Lana Yeganova
National Institutes of Health, National Library of Medicine, Department Member
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and... more
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.
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Analysis of the projection algorithm. The file provides the proof of convergence, and identifies a convenient stopping criterion for the projection algorithm. (CVS 149 kb)
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Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total... more
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.
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The need to organize a large collection in a manner that facilitates human comprehension is crucial given the ever-increasing volumes of information. In this work, we present PDC (probabilistic distributional clustering), a novel... more
The need to organize a large collection in a manner that facilitates human comprehension is crucial given the ever-increasing volumes of information. In this work, we present PDC (probabilistic distributional clustering), a novel algorithm that, given a document collection, computes disjoint term sets representing topics in the collection. The algorithm relies on probabilities of word co-occurrences to partition the set of terms appearing in the collection of documents into disjoint groups of related terms. In this work, we also present an environment to visualize the computed topics in the term space and retrieve the most related PubMed articles for each group of terms. We illustrate the algorithm by applying it to PubMed documents on the topic of suicide. Suicide is a major public health problem identified as the tenth leading cause of death in the US. In this application, our goal is to provide a global view of the mental health literature pertaining to the subject of suicide, an...
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Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards understanding the disease in general, identifying drugs to... more
Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards understanding the disease in general, identifying drugs to treat the disease, developing potential vaccines, etc. This has given rise to a rapidly growing body of literature that doubles in number of publications every 20 days as of May 2020. Providing medical professionals with means to quickly analyze the literature and discover growing areas of knowledge is necessary for addressing their question and information needs. In this study we analyze the LitCovid collection, 13,369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding. We do that by applying state-of-the-art named entity recognition, classification, clustering and other NLP techniques. By app...
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In biomedicine, key concepts are often expressed by multiple words (e.g., 'zinc finger protein'). Previous work has shown treating a sequence of words as a meaningful unit, where applicable, is not only important for human... more
In biomedicine, key concepts are often expressed by multiple words (e.g., 'zinc finger protein'). Previous work has shown treating a sequence of words as a meaningful unit, where applicable, is not only important for human understanding but also beneficial for automatic information seeking. Here we present a collection of PubMed Phrases that are beneficial for information retrieval and human comprehension. We define these phrases as coherent chunks that are logically connected. To collect the phrase set, we apply the hypergeometric test to detect segments of consecutive terms that are likely to appear together in PubMed. These text segments are then filtered using the BM25 ranking function to ensure that they are beneficial from an information retrieval perspective. Thus, we obtain a set of 705,915 PubMed Phrases. We evaluate the quality of the set by investigating PubMed user click data and manually annotating a sample of 500 randomly selected noun phrases. We also analyze ...
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The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the... more
The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.
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BioC is a recently created XML format to share text data and annotations, and an accompanying input/output library to promote interoperability of data and tools for natural language processing of biomedical text. This article reports the... more
BioC is a recently created XML format to share text data and annotations, and an accompanying input/output library to promote interoperability of data and tools for natural language processing of biomedical text. This article reports the use of BioC to address a common challenge in processing biomedical text information-that of frequent entity name abbreviation. We selected three different abbreviation definition identification modules, and used the publicly available BioC code to convert these independent modules into BioC-compatible components that interact seamlessly with BioC-formatted data, and other BioC-compatible modules. In addition, we consider four manually annotated corpora of abbreviations in biomedical text: the Ab3P corpus of 1250 PubMed abstracts, the BIOADI corpus of 1201 PubMed abstracts, the old MEDSTRACT corpus of 199 PubMed(®) citations and the Schwartz and Hearst corpus of 1000 PubMed abstracts. Annotations in these corpora have been re-evaluated by four annota...
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There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically... more
There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically identifying meaningful categories of entities in a large text corpus is useful for information extraction, construction of machine learning features, and development of semantic representations. In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent nouns from Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms as potential biomedical categories. We study and ...
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Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a... more
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a...
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ABSTRACT PubMed queries frequently retrieve thousands of documents making it very challenging for a user to identify information of interest. In this paper we propose a method for automatically identifying central concepts in large PubMed... more
ABSTRACT PubMed queries frequently retrieve thousands of documents making it very challenging for a user to identify information of interest. In this paper we propose a method for automatically identifying central concepts in large PubMed retrievals. The centrality of a concept is modeled using the hypergeometric distribution. Retrieved documents are grouped by concept, which can help users navigate the retrieval. We test our method on five datasets, each representing a medical condition.
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Clustering methods can be useful for automatically grouping documents into meaningful clusters, improving human comprehension of a document collection. Although there are clustering algorithms that can achieve the goal for relatively... more
Clustering methods can be useful for automatically grouping documents into meaningful clusters, improving human comprehension of a document collection. Although there are clustering algorithms that can achieve the goal for relatively large document collections, they do not always work well for small and homogenous datasets. In this article, we present Retro-a novel clustering algorithm that extracts meaningful clusters along with concise and descriptive titles from small and homogenous document collections. Unlike common clustering approaches, our algorithm predicts cluster titles before clustering. It relies on the hypergeometric distribution model to discover key phrases, and generates candidate clusters by assigning documents to these phrases. Further, the statistical significance of candidate clusters is tested using supervised learning methods, and a multiple testing correction technique is used to control the overall quality of clustering. We test our system on five disease da...
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Research Interests: Computer Science, Algorithms, Artificial Intelligence, Natural Language Processing, Biomedical informatics, and 11 moreMedicine, Biological Sciences, Software, Punctuation, Humans, United States, Medline, Information Storage and Retrieval, Phrase, Controlled Vocabulary, and Medical and Health Sciences
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A significant fraction of queries in PubMed™ are multiterm queries without parsing instructions. Generally, search engines interpret such queries as collections of terms, and handle them as a Boolean conjunction of these terms. However,... more
A significant fraction of queries in PubMed™ are multiterm queries without parsing instructions. Generally, search engines interpret such queries as collections of terms, and handle them as a Boolean conjunction of these terms. However, analysis of queries in PubMed™ indicates that many such queries are meaningful phrases, rather than simple collections of terms. In this study, we examine whether or not it makes a difference, in terms of retrieval quality, if such queries are interpreted as a phrase or as a conjunction of query terms. And, if it does, what is the optimal way of searching with such queries. To address the question, we developed an automated retrieval evaluation method, based on machine learning techniques, that enables us to evaluate and compare various retrieval outcomes. We show that the class of records that contain all the search terms, but not the phrase, qualitatively differs from the class of records containing the phrase. We also show that the difference is systematic, depending on the proximity of query terms to each other within the record. Based on these results, one can establish the best retrieval order for the records. Our findings are consistent with studies in proximity searching.
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Background The rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. Most existing approaches... more
Background The rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. Most existing approaches for the abbreviation definition identification task employ rule-based methods. While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns. Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem. However, they require manually labeled training data. Methods In this work, we develop a machine learning algorithm for abbreviation definition identification in text which makes use of what we term naturally labeled data. Positive training examples are naturally occurring potential abbreviation-definition pairs in text. Negative training examples are generated by randomly mixing potential abbreviations with unrelated potential definitions. The machine learner is trained to distinguish between these two sets of examples. Then, the learned feature weights are used to identify the abbreviation full form. This approach does not require manually labeled training data. Results We evaluate the performance of our algorithm on the Ab3P, BIOADI and Medstract corpora. Our system demonstrated results that compare favourably to the existing Ab3P and BIOADI systems. We achieve an F-measure of 91.36% on Ab3P corpus, and an F-measure of 87.13% on BIOADI corpus which are superior to the results reported by Ab3P and BIOADI systems. Moreover, we outperform these systems in terms of recall, which is one of our goals.
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Research Interests: Algorithms, Natural Language Processing, Information Extraction, Named Entity Recognition, Biological Sciences, and 9 moreSequence alignment, Markov chains, hidden Markov model, Proteins, Genes, Information Storage and Retrieval, CHEMICAL SCIENCES, Automatic term recognition, and Dynamic Programming Algorithm
The pool adjacent violators (PAV) algorithm is an efficient technique for the class of isotonic regression problems with complete ordering. The algorithm yields a stepwise isotonic estimate which approximates the function and assigns... more
The pool adjacent violators (PAV) algorithm is an efficient technique for the class of isotonic regression problems with complete ordering. The algorithm yields a stepwise isotonic estimate which approximates the function and assigns maximum likelihood to the data. However, if one has reasons to believe that the data were generated by a continuous function, a smoother estimate may provide a better approximation to that function. In this paper, we consider the formulation which assumes that the data were generated by a continuous monotonic function obeying the Lipschitz condition. We propose a new algorithm, the Lipschitz pool adjacent violators (LPAV) algorithm, which approximates that function; we prove the convergence of the algorithm and examine its complexity.
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Schapire and Singer's improved version of AdaBoost for handling weak hypotheses with confidence rated predictions represents an important advance in the theory and practice of boosting. Its success results from a more efficient use of... more
Schapire and Singer's improved version of AdaBoost for handling weak hypotheses with confidence rated predictions represents an important advance in the theory and practice of boosting. Its success results from a more efficient use of information in weak hypotheses during updating. Instead of simple binary voting a weak hypothesis is allowed to vote for or against a classification with a variable strength or confidence. The Pool Adjacent Violators (PAV) algorithm is a method for converting a score into a probability. We show how PAV may be applied to a weak hypothesis to yield a new weak hypothesis which is in a sense an ideal confidence rated prediction and that this leads to an optimal updating for AdaBoost. The result is a new algorithm which we term PAV-AdaBoost. We give several examples illustrating problems for which this new algorithm provides advantages in performance.
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There are many situations wherein a group of individuals (e.g., voters, experts, sports writers) must produce an ordered list of ‘best’ alternatives selected from a given group of alternatives (e.g., candidates, proposals, sports teams).... more
There are many situations wherein a group of individuals (e.g., voters, experts, sports writers) must produce an ordered list of ‘best’ alternatives selected from a given group of alternatives (e.g., candidates, proposals, sports teams). Two long established mechanisms that have been used for this task are ‘Zermelo’s Ranking Method’ (1929) and ‘Borda’s Voting Scheme’ (1781). The main purpose of this paper is to point out that they are, under certain common circumstances, identical. We then show that Zermelo’s Method can be used in situations that Borda’s Method is not designed to handle.