Web readibility and computer-assisted language learning

Proficiency in a second language is of vital importance for many people. Today’s access to corpora of text, including the Web, allows new techniques for improving language skill. Our project’s aim is the development of techniques for presenting the user with suitable web text, to allow optimal language acquisition via reading. Some text found on the Web may be of a suitable level of difficulty but appropriate techniques need to be devised for locating it, as well as methods for rapid retrieval. Our experiments described here compare the range of difficulty of text found on the Web to that found in traditional hard-copy texts for English as a Second Language (ESL) learners, using standard readability measures. The results show that the ESL text readability range fall within the range for Web text. This suggests that an on-line text retrieval engine based on readability can be of use to language learners. However, web pages pose their own difficulty, since those with scores representing high readability are often of limited use. Therefore readability measurement techniques need to be modified for the Web domain

Fun with filtering French

Early use of corpora for language learning has included analysis of word usage via concordancing. In addition, some attempts have been made to use readability criteria for recommending reading to learners. In this paper we discuss various tools and approaches for enhanced language learning support, including different methods of filtering text based on vocabulary and grammatical criteria. We demonstrate the effects of various criteria on the retrieval of text, assuming the user is English-speaking and learning French. Filtering text based on a small vocabulary of frequently occurring words, a set of English-French cognates and named entities, and high coverage criteria, results in the retrieval of short readable extracts from French literature. We expect that text available from the web may yield many more documents of appropriate readability

Gender, conversation style, schemas and policy

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Effectiveness of note duration information for music retrieval

Content-based music information retrieval uses features extracted from music to answer queries. For melodic queries, the two main features are the pitch and duration of notes. The note pitch feature has been well researched whereas duration has not been fully explored. In this paper, we discuss how the note duration feature can be used to alter music retrieval effectiveness. Notes are represented by strings called standardisations. A standardisation is designed for approximate string matching and may not capture melodic information precisely. To represent pitches, we use a string of pitch differences. Our duration standardisation uses a string of five symbols representing the relative durations of adjacent notes. For both features, the Smith-Waterman alignment is used for matching. We demonstrate combining the similarity in both features using a vector model. Results of our experiments in retrieval effectiveness show that note duration similarity by itself is not useful for effective music retrieval. Combining pitch and duration similarity using the vector model does not improve retrieval effectiveness over the use of pitch on its own

A study of human mood tagging of musical pieces

We conducted a survey in which participants were required to label the mood conveyed within a variety of musical pieces. Two different representations of mood were used, the 2D emotion space as well as updated Hevner mood labels. The results show that survey responses using the two mood representations were both consistent as well as sensible. In terms of music piece characteristics that influenced participant's responses, it has been shown that the intensity/energy, tempo and beat strength consistently influenced participant's mood responses while tonality and pitch did not. Finally, the survey has raised many important questions relating to labeling musical pieces with mood, including the handling of a musical piece conveying more than one mood simultaneously, as well as a musical piece that conveys rapid mood changes

Music ranking techniques evaluated

In a music retrieval system, a user presents a piece of music as a query and the system must identify from a corpus of performances other pieces with a similar melody. Several techniques have been proposed for matching such queries to stored music. In previous work, we found that local alignment, a technique derived from bioinformatics, was more effective than the n-gram methods derived from information retrieval; other researchers have reported success with n-grams, but have not compared against local alignment. In this paper we explore a broader range of n-gram techniques, and test them with both manual queries and queries automatically extracted from MIDI files. Our experiments show that n-gram matching techniques can be as effective as local alignment; one highly effective technique is to simply count the number of n-grams in common between the query and the stored piece of music. N-grams are particularly effective for short queries and manual queries, while local alignment is superior for automatic queries

The effect of using pitch and duration for symbolic music retrieval

Quite reasonable retrieval effectiveness is achieved for retrieving polyphonic (multiple notes at once) music that is symbolically encoded via melody queries, using relatively simple pattern matching techniques based on pitch sequences. Earlier work showed that adding duration information was not particularly helpful for improving retrieval effectiveness. In this paper we demonstrate that defining the duration information as the time interval between consecutive notes does lead to more effective retrieval when combined with pitch-based pattern matching in our collection of over 14 000 MIDI files

Note-based segmentation and hierarchy in the classification of digital musical instruments

The ability to automatically identify the musical instruments occurring in a recorded piece of music has important uses for various music-related applications. This paper examines the case of instrument classification where the raw data consists of musical phrases performed on digital instruments from eight instrument families. We compare the use of extracted features from a continuous sample of approximately one second, to the use of a systematic segmentation of the audio on note boundaries and using multiple, aligned note samples as input to classifiers. The accuracy of the segmented approach was greater than the one of the unsegmented approach. The best method was using a two-tiered hierarchical method which performed slightly better than the single-tiered flat approach. The best performing instrument category was woodwind, with an accuracy of 94% for the segmented approach, using the Bayesian network classifier. Distinguishing different types of pianos was difficult for all classifiers, with the segmented approach yielding an accuracy of 56%. For humans, broadly similar results were found, in that pianos were difficult to distinguish, along with woodwind and solo string instruments. However there was no symmetry between human comparisons of identical instruments and different instruments, with half of the broad instrument categories having widely different accuracies for the two cases

In your eyes: identifying cliches in song lyrics

We investigated methods for the discovery of cliches from song lyrics. Trigrams and rhyme features were extracted from a collection of lyrics and ranked using term-weighting techniques such as tf-idf. These attributes were also examined over both time and genre. We present an application to produce a cliche score for lyrics based on these findings and show that number one hits are substantially more cliched than the average published song

Exploring microtonal matching

Most research intomusic information retrieval thus far has only examined music from the western tradition. However, music of other origins often conforms to different tuning systems. Therefore there are problems both in representing this music as well as finding matches to queries from these diverse tuning systems. We discuss the issues associated with microtonal music retrieval and present some preliminary results from an experiment in applying scoring matrices to microtonal matching