University of Cambridge Sample Clauses

University of Cambridge. (UCAM) – lexical-semantic classes for verbs The basic resource requirements for LC acquisition for verbs are: raw corpora, text processing tools (including a tagger, a tokeniser, a lemmatiser, and a shallow parser), and an SCF acquisition system. UCAM has the following resources and tools available for English. Required resources and tools Available resources and tools Comments Raw corpora yes Text processing tools: tagger tokeniser lemmatiser shallow parser yes SCF acquisition system yes Tools We have a system which discovers lexical (syntactic-semantic) verb classes of the style found in (Xxxxx, 1993) and VerbNet (Xxxxxx-Xxxxxxx, 2005) in corpus data (Sun and Xxxxxxxx, 2009). The system extracts features from corpora which can indicate lexical classes. We employ a wide range features extracted from raw, tagged, lemmatized and/or parsed corpus data: co- occurrences, prepositional and lexical preferences (of verbs), tense (POS tags of verbs), voice (passive or active), SCFs parameterized for prepositions and other information, including verb selectional preferences. For classification we employ various methods. We have implemented both unsupervised methods (e.g. nearest neighbours, information bottleneck, information distortion, PLSI, spectral clustering) as well as supervised ones (e.g. SVMs, Gaussian). We have so far reported our best result using SCF+SP features and spectral clustering (Sun and Xxxxxxxx, 2009): around 80 F-measure when evaluated on the dataset of Sun et al., (2008).
Sample 1
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University of Cambridge. (UCAM) We plan to build domain-specific lexicons for SCFs for automotive and legal text. We will use the system described in Section 2 (as in Xxxxxx et al., 2007), but will investigate ways of improving this system and adapting it to new domains. We focus on improving both the hypothesis generation and hypothesis selection steps of SCF acquisition. The tagger and parser used for pre-processing in the hypothesis generation step of SCF acquisition have a large impact on the final accuracy of SCFs. Statistical techniques can be used to correct for noise in the parser output, but fundamentally the accuracy of this first stage remains crucial since detecting SCFs depends on syntactic analysis. As SCF systems have evolved, pre-processing has moved from lexical cues, to partial parsing, to full intermediate parsing. However, even in the last few years there have been further developments in tagging and parsing which could be important for SCF detection. Xxxxxx et al. (2007) has already shown that using the latest version of the RASP toolkit (Xxxxxxx et al., 2006) improved performance significantly. In addition to improvements in RASP, there are now other broad-coverage, high- accuracy unlexicalized parsers such as the Stanford parser (Xxxxx and Xxxxxxx, 2003) and the Berkeley parser (Xxxxxx et al. 2006). (We focus on unlexicalized parsers since they do not already have knowledge about SCFs, which is what we want to learn; although it may be possible to use lexicalized parsers for SCF acquisition in a self-training context.) We plan to use the latest version of RASP and also to investigate whether other unlexicalized parsers can provide alternative views of the data, or be used in an ensemble for more accurate pre- processing. This will involve some re-engineering of the classifier in the existing SCF acquisition tool to work with other parser formalisms. Parser ensembles have been successfully used to improve parsing accuracy on both intrinsic (Xxxxx and Xxxxx, 2006) and extrinsic measures (Xxxxx et al., 2008) and for such tasks as pre-processing French text for manual annotation as part of a large corpus (Xxxxxxxx et al. 2010). We will also look at retraining the POS tagger used in the RASP toolkit. A number of techniques for classifier domain adaptation have been introduced in the last few years (e.g. Xxxxx III, 2007) which make it possible to minimize the amount of manual annoation required in the new domain. We plan to investigate the use of such a technique....
Sample 1
University of Cambridge. (UCAM) UCAM has the following resources and tools available for English. Required resources and tools Available resources and tools Comments Raw corpora (min. 100 occurrences per verb) yes Several large corpora available, to be supplemented with project domain data Text processing tools: tagger tokeniser lemmatiser shallow parser/chunker yes RASP Subcat classifier yes Lexical builder yes Filter yes Evaluation resources (SCF dictionaries) yes But need to develop domain- specific resources Tools We have a system for subcategorization frame (SCF) acquisition which can be used to acquire comprehensive lexicons for verbs, nouns and adjectives from un-annotated corpus data (Xxxxxx et al., 2007). The system makes use of the RASP toolkit (Xxxxxxx et al., 2006). RASP is a modular statistical parsing system which includes a tokenizer, tagger, lemmatizer, and a wide- coverage unification-based tag-sequence parser. We use the standard scripts supplied with RASP to output the set of grammatical relations (GR) for the most probable analysis returned by the parser or, in the case of parse failures, the GRs for the most likely sequence of subanalyses. The dependency relationships which the GRs embody correspond closely to the head- complement structure which subcategorization acquisition attempts to recover, which makes GRs ideal input to the SCF classifier. The rule-based classifier incrementally matches GRs with the corresponding SCFs. The rules were manually developed by examining a set of development sentences to determine which relations were actually emitted by the parser for each SCF. The classifier identifies 168 verbal, 37 adjectival and 31 nominal frames. The SCFs recognized by the classifier were obtained by manually merging the frames exemplified in the COMLEX Syntax (Xxxxxxxx et al., 1994), ANLT (Xxxxxxxx et al., 1987) and NOMLEX (Xxxxxxx et al., 1997) dictionaries and including additional frames found by manual inspection of unclassifiable examples during development of the classifier. These consisted of e.g. some occurrences of phrasal verbs with complex complementation and with flexible ordering of the preposition/particle, some non-passivizable words with a surface direct object, and some rarer combinations of governed preposition and complementizer combinations. The frames were created so that they abstract over specific lexically-governed particles and prepositions and specific predicate selectional preferences but include some derived semi-predictable ...
Sample 1

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We will initiate new strategic curriculum projects and establish additional Teaching Scholars Networks to develop agreed curriculum benchmark standards and xxxxxx curriculum and teaching expertise across the faculties. Through collaboration between disciplines and faculties, our curriculum renewal projects will generate new resources and benchmark standards for use in future curriculum reviews and professional development for our staff. Enhancing teaching quality, support and recognition Alongside and supporting the process of curriculum reform is our work on enhancing and further valuing the high quality of teaching and curriculum across the institution. Following consistent improvements over the past five years in our performance against measures of student experience of their courses (Student Course Experience Questionnaires) we recently developed and introduced the first stage of a new University-wide strategy to enhance the quality of our students' experiences in all units of study. Through compacts on faculty teaching standards, we will continue to use a University-agreed teaching standards framework to help faculties address teaching quality issues. This process will be supported by new institutional data reporting processes. Each year, faculties will be required to negotiate improvement targets aligned to University-agreed standards and their own strategic priorities, and will be supported to identify and address quality issues. Longer term, we will embed these compacts in an annual cycle of planning, reporting and monitoring. We will extend the scope of our faculty teaching compacts to draw on a broader range of data than that relating to units of study, and will include additional institutional standards in relation to other institutional teaching priorities, such as engaged enquiry. During the life of our 2014-16 compact, we will extend this support to individual teachers through the rollout of the new Academic Planning and Development process for teaching, as well as through research and ongoing enhancements to our range of professional development opportunities for University teachers and research higher degree supervisors. This will complement the University’s enhancement and support for the career opportunities for teachers through the University’s new academic promotion process. It will also allow us to develop further the University and faculty teaching award and grants schemes. We will build institutional recognition for our talented teachers by engaging them in our curriculum renewal process, connecting them with each other through the establishment of additional Teaching Scholars Networks and by providing opportunities for their further professional development. Recognition of the importance of excellence in teaching will also be supported by the annual Sydney Teaching Colloquium, a successful initiative launched in 2011, which brings together the university teaching community to celebrate their achievements, critically debate key educational initiatives and share their expertise and exemplary practice. Improving the student experience Our Teaching and Learning strategies recognise that student wellbeing and the general quality of their experience while at university must underpin our efforts to improve teaching and learning. During the timeframe of our 2014-16 compact, we will deliver a greater coherence across all aspects of the student experience. 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