## 1 Introduction: Name Disambiguation and Collocation
### Algorithmic vs. manual name disambiguation and collocation
#### 7.2.2 Algorithmic name disambiguation and collocation  
Algorithmic name disambiguation is not a feature that can just be switched on in a database. Both Google Scholar and PubMed lack name disambiguation because, \"Such software is expensive and time-consuming to develop, and the algorithms are far from perfect\" [1]. The medical research domain is one where most algorithmic attempts to disambiguate names are being developed. Indeed, the best algorithmic name disambiguation occurs within a specific domain or field, because it's easier to create programs that are limited to a specific set of publications, formats (such as journal articles or web pages), and authors. Algorithmic name disambiguation and collocation, like Internet search engines, will always be imperfect. Automatic disambiguation works like relevancy ranking; it is at best a calculated guess. [PERSON] summarizes:  
\"Algorithmic failures to achieve quality name disambiguation parallel similar weaknesses in information retrieval systems that rely on full-text searching and probabilistic relevance ranking. These failures demonstrate that artificial intelligence has not advanced as much as we would like. Name disambiguation, like information retrieval, needs a deterministic approach and human intervention to be successful and precise.\" [9]  
Another effective obstacle to automated name disambiguation is the fact that the vast majority of authors publish only one or a few articles or other titles in their lifetimes. According to [PERSON] and [PERSON], \"Research from several studies has demonstrated consistently that the distribution of names follows a power law, such that most names occur only once in a file (_i.e._, most authors have written or edited, _etc._ only one work)\" [10]. The fewer articles, web pages, _etc._ by or about a single author there are, the less data the computer algorithms have to use to discriminate among authors with the same name.  
Automatic name disambiguation works by gathering as much information about an individual name and then making a guess as to which other matching names represent the same entity and which do not. One of the things that hinders effective automatic disambiguation is changing data. For example, disambiguation programs use data such as author affiliation to group works by an author. But affiliation and other data used for this purpose, such as an author's email address, often change, and this causes the disambiguation algorithms to fail. For example, if a researcher writes several articles while affiliated with University A, and then moves to University B and continues to publish research, an algorithm would most likely view them as two distinct researchers with the same name, when in fact, only one exists.  
On the other hand, once a disambiguation algorithm has been programmed and set up to work on a database, it can be run multiple times at a very low cost. Automatic name disambiguation and collocation may be more effective in data mining, where textual corpora are extremely large and include a large number of names.