Proper noun features II (corpus 1)

In the previous experiments on proper noun lists, certain author names were omitted because of parsing error. We deduce from this inadvertent mistake and the degradation of performance that we would have an improved result on the newly revised corpus with the same settings of proper noun feature as before. By modifying the way to match feature to token, we can prepare different learning data from same lists. So we design several detailed proper noun feature-matching strategies described in the following table. Same lists with the previous experiments are also used.

Strategy Type of external list Property Feature name
Baseline Without any proper noun lists
S1 People name list No distinction between surname and forename NAMELIST
S2 People name list Distinction between surname and forename SURNAMELIST FORENAMELIST
S3 People name list, Place lists Distinction between surname and forename, Place lists (city and country) SURNAMELIST FORENAMELIST PLACELIST
S4 People name list Surname and forename are separately found SURNAMELIST FORENAMELIST
S5 People name list, Place lists Surname and forename are separately found, Place lists (city and country) SURNAMELIST FORENAMELIST PLACELIST
S6 Place lists Place lists (city and country) PLACELIST

One solution against the restriction of name list, we allow the combination of new name from surnames and forenames in the list. It means that even a full author name in our data is not found in the list, if surname and forename are matched with their own list respectively, we accept them as name. This is possible because our author name list distinguishes between surname and forename.

An important rule in the matching strategies S1, S2 and S3 is we prohibit the separation of surname and forename in searching. That is, we first check if a given token is surname and if yes, we examine if one of surrounding token forename. For that we check three different expressions of full name: [surname forename], [forename surname], [surname , forename]. In this way we sequentially check all the tokens during feature extraction process. For the comparison, we also separately find surname and forename in the strategy S4 and S5. For the placelist feature, we also check city and country names, which consist of more than a word. A part of a reference in learning data with strategy S2 (or S3) is shown in the following figure.

Since the first four name tokens are found in the name list but the two latters are not, the presentation of the reference is like above. In this case, even though ‘René’ is found in the name list, as ‘OTAYEK’ is not, finally the matching rule does not attach ‘surnamelist’ or ‘forenamelist’ to them. On the contrary to this, we attach the ‘forenamelist’ feature to ‘René’ token when applying S4 and S5. In the next post we compare the performance of these seven (baseline + 6) strategies.


Vous aimerez aussi...

Laisser un commentaire

Votre adresse e-mail ne sera pas publiée. Les champs obligatoires sont indiqués avec *

Ce site utilise Akismet pour réduire les indésirables. En savoir plus sur comment les données de vos commentaires sont utilisées.

Rechercher dans OpenEdition Search

Vous allez être redirigé vers OpenEdition Search