Another Word For It

A Survey of Stochastic and Gazetteer Based Approaches for Named Entity Recognition – Part 2 by Benjamin Bengfort.

From the post:

Generally speaking, the most effective named entity recognition systems can be categorized as rule-based, gazetteer and machine learning approaches. Within each of these approaches are a myriad of sub-approaches that combine to varying degrees each of these top-level categorizations. However, because of the research challenge posed by each approach, typically one or the other is focused on in the literature.

Rule-based systems utilize pattern-matching techniques in text as well as heuristics derived either from the morphology or the semantics of the input sequence. They are generally used as classifiers in machine-learning approaches, or as candidate taggers in gazetteers. Some applications can also make effective use of stand-alone rule-based systems, but they are prone to both overreach and skipping over named entities. Rule-based approaches are discussed in (10), (12), (13), and (14).

Gazetteer approaches make use of some external knowledge source to match chunks of the text via some dynamically constructed lexicon or gazette to the names and entities. Gazetteers also further provide a non-local model for resolving multiple names to the same entity. This approach requires either the hand crafting of name lexicons or some dynamic approach to obtaining a gazette from the corpus or another external source. However, gazette based approaches achieve better results for specific domains. Most of the research on this topic focuses on the expansion of the gazetteer to more dynamic lexicons, e.g. the use of Wikipedia or Twitter to construct the gazette. Gazette based approaches are discussed in (15), (16), and (17).

Stochastic approaches fare better across domains, and can perform predictive analysis on entities that are unknown in a gazette. These systems use statistical models and some form of feature identification to make predictions about named entities in text. They can further be supplemented with smoothing for universal coverage. Unfortunately these approaches require large amounts of annotated training data in order to be effective, and they don’t naturally provide a non-local model for entity resolution. Systems implemented with this approach are discussed in (7), (8), (4), (9), and (6).

Benjamin continues his excellent survey of named entity recognition techniques.

All of these techniques may prove to be useful in constructing topic maps from source materials.

An Introduction to Named Entity Recognition in Natural Language Processing – Part 1 by Benjamin Bengfort.

From the post:

Abstract:

The task of identifying proper names of people, organizations, locations, or other entities is a subtask of information extraction from natural language documents. This paper presents a survey of techniques and methodologies that are currently being explored to solve this difficult subtask. After a brief review of the challenges of the task, as well as a look at previous conventional approaches, the focus will shift to a comparison of stochastic and gazetteer based approaches. Several machine-learning approaches are identified and explored, as well as a discussion of knowledge acquisition relevant to recognition. This two-part white paper will show that applications that require named entity recognition will be served best by some combination of knowledge- based and non-deterministic approaches.

Introduction:

In school we were taught that a proper noun was “a specific person, place, or thing,” thus extending our definition from a concrete noun. Unfortunately, this seemingly simple mnemonic masks an extremely complex computational linguistic task—the extraction of named entities, e.g. persons, organizations, or locations from corpora (1). More formally, the task of Named Entity Recognition and Classification can be described as the identification of named entities in computer readable text via annotation with categorization tags for information extraction.

Not only is named entity recognition a subtask of information extraction, but it also plays a vital role in reference resolution, other types of disambiguation, and meaning representation in other natural language processing applications. Semantic parsers, part of speech taggers, and thematic meaning representations could all be extended with this type of tagging to provide better results. Other, NER-specific, applications abound including question and answer systems, automatic forwarding, textual entailment, and document and news searching. Even at a surface level, an understanding of the named entities involved in a document provides much richer analytical frameworks and cross-referencing.

Named entities have three top-level categorizations according to DARPA’s Message Understanding Conference: entity names, temporal expressions, and number expressions (2). Because the entity names category describes the unique identifiers of people, locations, geopolitical bodies, events, and organizations, these are usually referred to as named entities and as such, much of the literature discussed in this paper focuses solely on this categorization, although it is easy to imagine extending the proposed systems to cover the full MUC-7 task. Further, the CoNLL-2003 Shared Task, upon which the standard of evaluation for such systems is based, only evaluates the categorization of organizations, persons, locations, and miscellaneous named entities. For example:

(ORG S.E.C.) chief (PER Mary Shapiro) to leave (LOC Washington) in December.

This sentence contains three named entities that demonstrate many of the complications associated with named entity recognition. First, S.E.C. is an acronym for the Securities and Exchange Commission, which is an organization. The two words “Mary Shapiro” indicate a single person, and Washington, in this case, is a location and not a name. Note also that the token “chief” is not included in the person tag, although it very well could be. In this scenario, it is ambiguous if “S.E.C. chief Mary Shapiro” is a single named entity, or if multiple, nested tags would be required.

Nice introduction to the area and ends with a great set of references.

Looking forward to part 2!

Named entity extraction

From the webpage:

The techniques we discussed in the Cleanup and Reconciliation parts come in very handy when your data is already in a structured format. However, many fields (notoriously description) contain unstructured text, yet they usually convey a high amount of interesting information. To capture this in machine-processable format, named entity recognition can be used.

A Google Refine / OpenRefine extension developed by Multimedia Lab (ELIS — Ghent University / iMinds) and MasTIC (Université Libre de Bruxelles.

Described in: Named-Entity Recognition: A Gateway Drug for Cultural Heritage Collections to the Linked Data Cloud?

Abstract:

Unstructured metadata fields such as ‘description’ offer tremendous value for users to understand cultural heritage objects. However, this type of narrative information is of little direct use within a machine-readable context due to its unstructured nature. This paper explores the possibilities and limitations of Named-Entity Recognition (NER) to mine such unstructured metadata for meaningful concepts. These concepts can be used to leverage otherwise limited searching and browsing operations, but they can also play an important role to foster Digital Humanities research. In order to catalyze experimentation with NER, the paper proposes an evaluation of the performance of three thirdparty NER APIs through a comprehensive case study, based on the descriptive fields of the Smithsonian Cooper-Hewitt National Design Museum in New York. A manual analysis is performed of the precision, recall, and F-score of the concepts identified by the third party NER APIs. Based on the outcomes of the analysis, the conclusions present the added value of NER services, but also point out to the dangers of uncritically using NER, and by extension Linked Data principles, within the Digital Humanities. All metadata and tools used within the paper are freely available, making it possible for researchers and practitioners to repeat the methodology. By doing so, the paper offers a significant contribution towards understanding the value of NER for the Digital Humanities.

I commend the paper to you for a very close reading, particularly those of you in the humanities.

To conclude, the Digital Humanities need to launch a broader debate on how we can incorporate within our work the probabilistic character of tools such as NER services. Drucker eloquently states that ‘we use tools from disciplines whose epistemological foundations are at odds with, or even hostile to, the humanities. Positivistic, quantitative and reductive, these techniques preclude humanistic methods because of the very assumptions on which they are designed: that objects of knowledge can be understood as ahistorical and autonomous.’

Drucker, J. (2012), Debates in the Digital Humanities, Minesota Press, chapter Humanistic Theory and Digital Scholarship, pp. 85–95.

“…that objects of knowledge can be understood as ahistorical and autonomous.”

Certainly possible, but lossy, very lossy, in my view.

You?