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PubMedDate not supplied

Corpus-based statistical screening for phrase identification.

PURPOSE: The authors study the extraction of useful phrases from a natural language database by statistical methods. The aim is to leverage human effort by providing preprocessed phrase lists with a high percentage of useful material. METHOD: The approach is to develop six different scoring methods that are based on different aspects of phrase occurrence. The emphasis here is not on lexical information or syntactic structure but rather on the statistical properties of word pairs and triples that can be obtained from a large database. MEASUREMENTS: The Unified Medical Language System (UMLS) incorporates a large list of humanly acceptable phrases in the medical field as a part of its structure. The authors use this list of phrases as a gold standard for validating their methods. A good method is one that ranks the UMLS phrases high among all phrases studied. Measurements are 11-point average precision values and precision-recall curves based on the rankings. RESULT: The authors find of six different scoring methods that each proves effective in identifying UMLS quality phrases in a large subset of MEDLINE. These methods are applicable both to word pairs and word triples. All six methods are optimally combined to produce composite scoring methods that are more effective than any single method. The quality of the composite methods appears sufficient to support the automatic placement of hyperlinks in text at the site of highly ranked phrases. CONCLUSION: Statistical scoring methods provide a promising approach to the extraction of useful phrases from a natural language database for the purpose of indexing or providing hyperlinks in text.

Abstracting and Indexing↗
PubMedDate not supplied

Evaluation of a command-line parser-based order entry pathway for the Department of Veterans Affairs electronic patient record.

OBJECTIVE: To improve and simplify electronic order entry in an existing electronic patient record, the authors developed an alternative system for entering orders, which is based on a command- interface using robust and simple natural-language techniques. DESIGN: The authors conducted a randomized evaluation of the new entry pathway, measuring time to complete a standard set of orders, and users' satisfaction measured by questionnaire. A group of 16 physician volunteers from the staff of the Department of Veterans Affairs Puget Sound Health Care System-Seattle Division participated in the evaluation. RESULTS: Thirteen of the 16 physicians (81%) were able to enter medical orders more quickly using the natural-language-based entry system than the standard graphical user interface that uses menus and dialogs (mean time spared, 16.06 +/- 4.52 minutes; P=0.029). Compared with the graphical user interface, the command--based pathway was perceived as easier to learn (P<0.01), was considered easier to use and faster (P<0.01), and was rated better overall (P<0.05). CONCLUSION: Physicians found the command- interface easier to learn and faster to use than the usual menu-driven system. The major advantage of the system is that it combines an intuitive graphical user interface with the power and speed of a natural-language analyzer.

Consumer Behavior↗
PubMedDate not supplied

Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS.

OBJECTIVES: To test the hypothesis that most instances of negated concepts in dictated medical documents can be detected by a strategy that relies on tools developed for the parsing of formal (computer) languages-specifically, a lexical scanner ("lexer") that uses regular expressions to generate a finite state machine, and a parser that relies on a restricted subset of context-free grammars, known as LALR(1) grammars. METHODS: A diverse training set of 40 medical documents from a variety of specialties was manually inspected and used to develop a program (Negfinder) that contained rules to recognize a large set of negated patterns occurring in the text. Negfinder's lexer and parser were developed using tools normally used to generate programming language compilers. The input to Negfinder consisted of medical narrative that was preprocessed to recognize UMLS concepts: the text of a recognized concept had been replaced with a coded representation that included its UMLS concept ID. The program generated an index with one entry per instance of a concept in the document, where the presence or absence of negation of that concept was recorded. This information was used to mark up the text of each document by color-coding it to make it easier to inspect. The parser was then evaluated in two ways: 1) a test set of 60 documents (30 discharge summaries, 30 surgical notes) marked-up by Negfinder was inspected visually to quantify false-positive and false-negative results; and 2) a different test set of 10 documents was independently examined for negatives by a human observer and by Negfinder, and the results were compared. RESULTS: In the first evaluation using marked-up documents, 8,358 instances of UMLS concepts were detected in the 60 documents, of which 544 were negations detected by the program and verified by human observation (true-positive results, or TPs). Thirteen instances were wrongly flagged as negated (false-positive results, or FPs), and the program missed 27 instances of negation (false-negative results, or FNs), yielding a sensitivity of 95.3 percent and a specificity of 97.7 percent. In the second evaluation using independent negation detection, 1,869 concepts were detected in 10 documents, with 135 TPs, 12 FPs, and 6 FNs, yielding a sensitivity of 95.7 percent and a specificity of 91.8 percent. One of the words "no," "denies/denied," "not," or "without" was present in 92.5 percent of all negations. CONCLUSIONS: Negation of most concepts in medical narrative can be reliably detected by a simple strategy. The reliability of detection depends on several factors, the most important being the accuracy of concept matching.

Abstracting and Indexing↗
PubMed2004

The compositional structure of Gene Ontology terms.

An analysis of the term names in the Gene Ontology reveals the prevalence of substring relations between terms: 65.3% of all GO terms contain another GO term as a proper substring. This substring relation often coincides with a derivational relationship between the terms. For example, the term regulation of cell proliferation (GO:0042127) is derived from the term cell proliferation (GO:0008283) by addition of the phrase regulation of. Further, we note that particular substrings which are not themselves GO terms (e.g. regulation of in the preceding example) recur frequently and in consistent subtrees of the ontology, and that these frequently occurring substrings often indicate interesting semantic relationships between the related terms. We describe the extent of these phenomena--substring relations between terms, and the recurrence of derivational phrases such as regulation of--and propose that these phenomena can be exploited in various ways to make the information in GO more computationally accessible, to construct a conceptually richer representation of the data encoded in the ontology, and to assist in the analysis of natural language texts.

Computational Biology↗
PubMed2001

Including biological literature improves homology search.

Annotating the tremendous amount of sequence information being generated requires accurate automated methods for recognizing homology. Although sequence similarity is only one of many indicators of evolutionary homology, it is often the only one used. Here we find that supplementing sequence similarity with information from biomedical literature is successful in increasing the accuracy of homology search results. We modified the PSI-BLAST algorithm to use literature similarity in each iteration of its database search. The modified algorithm is evaluated and compared to standard PSI-BLAST in searching for homologous proteins. The performance of the modified algorithm achieved 32% recall with 95% precision, while the original one achieved 33% recall with 84% precision; the literature similarity requirement preserved the sensitive characteristic of the PSI-BLAST algorithm while improving the precision.

Algorithms↗
PubMed2004

Bioie: retargetable information extraction and ontological annotation of biological interactions from the literature.

The need for extracting general biological interactions of arbitrary types from the rapidly growing volume of the biomedical literature is drawing increased attention, while the need for this much diversity also requires both a robust treatment of complex linguistic phenomena and a method to consistently characterize the results. We present a biomedical information extraction system, BioIE, to address both of these needs by utilizing a full-fledged English grammar formalism, or a combinatory categorial grammar, and by annotating the results with the terms of Gene Ontology, which provides a common and controlled vocabulary. BioIE deals with complex linguistic phenomena such as coordination, relative structures, acronyms, appositive structures, and anaphoric expressions. In order to deal with real-world syntactic variations of ontological terms, BioIE utilizes the syntactic dependencies between words in sentences as well, based on the observation that the component words in an ontological term usually appear in a sentence with known patterns of syntactic dependencies.

Abstracting and Indexing↗
PubMed2004

Ruleminer: a knowledge system for supporting high-throughput protein function annotations.

In this paper, we present RuleMiner, a knowledge system to facilitate a seamless integration of multi-sequence analysis tools and define profile-based rules for supporting high-throughput protein function annotations. This system consists of three essential components, Protein Function Groups (PFGs), PFG profiles and rules. The PFGs, established from an integrated analysis of current knowledge of protein functions from Swiss-Prot database and protein family-based sequence classifications, cover all possible cellular functions available in the database. The PFG profiles illustrate detailed protein features in the PFGs as in sequence conservations, the occurrences of sequence-based motifs, domains and species distributions. The rules, extracted from the PFG profiles, describe the clear relationships between these PFGs and all possible features. As a result, the RuleMiner is able to provide an enhanced capability for protein function analysis, such as results from the integrated sequence analysis tools for given proteins can be comparatively analyzed due to the clear feature-PFG relationships. Also, much needed guidance is readily available for such analysis. If the rules describe one-to-one (unique) relationships between the protein features and the PFGs, then these features can be utilized as unique functional identifiers and cellular functions of unknown proteins can be reliably determined. Otherwise, additional information has to be provided.

Algorithms↗
PubMed2005

Suregene, a scalable system for automated term disambiguation of gene and protein names.

Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system that is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts is described. The system is based on supervised learning and builds a model for each LLID. The training sets for all LLIDs are extracted automatically from MEDLINE references in the LocusLink and SwissProt databases. A validation was done of the performance for all 20,546 human genes with LLIDs. Of these, 7344 produced good quality models (F-measure >0.7, nearly 60% of which were >0.9) and 13,202 did not, mainly due to insufficient numbers of known document references. A hand validation of MEDLINE documents for a set of 66 genes agreed well with the system's internal accuracy assessment. It is concluded that it is possible to achieve high quality gene disambiguation using scaleable automated techniques.

Algorithms↗
PubMed2006

Deriving pathway maps from automated text analysis using a grammar-based approach.

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

Abstracting and Indexing↗
PubMed2002

Psychological aspects of natural language. use: our words, our selves.

The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.

Artificial Intelligence↗
PubMed2001

Constrained emergence of universals and variation in syllable systems.

A computational model of emergent syllable systems is developed based on a set of functional constraints on syllable systems and the assumption that language structure emerges through cumulative change over time. The constraints were derived from general communicative factors as well as from the phonetic principles of perceptual distinctiveness and articulatory ease. Through evolutionary optimization, the model generated mock vocabularies optimized for the given constraints. Several simulations were run to understand how these constraints might define the emergence of universals and variation in complex sound systems. The predictions were that (1) CV syllables would be highly frequent in all vocabularies evolved under the constraints; (2) syllables with consonant clusters, consonant codas, and vowel onsets would occur much less frequently; (3) a relationship would exist between the number of syllable types in a vocabulary and the average word length in the vocabulary; (4) different syllable types would emerge according to, what we termed, an iterative principle of syllable structure and their frequency would be directly related to their complexity; and (5) categorical differences would emerge between vocabularies evolved under the same constraints. Simulation results confirmed these predictions and provided novel insights into why regularities and differences may occur across languages. Specifically, the model suggested that both language universals and variation are consistent with a set of functional constraints that are fixed relative to one another. Language universals reflect underlying constraints on the system and language variation represents the many different and equally-good solutions to the unique problem defined by these constraints.

Computer Simulation↗
PubMed2006

Spontaneous speech events in two speech databases of human-computer and human-human dialogs in Spanish.

Previous works in English have revealed that disfluencies follow regular patterns and that incorporating them into the language model of a speech recognizer leads to lower perplexities and sometimes to a better performance. Although work on disfluency modeling has been applied outside the English community (e.g., in Japanese), as far as we know there is no specific work dealing with disfluencies in Spanish. In this paper, we follow a data driven approach in exploring the potential benefit of modeling disfluencies in a speech recognizer in Spanish. Two databases of human-computer and human-human dialogs are considered, which allow the absolute and relative frequencies of disfluencies in the two situations to be compared. The rate of disfluencies in human-human dialogs is found to be very close to that found for similar databases in English. Due to setup factors, the rate of disfluencies found in human-computer dialogs was remarkably higher than that reported for similar databases in English. In any case, from the point of view of speech recognition, the high frequencies of disfluencies and the distinct features of the acoustic events related to them support the need for explicit acoustic models. The regularities observed in the distribution of filled pauses and speech repairs reveal that including them in the language model of the speech recognizer may be also helpful. The extent to which the number of events depends on utterance length and on the speaker is also explored. Statistics are shown that follow previous studies for English, and a sizeable space is devoted to comparing our results with them. Finally, various possible cues for the automatic detection of speech repairs--a key issue from the point of view of speech understanding--are explored: silent pauses, filled pauses, lengthenings, cut off words and discourse markers. As previously observed for English, none of them was found to be reliable by itself. More information, especially at the acoustic-prosodic level, is no doubt needed to reliably detect speech repairs.

Communication↗
PubMedDate not supplied

Linguistic adaptations during spoken and multimodal error resolution.

Fragile error handling in recognition-based systems is a major problem that degrades their performance, frustrates users, and limits commercial potential. The aim of the present research was to analyze the types and magnitude of linguistic adaptation that occur during spoken and multimodal human-computer error resolution. A semiautomatic simulation method with a novel error-generation capability was used to collect samples of users' spoken and pen-based input immediately before and after recognition errors, and at different spiral depths in terms of the number of repetitions needed to resolve an error. When correcting persistent recognition errors, results revealed that users adapt their speech and language in three qualitatively different ways. First, they increase linguistic contrast through alternation of input modes and lexical content over repeated correction attempts. Second, when correcting with verbatim speech, they increase hyperarticulation by lengthening speech segments and pauses, and increasing the use of final falling contours. Third, when they hyperarticulate, users simultaneously suppress linguistic variability in their speech signal's amplitude and fundamental frequency. These findings are discussed from the perspective of enhancement of linguistic intelligibility. Implications are also discussed for corroboration and generalization of the Computer-elicited Hyperarticulate Adaptation Model (CHAM), and for improved error handling capabilities in next-generation spoken language and multimodal systems.

Computer Simulation↗
PubMedDate not supplied

Intonation and dialog context as constraints for speech recognition.

This paper describes a way of using intonation and dialog context to improve the performance of an automatic speech recognition (ASR) system. Our experiments were run on the DCIEM Maptask corpus, a corpus of spontaneous task-oriented dialog speech. This corpus has been tagged according to a dialog analysis scheme that assigns each utterance to one of 12 "move types," such as "acknowledge," "query-yes/no" or "instruct." Most ASR systems use a bigram language model to constrain the possible sequences of words that might be recognized. Here we use a separate bigram language model for each move type. We show that when the "correct" move-specific language model is used for each utterance in the test set, the word error rate of the recognizer drops. Of course when the recognizer is run on previously unseen data, it cannot know in advance what move type the speaker has just produced. To determine the move type we use an intonation model combined with a dialog model that puts constraints on possible sequences of move types, as well as the speech recognizer likelihoods for the different move-specific models. In the full recognition system, the combination of automatic move type recognition with the move specific language models reduces the overall word error rate by a small but significant amount when compared with a baseline system that does not take intonation or dialog acts into account. Interestingly, the word error improvement is restricted to "initiating" move types, where word recognition is important. In "response" move types, where the important information is conveyed by the move type itself--for example, positive versus negative response--there is no word error improvement, but recognition of the response types themselves is good. The paper discusses the intonation model, the language models, and the dialog model in detail and describes the architecture in which they are combined.

Humans↗
PubMed2005

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

BACKGROUND: Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. RESULTS: Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs. CONCLUSION: The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

Algorithms↗
PubMed2005

Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information.

BACKGROUND: The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest. RESULTS: This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83). CONCLUSION: This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.

Artificial Intelligence↗
PubMed2005

PSE: a tool for browsing a large amount of MEDLINE/PubMed abstracts with gene names and common words as the keywords.

BACKGROUND: MEDLINE/PubMed (hereinafter called PubMed) is one of the most important literature databases for the biological and medical sciences, but it is impossible to read all related records due to the sheer size of the repository. We usually have to repeatedly enter keywords in a trial-and-error manner to extract useful records. Software which can reduce such a laborious task is therefore required. RESULTS: We developed a web-based software, the PubMed Sentence Extractor (PSE), which parses large number of PubMed abstracts, extracts and displays the co-occurrence sentences of gene names and other keywords, and some information from EntrezGene records. The result links to whole abstracts and other resources such as the Online Mendelian Inheritance in Men and Reference Sequence. While PSE executes at the sentence-level when evaluating the existence of keywords, the popular PubMed operates at the record-level. Therefore, the relationship between the two keywords, a gene name and a common word, is more accurately captured by PSE than PubMed. In addition, PSE shows the list of keywords and considers the synonyms and variations on gene names. Through these functions, PSE would reduce the task of searching through records for gene information. CONCLUSION: We developed PSE in order to extract useful records efficiently from PubMed. This system has four advantages over a simple PubMed search; the reduction in the amount of collected literatures, the showing of keyword lists, the consideration for synonyms and variations on gene names, and the links to external databases. We believe PSE is helpful in collecting necessary literatures efficiently in order to find research targets. PSE is freely available under the GPL licence as additional files to this manuscript.

Abstracting and Indexing↗