Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Improving Term Extraction with Terminological Resources

Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. The difficulty or impossibility of customising them to new domains is an additional limitation. In this paper, we propose to use external terminologies to influence generic linguistic data in order to augment the quality of the extraction. The tool we implemented exploits testified terms at different steps of the process: chunking, parsing and extraction of term candidates. Experiments reported here show that, using this method, more term candidates can be acquired with a higher level of reliability. We further describe the extraction process involving endogenous disambiguation implemented in the term extractor YaTeA

Sense-based biomedical indexing and retrieval

International audienceThis paper tackles the problem of term ambiguity, especially for biomedical literature. We propose and evaluate two methods of Word Sense Disambiguation (WSD) for biomedical terms and integrate them to a sense-based document indexing and retrieval framework. Ambiguous biomedical terms in documents and queries are disambiguated using the Medical Subject Headings (MeSH) thesaurus and semantically indexed with their associated correct sense. Experimental evaluation carried out on the TREC9-FT 2000 collection shows that our approach of WSD and sense-based indexing and retrieval is promising

Negation detection and word sense disambiguation in digital archaeology reports for the purposes of semantic annotation

The paper presents the role and contribution of Natural Language Processing Techniques, in particular Negation Detection and Word Sense Disambiguation in the process of Semantic Annotation of Archaeological Grey Literature. Archaeological reports contain a great deal of information that conveys facts and findings in different ways. This kind of information is highly relevant to the research and analysis of archaeological evidence but at the same time can be a hindrance for the accurate indexing of documents with respect to positive assertion

Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation

<p>Abstract</p> <p>Background</p> <p>Evaluation of Word Sense Disambiguation (WSD) methods in the biomedical domain is difficult because the available resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We present a method that can be used to automatically develop a WSD test collection using the Unified Medical Language System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. We demonstrate the use of this method by developing such a data set, called MSH WSD.</p> <p>Methods</p> <p>In our method, the Metathesaurus is first screened to identify ambiguous terms whose possible senses consist of two or more MeSH headings. We then use each ambiguous term and its corresponding MeSH heading to extract MEDLINE citations where the term and only one of the MeSH headings co-occur. The term found in the MEDLINE citation is automatically assigned the UMLS CUI linked to the MeSH heading. Each instance has been assigned a UMLS Concept Unique Identifier (CUI). We compare the characteristics of the MSH WSD data set to the previously existing NLM WSD data set.</p> <p>Results</p> <p>The resulting MSH WSD data set consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203 ambiguous entities. For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from MEDLINE.</p> <p>We evaluated the reliability of the MSH WSD data set using existing knowledge-based methods and compared their performance to that of the results previously obtained by these algorithms on the pre-existing data set, NLM WSD. We show that the knowledge-based methods achieve different results but keep their relative performance except for the Journal Descriptor Indexing (JDI) method, whose performance is below the other methods.</p> <p>Conclusions</p> <p>The MSH WSD data set allows the evaluation of WSD algorithms in the biomedical domain. Compared to previously existing data sets, MSH WSD contains a larger number of biomedical terms/abbreviations and covers the largest set of UMLS Semantic Types. Furthermore, the MSH WSD data set has been generated automatically reusing already existing annotations and, therefore, can be regenerated from subsequent UMLS versions.</p

Knowledge-based biomedical word sense disambiguation: comparison of approaches

<p>Abstract</p> <p>Background</p> <p>Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.</p> <p>Methods</p> <p>We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve MEDLINE citations. Then, a machine learning approach is trained on this corpus. The third approach is a graph-based method which exploits the structure of the Metathesaurus network of relations to perform unsupervised WSD. This approach ranks nodes in the graph according to their relative structural importance. The last approach uses the semantic types assigned to the concepts in the Metathesaurus to perform WSD. The context of the ambiguous word and semantic types of the candidate concepts are mapped to Journal Descriptors. These mappings are compared to decide among the candidate concepts. Results are provided estimating accuracy of the different methods on the WSD test collection available from the NLM.</p> <p>Conclusions</p> <p>We have found that the last approach achieves better results compared to the other methods. The graph-based approach, using the structure of the Metathesaurus network to estimate the relevance of the Metathesaurus concepts, does not perform well compared to the first two methods. In addition, the combination of methods improves the performance over the individual approaches. On the other hand, the performance is still below statistical learning trained on manually produced data and below the maximum frequency sense baseline. Finally, we propose several directions to improve the existing methods and to improve the Metathesaurus to be more effective in WSD.</p