Generalisation in named entity recognition: A quantitative analysis

Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Conceptual Search and Text Categorization

The most fundamental problem in information retrieval is that of interpreting information needs of users, typically expressed in a short query. Using the surface level representation of the query is especially unsatisfactory when the information needs are topic specific such as ``US politics'' or ``Space Science'', that seem to require understanding of what the query mean rather than what it is. We suggest that a newly proposed semantic representation of Words (GabrilovichMa2007) can be used to support Conceptual Search. Namely, it allows retrieving documents on a given topic even when existing keyword-based search approaches fail. The method we develop allows us to categorize and retrieve documents topically on-the-fly, without looking at the data collection ahead of time, without knowing a-priori the topics of interest and without training topic categorization classifiers. We compare our approach experimentally to state-of-the-art IR techniques and to machine learning based text categorization techniques and demonstrate significant improvement in performance. Moreover, as we show, our method is intrinsically adaptable to new text collections and domains

Who's Who in Your Digital Collection: Developing a Tool for Name Disambiguation and Identity Resolution

In the past twenty years, the problem space of automatically recognizing, extracting, classifying, and disambiguating named entities (e.g., the names of people, places, and organizations) from digitized text has received considerable attention in research produced by the library, computer science, and the computational linguistics communities. However, linking the output of these advances with the library community continues to be a challenge. This paper describes work being done by the University of Illinois, the Online Computer Library Center (OCLC), and the University of Maryland to develop, evaluate and link Named Entity Recognition (NER) and Entity Resolution with tools used for search and access. Name identification and extraction tools, particularly when integrated with a resolution into an authority file (e.g., WorldCat Identities, Wikipedia, etc.), can enhance reliable subject access for a document collection, improving document discoverability by end-users.Library of Congress / NDIIPP-2 A6075published or submitted for publicationis peer reviewe

Investing in Russian Securities: Analysis of Capital Market Development

Russia works hard to create a capitalistic system. Although only established in 1991, Russia\u27s securities market has already attracted a large number of foreign investors. The American financier George Soros has already invested US $1 billion in the Russian economy. Although experts agree that its potential is enormous, the newborn Russian capital market contains a significant amount of risk. This Essay shows the basic features of the development of the Russian Securities Law, analyzes the present market conditions, and shares some ideas for the near future

Exploiting knowledge in NLP

In recent decades, the society depends more and more on computers for a large number of tasks. The first steps in NLP applications involve identification of topics, entities, concepts, and relations in text. Traditionally, statistical models have been successfully deployed for the aforementioned problems. However, the major trend so far has been: “scaling up by dumbing down”- that is, applying sophisticated statistical algorithms operating on very simple or low-level features of the text. This trend is also exemplified, by expressions such as "we present a knowledge-lean approach", which have been traditionally viewed as a positive statement, one that will help papers get into top conferences. This thesis suggests that it is essential to use knowledge in NLP, proposes several ways of doing it, and provides case studies on several fundamental NLP problems. It is clear that humans use a lot of knowledge when understanding text. Let us consider the following text "Carnahan campaigned with Al Gore whenever the vice president was in Missouri." and ask two questions: (1) who is the vice president? (2) is this sentence about politics or sports? A knowledge-lean NLP approach will have a great difficulty answering the first question, and will require a lot of training data to answer the second one. On the other hand, people can answer both questions effortlessly. We are not the first to suggest that NLP requires knowledge. One of the first such large-scale efforts, CYC, has started in 1984, and by 1995 has consumed a person-century of effort collecting 100000 concepts and 1000000 commonsense axioms, including "You can usually see peoples noses, but not their hearts". Unfortunately, such an effort has several problems. (a) The set of facts we can deduce is significantly larger than 1M . For example, in the above example "heart" can be replaced by any internal organ or tissue, as well as by a bank account, thoughts etc., leading to thousands of axioms. (b) The axioms often do not hold. For example, if the person is standing with their back to you, can cannot see their nose. And during an open heart surgery, you can see someone's heart. (c) Matching the concepts to natural-language expressions is challenging. For example, "Al Gore" can be referred to as "Democrat", "environmentalist", "vice president", "Nobel prize laureate" among other things. The idea of "buying a used car" can be also expressed as "purchasing a pre-owned automobile". Lexical variability in text makes using knowledge challenging. Instead of focusing on obtaining a large set of logic axioms, we are focusing on using knowledge-rich features in NLP solutions. We have used three sources of knowledge: a large corpus of unlabeled text, encyclopedic knowledge derived from Wikipedia and first-order-logic-like constraints within a machine learning framework. Namely, we have developed a Named Entity Recognition system which uses word representations induced from unlabeled text and gazetteers extracted from Wikipedia to achieve new state of the art performance. We have investigated the implications of augmenting text representation with a set of Wikipedia concepts. The concepts can either be directly mentioned in the documents, or not explicitly mentioned but closely related. We have shown that such document representation allows more efficient search and categorization than the traditional lexical representations. Our next step is using the knowledge injected from Wikipedia for co-reference resolution. While the majority of the knowledge in this thesis is encyclopedic, we have also investigated how knowledge about the structure of the problem in the form of constraints can allow leveraging unlabeled data in semi-supervised settings. This thesis shows how to use knowledge to improve state-of-the-art for four fundamental problems in NLP: text categorization, information extraction, concept disambiguation and coreference resolution, four tasks which have been considered the bedrock of NLP since its inception