Automating the production of bibliographic records for MEDLINE

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Automating the production of bibliographic records for MEDLINE

7.3 Reformatting the affiliation field

Institutional affiliations of the authors are reformatted by finding the best match between the OCR text and a list of about 130,000 correctly formatted affiliations obtained from the current production version of MARS. Simple string matching is not promising because of the myriad arrangements in which affiliations can be expressed. Most journals show the affiliations of all authors, but by convention only the affiliation of the first author is entered into MEDLINE. However, the text string corresponding to the first affiliation may be scattered throughout the OCR text for the affiliation field. As an example, when multiple authors are affiliated with different departments within the same institution, the printed affiliation may be "Department A, Department B, Department C, Institution XYZ," while the correct MEDLINE entry is "Department A, Institution XYZ." The problem is further confounded by OCR errors, especially errors in detecting superscripts and subscripts. To find a match, the entire OCR text of the affiliation field is compared with every entry in the list of existing affiliations. A matching score for each of the existing affiliations is calculated on the basis of partial token matches, distance between token matches and customized soundex matching. The three highest scoring candidates are presented to the Reconcile operator for selection. In preliminary tests, our current version of affiliation field reformatting successfully identifies the correct affiliation over 80% of the time when the affiliation is represented in the list. This success rate is expected to improve with parallel efforts to reduce OCR errors and the expansion of the list of affiliations from ongoing production data.

The first step is to read all these unique affiliations into memory and create a ternary search tree⁵⁶ for each affiliation, after which we create a soundex word list⁵⁷ for each affiliation.

When a zone is identified at the labeling stage as an affiliation field, the OCR data is first processed through a partial-matching algorithm. Low confidence characters are replaced with wildcards.

Example: Uniuersity. The 'u' is actually a 'v' but the OCR engine assigned it as a 'u' with a low confidence level. The partial match algorithm replaces the 'u' with a '.' signifying that this character is a wildcard, and that any word in our search tree that has the pattern Uni<any letter>ersity is considered to be a match.

The first step is to determine if a word in the affiliation zone matches one in the affiliation list. Ignoring implemented performance optimizations² we perform a partial word match for all the words in the OCR list and build up a chain of those words that do match. We also track distances between chains.

Consider the example of trying to find the affiliation "Department of Computer Science, University of Maryland" in the affiliation list. The OCR input string might look like: "Department of Computer Science, Department of Engineering, University of Maryland, Department of Computer Science, Johns Hopkins University."

Since only the first affiliation is to be retained, there is considerable data that is irrelevant. The problem is to retrieve just the data needed. By word chaining we can find chains of words that exist in both the OCR text and in an affiliation zone and then use these to derive weighted probabilities.

In this example there is a chain of 4 words that match, followed by 3 that do not match, followed by 3 more that match, and finally 7 that do not. Our probability algorithms compute chain word matches and distances between chained words.

The next step in our process reverses the partial word match. The ~130,000 affiliations are matched to the OCR affiliation.

Using the same example, "Department of Computer Science, University of Maryland" has 7 words and all 7 occur in our OCR word list. It is likely there is another affiliation entry that looks like "Department of Computer Science, University of Delaware". This would give a high match of 6/7 words. By comparing and weighting word matches from OCR to Corrected Affiliation and Corrected Affiliation to OCR, and using information such as the number of words matched, total number of words, chain of words matched, and chain of words unmatched, we arrive at a probability between 0 and 1. Note that partial matching is used to help cover OCR errors that would ruin a literal string pattern matching as the affiliation field is often in a smaller font and is likely to incur higher than normal OCR error rates.

In addition to a partial match search algorithm, a soundex algorithm is used with the addition of OCR substitution. For the example in which 'Uniuersity" has the 'u' as low confidence, a substitution table developed lists of common OCR errors where a u == v == y. All three letters are substituted in the low confidence 'u' position, and if a word matches with a soundex hash it counts as a match.

In our ground truth testing with affiliation zones²³, we found that if the OCR affiliation exists in our affiliation list of 130,000 entries, the probability that the affiliation match is the correct one is 88%. The affiliation reformat module picks the top 5 candidates which are presented to the reconcile operator who can choose the correct one in the 5, or pick the nearest match and type in any missing data, usually a room number, zip code or an email address.

7.4 Ongoing work

Current research focuses on the correct detection of superscripts in both the author and affiliation fields to help improve reformatting algorithms. With this information available, correct affiliation matching is expected to improve further.

Table 7.1 Categories of Author Reformat Rules
Category	Description	Example
Particle Name	Many names contain "particles" forming an integral part of the family name and possibly bearing significance to the family. A particle is retained as part of the reformatted author name.	Etienne du Vivier becomes du Vivier E, where 'du' is a particle and is retained as is and preceding the last name Vivier. The first name is initialized.
Compound	Compound family names are preserved in the form given and are often difficult to detect. We use a mix of rules to deduce it as a compound name. Most compound names use a hyphen. Those that don't can often use particle name rules to help preserve the compound name.	L.G. Huis in't Veld becomes Huis in't Veld LG. HG. Huigbregtse-Meyerink becomes HuigBregtse-Meyerink HG
Convert	Convert is a broad category that deals with general requirements to convert one pattern of text with another.	James A. Smith IV becomes Smith JA ^4th
Religious	Religious titles include Mother, Sister, Father, Brother. Names with surnames are handled differently from those that have no surnames.	Surname example: Sister Mary Hilda Miley becomes Miley MHNo-Surname example: Sister May Hilda becomes Mary Hilda Sister. For translated articles, e.g., from the French, Soeur becomes Sister.
Reduce	Reduction rules cover the elimination of text with a single author name. It also handles the Reduction of a person's given name and marking of the Surname if present.	Mr. John Smith becomes Smith J. John Smith MD becomes Smith J.
Lowercase	Some fields present all data uppercase. This rule simply converts to lower case all text that is uppercase.	JOHN SMITH becomes Smith J.
First Letter Upper	Title and Author at times will require that the first letter of a specific word be uppercased, depending on other rules.	JOHN SMITH becomes Smith J.
Author Delimiter	Many articles are by multiple authors who contributed to the paper, such as this one. This rule takes an OCR stream of text and creates a word list, a chain of words, and delimits where a particular author begins and ends in the complete chain of words.	Example1: Glenn M Ford, John Smith becomes: Ford GM Smith J (, is the delimiter here) Example 2: Glenn M. Ford, John Smith, and Susan O'Malley becomes: Ford GM Smith J O'Malley S (', and' is the trigger, which must precede in priority ',' as a triggered rule)

_{2. Optimizations such as: if the first word does not exist in the affiliation listing entry 1, go to entry 2 instead of looking at every OCR word.}

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