) identification is an integral component of many biomedical research and database curation tools. Automation of this task through classification is one of the key goals of text mining (TM). However, labelled PPI corpora required to train classifiers are generally small. In order to overcome this sparsity in the training data, we propose a novel method of integrating corpora that do not contain relevance judgements. Our approach uses a semantic language model to gather word similarity from a large unlabelled corpus. This additional information is integrated into the sentence classification process using kernel transformations and has a re-weighting effect on the training features that leads to an 8% improvement in F-score over the baseline results. Furthermore, we discover that some words which are generally considered indicative of interactions are actually neutralised by this process

Ranking deep web text collections for scalable information extraction

Information extraction (IE) systems discover structured in-formation from natural language text, to enable much richer querying and data mining than possible directly over the unstructured text. Unfortunately, IE is generally a com-putationally expensive process, and hence improving its ef-ficiency, so that it scales over large volumes of text, is of critical importance. State-of-the-art approaches for scaling the IE process focus on one text collection at a time. These approaches prioritize the extraction effort by learning key-word queries to identify the “useful ” documents for the IE task at hand, namely, those that lead to the extraction of structured “tuples. ” These approaches, however, do not at-tempt to predict which text collections are useful for the IE task—and hence merit further processing—and which ones will not contribute any useful output—and hence should be ignored altogether, for efficiency. In this paper, we focus on an especially valuable family of text sources, the so-called deep web collections, whose (remote) contents are only ac-cessible via querying. Specifically, we introduce and study techniques for ranking deep web collections for an IE task, to prioritize the extraction effort by focusing on collections with substantial numbers of useful documents for the task. We study both (adaptations of) state-of-the-art resource se-lection strategies for distributed information retrieval, and IE-specific approaches. Our extensive experimental eval-uation over realistic deep web collections, and for several different IE tasks, shows the merits and limitations of the alternative families of approaches, and provides a roadmap for addressing this critically important building block for efficient, scalable information extraction. 1

Chemoenzymatic microfluidic cascade reaction: Coupling of a diels-alder reaction with a transketolase-catalyzed reaction

A chemoenzymatic microfluidic cascade reaction is demonstrated for the first time, where a Diels-Alder reaction is followed by a transketolase reaction, for the synthesis of 3,4-dimethylcyclohex-3-ene-2’-keto-1’,3’- propanediols, which are used as scaffolds for a number of interesting pharmaceutical compounds. For an efficient organic synthesis, an enzymatic reaction would be advantageous, as it would minimize the number of process steps by eliminating the need for protective chemistry [1]. However, most catalysts and reactions conditions used with DA reactions are not compatible with a subsequent enzymatic reaction (issues revolve e.g. around solvent compatibility, differing reaction rates, and mis-match of pH). We used the spatial confinement of reactions afforded by cascaded microreactors, which has been well established for enzyme-enzyme reactions [2], to overcome these challenges and to achieve a chemoenzymatic reaction in continuous flow. Each reaction was optimized individually or in a step-wise synthesis, considering solvents and catalyst combinations, before being coupled in continuous flow

A realistic assessment of methods for extracting gene/protein interactions from free text

Background: The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger. Results: Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions. Conclusion: In terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community

Classification of protein interaction sentences via gaussian processes

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption