The semantic annotation of texts with senses from a computational lexicon is a complex and often subjective task. As a matter of fact, the fine granularity of the WordNet sense inventory [Fellbaum, Christiane (ed.). 1998. WordNet: An Electronic Lexical Database MIT Press], a de facto standard within the research community, is one of the main causes of a low inter-tagger agreement ranging between 70% and 80% and the disappointing performance of automated fine-grained disambiguation systems (around 65% state of the art in the Senseval-3 English all-words task). In order to improve the performance of both manual and automated sense taggers, either we change the sense inventory (e.g. adopting a new dictionary or clustering WordNet senses) or we aim at resolving the disagreements between annotators by dealing with the fineness of sense distinctions. The former approach is not viable in the short term, as wide-coverage resources are not publicly available and no large-scale reliable clustering of WordNet senses has been released to date. The latter approach requires the ability to distinguish between subtle or misleading sense distinctions. In this paper, we propose the use of structural semantic interconnections – a specific kind of lexical chains – for the adjudication of disagreed sense assignments to words in context. The approach relies on the exploitation of the lexicon structure as a support to smooth possible divergencies between sense annotators and foster coherent choices. We perform a twofold experimental evaluation of the approach applied to manual annotations from the SemCor corpus, and automatic annotations from the Senseval-3 English all-words competition. Both sets of experiments and results are entirely novel: structural adjudication allows to improve the state-of-the-art performance in all-words disambiguation by 3.3 points (achieving a 68.5% F1-score) and attains figures around 80% precision and 60% recall in the adjudication of disagreements from human annotators.

Here's a round-up of notable events in the commercial language technology space in the last quarter of 2007, organized by broad application category. A common thread that pops up throughout many of these is the integration of language technology into social networking applications and other related Web 2.0 themes. I'd put my money on this being a hot direction in 2008.

A year ago in this column I wondered aloud whether 2007 was to be the year in which question-answering (QA) really took off in the commercial space. I was provoked to ask that question by the increasing number of Web-based QA systems that were portraying themselves as the Next Thing in search for the masses. There was, in particular, a lot of buzz around the $12.5 million funding deal announced by Powerset. The San Francisco-based company had gained exclusive access to parsing technology from PARC, but hadn't at that point displayed any of its wares to the general public. However, the company was inviting people to sign up for access to Powerset Labs, where, we were told, we would get the opportunity to be the first to play with the technology and to provide feedback to make it better. Since then, there have been occasional screen images of the application seen in blog posts and other news items, and a small number of claimed sightings by bloggers who were granted privileged access. But Powerset Labs was finally launched at TechCrunch 40 in mid-September. The company's Web site says that they've begun to let people have access to the technology, and that they'll be ‘letting in the next wave of users as soon as possible’. My surf board is ready, but I'm not holding my breath. I signed up in June 2007, and haven't heard a thing since. Comments posted on the Powerset site suggest that there might be quite a few in the line before me.