Monthly Archives: March 2019

Word sense disambiguation: a hard case

Let us consider a hard case for word sense disambiguation, in the context of French to Corsican MT. But the same goes for French to English MT. It relates to French words such as: ‘accomplit’, ‘affaiblit’, ‘affranchit’, ‘alourdit’, ‘amortit’. The corresponding verbs ‘accomplir’ (to fulfill, to accomplish), ‘affaiblir’ (to weaken), ‘affranchir’ (to free), ‘alourdir’ (to burden), ‘amortir’ (to damp) have the same word for simple present and simple past at the third person singular: respectively ‘accomplit’, ‘affaiblit’, ‘affranchit’, ‘alourdit’, ‘amortit’. The upshot is that a single sentence such as: ‘Il affaiblit sa position.’ can be translated either into he weakens his position or into he weakened his position. If the context is unambiguous with regard to the sence of the discourse, the correct tense can be adequately chosen. But in the lack of informative context, it would be opportune to let the ambiguity prevail.

It should be pointed out that any such verbs are not rare. A more complete list includes: accomplit, affaiblit, affranchit, alourdit, amortit, anéantit, anoblit, aplatit, arrondit, assombrit, bannit, bâtit, blanchit, blondit, démolit, éblouit, emplit, enfouit, enhardit, enlaidit, ennoblit, envahit, épaissit, étourdit, exclut, franchit, glapit, investit, jaunit, jouit, munit, noircit, obéit, obscurcit, occit, périt, réagit, régit, réjouit, remplit, répartit, resplendit, rétrécit, rit, rougit, rouvrit, saisit, sévit, surgit.

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More on grammatical type disambiguation

Let us focus on grammatical type disambiguation, which is a subproblem of word disambiguation. General grammatical types are: verbs, nouns, adjectives, adverbs, prepositions, gerundive, etc. But for grammatical type disambiguation purposes, more accuracy is in order: instances of grammatical types are then: masculine singular noun, feminine singular noun, masculine plural noun, feminine plural noun, masculine singular adjective, feminine singular adjective, masculine plural adjective, feminine plural adjective, adverbs, prepositions, gerundive, etc. Now grammatical type disambiguation can occur between two different grammatical types (in the above-mentioned form). For example, an ambiguity can occur between preposition and gerundive. In French, this is notably the case for ‘devant’ and ‘maintenant’. For ‘devant’ can either be an adverb (in front) or a gerundive (from the verb ‘devoir’, to have to). Similarly, ‘maintenant’ can either be an adverb (now) or a gerundive (from the verb ‘maintenir’, to maintain). It should be clear now that ‘devant’ and ‘maintenant’ are both ambiguous with regard to their grammatical type. In English, depending on the relevant grammatical type, ‘devant’ is ambiguous between having to or in front). In the same way, ‘maintenant’ is ambiguous between now and maintening.
In order to disambiguate French words ‘devant’ or ‘maintenant’, rule-based MT needs a disambiguation module that is able to distinguish whether ‘devant’ or ‘maintenant’ are adverbs or gerundives.

(not to mention the fact that ‘devant’ can also be a preposition, for the sake of clarity).

New insight on the issue of pair reversal (updated)

The issue of pair reversal: it goes as follows: Suppose your have a given translation pair A>B that translates language A into language B, how hard is it to build the reverse pair B>A? Now the current instance of this problem goes as follows: given the French>Italian pair, how hard is it to build an Italian>French pair? To state it more explicitly : could AI help build a reverse pair in a very short time. Arguably, if AI could build such reverse pair shortly, it seems it would be some kind of breakthrough. Supposedly, we do not expect a 100% efficiency and accuracy in this reversal process, but if some 98% or 99% were possible, it would do the job. For AI within MT is not only targeted at translating, it is also targeted at constructing translation engines.

Just tested pair reversal from French-Italian to Italian-French. Well, some 70% can be made automatically, but a big issue is still remaining, that relates to the disambiguation of Italian words. The disambiguation engine seems to be the crux of the matter here. The uupshot is that the entire disambiguation module needs to be rewritten, in order (if possible) to be language-related. The new module must be more AI-focused. If successful, it could open the path to the (somewhat) fast construction of a multi-language ecosystem with a rule-based MT architecture.