BACKGROUND
topic detection is defined in this application as a task that automatically identifies topics  based on the information content of a scientific article.
topic detection is an important field that can benefit many other natural language processing tasks including information retrieval, summarization, and question answering.
information retrieval may organize the retrieved results into topics to facilitate user navigation ); for example, molecular biologists may be interested in browsing articles related to "gene function"; and structural biologists may prefer to browse the articles related to "protein structure".
knowing the topics of relevant articles will assist human annotators to create knowledge bases efficiently.
for example, t1dbase  <cit>  is a database designated to genes related to type i diabetes mellitus.
the annotators need to identify literature articles that are relevant to specific genes related to type i diabetes mellitus with the topic of "cellular molecular biology and genetics".
topic detection is crucial for the summarization, the technique that condenses information while preserving information content.
a summarization system may summarize documents within a topic and aggregate information across topics.
topic detection is also important for question answering, the techniques that produce short texts to answer users' specific questions.
for example, yu and hatzivassiloglou  <cit>  made extensive uses of topic detection techniques to extract specific answers
research in topic detection has started since  <dig> as a part of topic detection and tracking  under the darpa translingual information detection, extraction, and summarization  program.
the closely related task is text categorization that has become very standard to organize text  <cit> .
on the other hand, there is much less reported work in the biomedical domain.
a closely related work is by wilbur  <cit>  who developed an em algorithm to identify "theme" or topics from a large collection of text.
however, his method has not yet been evaluated in the tasks of topic detection.
another related work is by hearst  <cit>  who has built an information retrieval system in which retrieved pubmed citations are aggregated based on the medical subject headings  that were assigned to the articles.
mesh terms in this case serve as the topics of the citation.
this approach benefits from the fact that the mesh terms represent the content of the full-text articles of the pubmed citations and each medline citation has assigned mesh terms by the nlm annotators.
on the other hand, there are several disadvantages of this approach.
since each medline abstract has an average of eleven mesh terms assigned and therefore it might result in too many topics for a collection of pubmed citations; many mesh terms are also too specific  to be used as topics.
in addition, the mesh terms are related hierarchically, and frequently both the parent and the child are assigned to the same citation; this results in "redundant" topics.
much of other related work focus on the tasks of associating genes to gene ontology codes  <cit> .
this work presents the first attempt that applies both topic spotting and topic clustering, two topic detection methods that automatically detect topics in the biomedical text.
we evaluate our methods to detect topics from more than fifteen thousand reference articles that are cited over sixteen thousand entries in the online mendelian inheritance in man  database  <cit> .
additionally, we have evaluated our methods to detect topics that are annotated by biologists.
we have explored the semantic features, which come from the biomedical, domain-specific knowledge resources; namely, the medical subject headings  and the unified medical language system , to facilitate the task of topic detection.
we have found that the semantic features enhance the performance of topic detection in the biomedical texts.
RESULTS
we report the results of applying the models of topic spotting and topic clustering to detect topics of  <dig>  articles that have been incorporated into the omim database, as well as an additional set of  <dig> articles that are cited in four biological review articles.
omim statistics
topic spotting
our results show that the classification are in general enhanced when we include the mesh terms and the umls semantic types as the additional features and the best performance is achieved by combining all four features; namely, semantic types, mesh terms, and the words in both title and abstract; this leads to a  <dig> % accuracy.
as described in the methods section, the mesh terms are assigned by the national library of medicine annotators to each medline article.
the umls has assigned semantic types to each mesh term.
note that a baseline system that classifies every document into the largest category  would achieve an overall accuracy of  <dig> % and random guessing would lead to an overall accuracy of 4%; and therefore the topic detection method with naïve bayes is significantly better than the random guessing.
when we reduced the total number of topics to the top ten topics, we have obtained the best accuracy to be  <dig> % with a baseline of  <dig> %.
our results show that the classification performance, on the other hand, significantly decreases  when we replace the mesh terms with their general categories specified in the mesh hierarchy; the performance also slightly decreases when we add in the general mesh terms in addition to the original assigned mesh terms.
our results also show that using the semantic types alone as features also have disappointing performance , although the performance is notably higher than using the general categories of the mesh terms .
topic clustering
recall that we have applied both the group-wise-average and single-pass hierarchical clustering algorithms for topic clustering.
since the previous results of topic spotting  have shown that the general mesh categories are not useful features for topic detection, we report only the results with the feature combinations including the semantic types, the mesh terms, and the bag-of-words in title and abstract.
we report cost of detection  <cit>  as the evaluation metrics for topic clustering.
cost of detection is defined in the evaluation metrics of the methods section.
figure  <dig> and  <dig> report the results of cost of detection with the single-pass algorithms and the group-wise average respectively as a function of similarity threshold  in different feature sets.
both single-pass and group-wise-average hierarchical clustering algorithms are described in the methods section.
in both cases, the higher the threshold, the lower cost of detection or the better the system.
the results show that the best system  is group-wise-average with mesh terms alone at the similarity threshold τ =  <dig>  to achieve  <dig>  , which is comparable with the single pass's performance  <dig>  .
pm is defined as the probability of missing an article in a topic and pfa is defined as the probability of assigning an article to wrong topic.
since previous studies have shown that feature selections have an impact on the performance of topic detection  <cit> , we have therefore explored feature selection by the inverse document frequency  threshold.
idf is defined as log⁡2
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacyggsbabcqggvbwbcqggnbwzdawgaawcbagaegomaidabeaakmaabmaabawaasaaaeaacqwgobgtaeaacqwgubgbaaaacagloagaayzkaaaaaa@3615@ where n is the number of documents in the text collection and n is the number of documents containing this feature in the text collection.
we have performed idf feature selections based on both single pass and group-wise-average.
the results ) show that the idf threshold yields an improvement over the cost of detection.
however this improvement comes at the cost of higher false alarm  as noted.
the lowest cost of detection is when idf threshold =  <dig>  which has the cost of detection of  <dig>  ; this corresponds to an accuracy of  <dig> %.
the results also show that the differences between the single-pass and group-wise average further narrowed.
in our hierarchical clustering algorithm, a higher similarity threshold indicates a greater number of clusters will be generated by the algorithm; this will generally lead to a greater pm and lower pfa.. however, the number of clusters generated might be too many or too few for a given threshold and the performance might not be consistent over varied numbers of topics.
we have therefore examined the topic clustering performance in omim as a function of topics for our best system .
as shown in figure  <dig>  the performance of single-pass slightly decreases as the number of topics increases.
discussion
our results show that naïve bayes can achieve the highest accuracy  to automatically detect the topic of  <dig>  omim article to belong to the categories of  <dig> topics specified by the omim; which is significantly higher than the baseline system .
the results suggest that supervised machine-learning may be used to assist the manual efforts of omim annotation, in addition to the general nlp benefits that we have described in introduction.
we have found that the umls semantic types consistently enhance the performance of topic detection when used in addition to other features .
this is not surprising because we have found that the umls semantic network provide a comprehensive coverage of a high-level conceptual knowledge  <cit> .
our results also show that the mesh terms are useful features as they have enhanced the performance of topic detection.
on the other hand, we have found a drop in performance when we replace the mesh terms with their general terms; this might be partially caused by the fact that the mesh terms are organized in strict hierarchy and therefore result in fewer useful features compared with the multiple semantic types.
for example, the mesh term protein tyrosine phosphatase corresponds to only one second-level general mesh term enzyme, while the same term has two umls semantic types assigned .
our results suggest that the approach of directly applying the mesh terms as topics might not be optimal for assigning topics to biomedical texts, particularly in the omim database.
although topic spotting has the best performance, it requires a large set of predefined topics with an available training corpus.
because the training corpora, in most of cases, are very difficult to obtain, topic spotting has a significant limitation.
in fact, the field of biology is very dynamic and biologists frequently organize different collections of literature articles based on different biological purposes; this is reflected by the fact that different biological review articles incorporate different collections of articles under different topics created by the authors of the review articles.
the unsupervised topic clustering methods have the advantages of not depending on a training corpus and therefore being robust and being likely to be applicable to real world settings.
since unsupervised approaches in general under-perform supervised ones, it is not surprising that our results have shown that the best topic clustering algorithms have achieved a lower accuracy , when comparing to the accuracy of topic spotting of an accuracy of  <dig> %, to automatically detect topics of  <dig>  cited articles in the omim.
on the other hand, the performance of topic clustering is significantly higher  than the baseline of  <dig> % that classifies every document into the largest category and more than 30% higher than the random guessing 4%.
additionally, our results show that the topic clustering model performs reasonably well with our additional annotated corpus of a total of  <dig> articles that are cited in four review articles; the performance of the topic clustering increases with the quality of this corpus, and achieves the highest of 50% accuracy.
topic clustering therefore provides an alternative, robust unsupervised approach that could cluster documents and detect topics dynamically.
we have applied two approaches for topic clustering; namely, single-pass and group-wise-average.
our evaluation results show that group-wise-average out-performed single-pass; however, the differences are subtle .
because it is computationally much more expensive to apply the group-wise-average approach in a large text collection, we conclude that single-pass is an excellent alternative for topic-clustering in a large collection of text.
one disadvantage of the topic clustering methods is that the method only aggregates documents into clusters and does not provide a topic term for each cluster.
to automatically identify a topic term, we may apply the work of  <cit>  to automatically obtain keywords from each cluster and apply the keywords as the topics to represent the cluster.
we may also apply the work of  <cit>  to identify "theme" words to be used as the topics.
another disadvantage of our study is that we have evaluated both topic spotting and topic clustering methods to detect documents that incorporate only one topic, not multiple topics.
however, in reality, it is frequent that a document incorporates multiple topics.
to detect multiple topics, we may apply topic spotting with binary classification  so that a document can be assigned multiple topics.
to apply topic clustering methods for the purpose of detecting multiple topics, we may first apply  <cit>  to identify a list of the "theme" features for each potential topic.
given a document, we measure document similarity with the "theme" features for each topic and then assign yes or no to the document for the topic.
our results show that the lowest value of cost of detection or the best system for topic clustering is  <dig>  , which is higher than the reported cost of detection  by the systems that apply hierarchical clustering algorithms to detect topics in news articles  <cit> .
the difference may be explained by the quality of the evaluation data.
the tdt <dig> collection has been annotated by specialists for the purpose of evaluating topic detection and therefore has a high quality and the topics in the tdt <dig> collection are quite distinct.
the biological texts we have experimented with  are related by the same genes with related the biological topics  and therefore it is harder to separate biological texts for the purpose of topic detection.
in addition, our results have shown that the performance of topic clustering is enhanced significantly when the quality of the evaluation corpus increases.
in this case, the accuracy increases to  <dig> % from  <dig> % when a biologist has manually excluded the articles that do not belong to the assigned topics.
all our evaluation results of topic clustering have achieved significantly above the baselines, and therefore the results suggest that topic clustering methods are applicable to biomedical text, although there is still a big room to enhance the performance to make the methods real useful.
future work
to enhance the performance, one may further expand feature selection to biological functionally important words.
for example, "phosphorylation" and "3-d" are important word which might sufficiently separate "protein function" from "protein structure".
CONCLUSIONS
this study represents the first and the state-of-the-art topic detection methods in biomedical texts.
the evaluation has concluded that the supervised topic spotting has the highest performance for topic detection in the omim data.
our results show that although the unsupervised topic clustering methods under-perform than the topic spotting methods, the performance are significantly above the baseline.
additionally, the performance of the topic clustering methods is enhanced when applying to detecting topics that are defined by biologists in their review articles.
our results show that topic clustering methods are robust to deal with real world events.
the results also conclude that the performance of topic clustering increases with the quality of data.

 topic detection is defined in this application as a task that automatically identifies topics based on the information content of a scientific article. topic detection is an important field that can benefit many other natural language processing tasks including information retrieval, summarization, and question answering. information retrieval may organize the retrieved results into topics to facilitate user navigation ); for example, molecular biologists may be interested in browsing articles related to "gene function"; and structural biologists may prefer to browse the articles related to "protein structure". knowing the topics of relevant articles will assist human annotators to create knowledge bases efficiently. for example, t1dbase is a database designated to genes related to type i diabetes mellitus. the annotators need to identify literature articles that are relevant to specific genes related to type i diabetes mellitus with the topic of "cellular molecular biology and genetics". topic detection is crucial for the summarization, the technique that condenses information while preserving information content. a summarization system may summarize documents within a topic and aggregate information across topics. topic detection is also important for question answering, the techniques that produce short texts to answer users' specific questions. for example, yu and hatzivassiloglou made extensive uses of topic detection techniques to extract specific answers research in topic detection has started since as a part of topic detection and tracking under the darpa translingual information detection, extraction, and summarization program. the closely related task is text categorization that has become very standard to organize text. on the other hand, there is much less reported work in the biomedical domain. a closely related work is by wilbur who developed an em algorithm to identify "theme" or topics from a large collection of text. however, his method has not yet been evaluated in the tasks of topic detection. another related work is by hearst who has built an information retrieval system in which retrieved pubmed citations are aggregated based on the medical subject headings that were assigned to the articles. mesh terms in this case serve as the topics of the citation. this approach benefits from the fact that the mesh terms represent the content of the full-text articles of the pubmed citations and each medline citation has assigned mesh terms by the nlm annotators. on the other hand, there are several disadvantages of this approach. since each medline abstract has an average of eleven mesh terms assigned and therefore it might result in too many topics for a collection of pubmed citations; many mesh terms are also too specific to be used as topics. in addition, the mesh terms are related hierarchically, and frequently both the parent and the child are assigned to the same citation; this results in "redundant" topics. much of other related work focus on the tasks of associating genes to gene ontology codes. this work presents the first attempt that applies both topic spotting and topic clustering, two topic detection methods that automatically detect topics in the biomedical text. we evaluate our methods to detect topics from more than fifteen thousand reference articles that are cited over sixteen thousand entries in the online mendelian inheritance in man database. additionally, we have evaluated our methods to detect topics that are annotated by biologists. we have explored the semantic features, which come from the biomedical, domain-specific knowledge resources; namely, the medical subject headings and the unified medical language system, to facilitate the task of topic detection. we have found that the semantic features enhance the performance of topic detection in the biomedical texts.  we report the results of applying the models of topic spotting and topic clustering to detect topics of articles that have been incorporated into the omim database, as well as an additional set of articles that are cited in four biological review articles. omim statistics topic spotting our results show that the classification are in general enhanced when we include the mesh terms and the umls semantic types as the additional features and the best performance is achieved by combining all four features; namely, semantic types, mesh terms, and the words in both title and abstract; this leads to a % accuracy. as described in the methods section, the mesh terms are assigned by the national library of medicine annotators to each medline article. the umls has assigned semantic types to each mesh term. note that a baseline system that classifies every document into the largest category would achieve an overall accuracy of % and random guessing would lead to an overall accuracy of 4%; and therefore the topic detection method with naïve bayes is significantly better than the random guessing. when we reduced the total number of topics to the top ten topics, we have obtained the best accuracy to be % with a baseline of %. our results show that the classification performance, on the other hand, significantly decreases when we replace the mesh terms with their general categories specified in the mesh hierarchy; the performance also slightly decreases when we add in the general mesh terms in addition to the original assigned mesh terms. our results also show that using the semantic types alone as features also have disappointing performance, although the performance is notably higher than using the general categories of the mesh terms. topic clustering recall that we have applied both the group-wise-average and single-pass hierarchical clustering algorithms for topic clustering. since the previous results of topic spotting have shown that the general mesh categories are not useful features for topic detection, we report only the results with the feature combinations including the semantic types, the mesh terms, and the bag-of-words in title and abstract. we report cost of detection as the evaluation metrics for topic clustering. cost of detection is defined in the evaluation metrics of the methods section. figure and report the results of cost of detection with the single-pass algorithms and the group-wise average respectively as a function of similarity threshold in different feature sets. both single-pass and group-wise-average hierarchical clustering algorithms are described in the methods section. in both cases, the higher the threshold, the lower cost of detection or the better the system. the results show that the best system is group-wise-average with mesh terms alone at the similarity threshold τ = to achieve, which is comparable with the single pass's performance. pm is defined as the probability of missing an article in a topic and pfa is defined as the probability of assigning an article to wrong topic. since previous studies have shown that feature selections have an impact on the performance of topic detection, we have therefore explored feature selection by the inverse document frequency threshold. idf is defined as log⁡2 mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacyggsbabcqggvbwbcqggnbwzdawgaawcbagaegomaidabeaakmaabmaabawaasaaaeaacqwgobgtaeaacqwgubgbaaaacagloagaayzkaaaaaa@3615@ where n is the number of documents in the text collection and n is the number of documents containing this feature in the text collection. we have performed idf feature selections based on both single pass and group-wise-average. the results ) show that the idf threshold yields an improvement over the cost of detection. however this improvement comes at the cost of higher false alarm as noted. the lowest cost of detection is when idf threshold = which has the cost of detection of; this corresponds to an accuracy of %. the results also show that the differences between the single-pass and group-wise average further narrowed. in our hierarchical clustering algorithm, a higher similarity threshold indicates a greater number of clusters will be generated by the algorithm; this will generally lead to a greater pm and lower pfa.. however, the number of clusters generated might be too many or too few for a given threshold and the performance might not be consistent over varied numbers of topics. we have therefore examined the topic clustering performance in omim as a function of topics for our best system. as shown in figure the performance of single-pass slightly decreases as the number of topics increases. discussion our results show that naïve bayes can achieve the highest accuracy to automatically detect the topic of omim article to belong to the categories of topics specified by the omim; which is significantly higher than the baseline system. the results suggest that supervised machine-learning may be used to assist the manual efforts of omim annotation, in addition to the general nlp benefits that we have described in introduction. we have found that the umls semantic types consistently enhance the performance of topic detection when used in addition to other features. this is not surprising because we have found that the umls semantic network provide a comprehensive coverage of a high-level conceptual knowledge. our results also show that the mesh terms are useful features as they have enhanced the performance of topic detection. on the other hand, we have found a drop in performance when we replace the mesh terms with their general terms; this might be partially caused by the fact that the mesh terms are organized in strict hierarchy and therefore result in fewer useful features compared with the multiple semantic types. for example, the mesh term protein tyrosine phosphatase corresponds to only one second-level general mesh term enzyme, while the same term has two umls semantic types assigned. our results suggest that the approach of directly applying the mesh terms as topics might not be optimal for assigning topics to biomedical texts, particularly in the omim database. although topic spotting has the best performance, it requires a large set of predefined topics with an available training corpus. because the training corpora, in most of cases, are very difficult to obtain, topic spotting has a significant limitation. in fact, the field of biology is very dynamic and biologists frequently organize different collections of literature articles based on different biological purposes; this is reflected by the fact that different biological review articles incorporate different collections of articles under different topics created by the authors of the review articles. the unsupervised topic clustering methods have the advantages of not depending on a training corpus and therefore being robust and being likely to be applicable to real world settings. since unsupervised approaches in general under-perform supervised ones, it is not surprising that our results have shown that the best topic clustering algorithms have achieved a lower accuracy, when comparing to the accuracy of topic spotting of an accuracy of %, to automatically detect topics of cited articles in the omim. on the other hand, the performance of topic clustering is significantly higher than the baseline of % that classifies every document into the largest category and more than 30% higher than the random guessing 4%. additionally, our results show that the topic clustering model performs reasonably well with our additional annotated corpus of a total of articles that are cited in four review articles; the performance of the topic clustering increases with the quality of this corpus, and achieves the highest of 50% accuracy. topic clustering therefore provides an alternative, robust unsupervised approach that could cluster documents and detect topics dynamically. we have applied two approaches for topic clustering; namely, single-pass and group-wise-average. our evaluation results show that group-wise-average out-performed single-pass; however, the differences are subtle. because it is computationally much more expensive to apply the group-wise-average approach in a large text collection, we conclude that single-pass is an excellent alternative for topic-clustering in a large collection of text. one disadvantage of the topic clustering methods is that the method only aggregates documents into clusters and does not provide a topic term for each cluster. to automatically identify a topic term, we may apply the work of to automatically obtain keywords from each cluster and apply the keywords as the topics to represent the cluster. we may also apply the work of to identify "theme" words to be used as the topics. another disadvantage of our study is that we have evaluated both topic spotting and topic clustering methods to detect documents that incorporate only one topic, not multiple topics. however, in reality, it is frequent that a document incorporates multiple topics. to detect multiple topics, we may apply topic spotting with binary classification so that a document can be assigned multiple topics. to apply topic clustering methods for the purpose of detecting multiple topics, we may first apply to identify a list of the "theme" features for each potential topic. given a document, we measure document similarity with the "theme" features for each topic and then assign yes or no to the document for the topic. our results show that the lowest value of cost of detection or the best system for topic clustering is, which is higher than the reported cost of detection by the systems that apply hierarchical clustering algorithms to detect topics in news articles. the difference may be explained by the quality of the evaluation data. the tdt collection has been annotated by specialists for the purpose of evaluating topic detection and therefore has a high quality and the topics in the tdt collection are quite distinct. the biological texts we have experimented with are related by the same genes with related the biological topics and therefore it is harder to separate biological texts for the purpose of topic detection. in addition, our results have shown that the performance of topic clustering is enhanced significantly when the quality of the evaluation corpus increases. in this case, the accuracy increases to % from % when a biologist has manually excluded the articles that do not belong to the assigned topics. all our evaluation results of topic clustering have achieved significantly above the baselines, and therefore the results suggest that topic clustering methods are applicable to biomedical text, although there is still a big room to enhance the performance to make the methods real useful. future work to enhance the performance, one may further expand feature selection to biological functionally important words. for example, "phosphorylation" and "3-d" are important word which might sufficiently separate "protein function" from "protein structure".  this study represents the first and the state-of-the-art topic detection methods in biomedical texts. the evaluation has concluded that the supervised topic spotting has the highest performance for topic detection in the omim data. our results show that although the unsupervised topic clustering methods under-perform than the topic spotting methods, the performance are significantly above the baseline. additionally, the performance of the topic clustering methods is enhanced when applying to detecting topics that are defined by biologists in their review articles. our results show that topic clustering methods are robust to deal with real world events. the results also conclude that the performance of topic clustering increases with the quality of data. 
topic detection is a task that automatically identifies topics in scientific articles based on information content. our results indicate that incorporating the mesh terms and the umls semantic types as additional features enhances the performance of topic detection and the naïve bayes has the highest accuracy, %, for predicting the topic of an omim article as one of the total twenty-five topics. we have explored the methods of topic spotting, a task of text categorization that applies the supervised machine-learning technique naïve bayes to assign automatically a document into one or more predefined topics; and topic clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. we have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the online mendelian inheritance in man database. topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. additionally, we have explored semantic features; namely, the medical subject headings that are assigned to the medline records, and the unified medical language system semantic types that correspond to the mesh terms, in addition to bag of words, to facilitate the tasks of topic detection.
 topic detection is a task that automatically identifies topics in scientific articles based on information content. topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.  we have explored the methods of topic spotting, a task of text categorization that applies the supervised machine-learning technique naïve bayes to assign automatically a document into one or more predefined topics; and topic clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. we have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the online mendelian inheritance in man database. we have explored bag of words as the features. additionally, we have explored semantic features; namely, the medical subject headings that are assigned to the medline records, and the unified medical language system semantic types that correspond to the mesh terms, in addition to bag of words, to facilitate the tasks of topic detection. our results indicate that incorporating the mesh terms and the umls semantic types as additional features enhances the performance of topic detection and the naïve bayes has the highest accuracy, %, for predicting the topic of an omim article as one of the total twenty-five topics.  our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. 