Department of Languages,
This paper is organised as follows:
An interesting feature of neural nets is that knowledge can be represented at a finer level than a single symbol (i.e., by means of several numbers), a distributed description is therefore less prone to be corrupt. This method for knowledge representation is known as sub-symbolic. When there are difficulties to list or describe all symbols needed for an adequate description by frames, semantic nets or logic, a neural net is very useful: it works out the description for itself.
There are some important differences when comparing connectionist techniques (known as neural nets) with traditional, symbolic artificial intelligence. Symbolic AI techniques are universal and especially good at searching for solutions by sequences of reasoning steps. These traditional systems make use of structured design methods, they have declarative knowledge which can be read and maintained by non-experts but they are weak at simulating human common sense. Neural networks, on the other hand, are good at perceptual optimising and common sense tasks but are weak at logical reasoning and search. This sort of knowledge is not declarative and can therefore not be inspected that easily. Symbolic and connectionist AI can be seen as complementary approaches. (Bowerman, 2002)
The basic idea underlying computational neural nets is that functions of the brain can be approximated with a computing system containing a very large number of simple units. In analogy with biological nervous systems these simple units are called neurons, it is known that the human brain contains 1011of biological neurons.
A biological cell consists of dendrites, a cell body, and an axon. The membrane covering the cell body has a negative electric potential at its resting state. The synapses (narrow gaps) transmit activations between the dendrites. Inputs through excitatory synapses increases, and inputs through inhibitory synapses reduces, the membrane potential. When the potential exceeds a threshold, a pulse is generated. This pulse travels through the axon. The more inputs from excitatory synapses a neuron receives, the more frequently it fires (i.e. sends an output impulse).
Figure 1. A biological neuron A general artificial neuron
A general computational system has a labelled directed graph structure, see picture above. Each node performs some simple computations and each connection conveys a signal from one node to another. All connections are labelled with a number called the connection strength or weight that indicates to which extent a signal is amplified or diminished by the connection. Unlike the biological original, the simplified artificial neuron is modelled under the following assumptions:
- each node has only one output value, distributed to other nodes via links
which positions are irrelevant.
- all inputs are given simultaneously and remain activated until the computation
of the output function is completed.
- the position on the node of the incoming connection is irrelevant.
(Mehrotra et al., 1997:9)
The American researcher Frank Rosenblatt’s perceptron for pattern classification is an early and one of the most known neural networks, a very simplified description of it is here used to give an example of the function of such a network.
This simple perceptron has two layers of units, the input nodes (x1, x2) and one output node, with weighted connections (w1, w2) between them. The units take binary activity values (i.e. the pattern to classify has to be encoded as a collection of binary values) and the final output (y) will either be 0 or 1, which places the pattern in one of two classes (a simple perceptron can only handle problems with just 2 answers, e.g. On/Off, Yes/No, Left/Right).
Figure 2. A two-input perceptron with two adjustable weights and one output node
First, the connections are assigned a weight at random, then, the weighted input for all input nodes are summed up (in this case: x1w1 + x2w2) and compared to a chosen threshold value. Possible input to this simple perceptron is (0,0), (0,1), (1,0) or (1,1). The output function classifies each input example according to its summed activity. In order to classify correctly, the network can be trained. The training examples can therefore be assigned their known desired output with which the actual output can be compared. Then by adjusting the weights, the behaviour of the perceptron changes, so that the network eventually generalizes over all examples and will be able to classify even new presented patterns correctly, this is known as supervised learning. Because the weights record cumulative changes, the order in which the training patterns are presented does affect the search path through weight space. To reduce the tendency for more recently presented patterns to overshadow earlier patterns, the training set is repeated over many, so called, epochs. An epoch refers to one presentation of all examples in the training set.
Simple perceptrons have a fundamental restriction, they can only induce linear functions and thus not solve XOR-like problems. (Werbos, 1974) was the first to propose a learning algorithm for networks with more than two layers and with differentiable functions which solved such problems (see the backpropagation algorithm below). Several researchers in parallel rediscovered and developed his work a decade later.
The simplest architecture for a neural network has a feedforward structure, in which information flows only in one direction: i.e. from the input layer to the output layer, and in general via one or more layers of intermediate, hidden, nodes. Such networks are very limited in their ability to learn and process information over time, and that is why they are not so well suited for language domains, that typically involve complex temporal structure (Plaut, 1998). A better type of network for language tasks has a recurrent structure, with no a priori constraints regarding in which direction units can interact (Elman, 1991). Recurrent networks can also learn to process sequences of inputs and/or to produce sequences of outputs. For example, in a simple recurrent network like those proposed by Jeffrey Elman, a representation of a given element of a sequence can be stored and reintroduced as context information for subsequent elements, see schematic figure below.
Figure 6. A recurrent network with one hidden and one context layer, the latter is used as a sort of memory.
In many linguistic theories context free and context sensitive components of language grammar are separately represented by phrase structure at the one hand and some labelling or slash notation at the other. But (Elman, 1991) stated that with his neural network (that was used for word prediction) both could be represented as context sensitive. As mentioned above, this was done through the insertion of an intermediate context layer which is fed with a copy of each hidden layer activity and then reintroduces this information back to the hidden layer when the next input is presented. In this way the context and the current input together are used to compute the next output and to update the context.
Modern networks often use non-linear, proportional output and can thus cope with problems which have more than two answers. Instead of the stepfunction used by the perceptron output node described above (which according to a threshold value gives either “on” or “off” as an answer), more complex functions that are differentiable can be used. The choice of node functions that are differentiable everywhere allows the use of learning algorithms based on gradient descent: i.e. the weights are modified in small steps in a direction that corresponds to the negative gradient of an error measure. The backpropagation rule derived its name from the fact that the errors made by the network are propagated back through the network, i.e. the weights of the hidden layers are adapted according to the error. The basic idea of the backpropagation (BP) algorithm is that the error can be seen as a function of the weights (E = f(w) = f(w1, w2, …, wn)). The different weights decide the output and thereby also the errors, so the algorithm adjusts all weights in proportion to their contribution to the output.
Popular node functions used in many neural nets are sigmoid functions (see the graph of a sigmoid S-shaped curve below). They are continuous, differentiable everywhere, rotationally symmetric about some point (net = 0, in the given example below) and they asymptotically approach their saturation values (0 and 1 respectively, in the example below):
Figure 3. Graph of a sigmoid function
For this sigmoid function, when net (the net weighted input to the node in question) is approaching ∞, the saturation value for f(net) = 1, when net is approaching - ∞, the saturation value for f(net) = 0 (the most common choices of f(net) saturation values for a sigmoid function are 0 or -1 to 1). Other node functions, as the Gaussian or Radial Basis functions, can be described as bell-shaped curves. Here f(net) has a single maximum for net, which is the highest point of the bell-shaped curve. The net value can also here be interpreted in terms of class membership, depending on how close the net input is to the net maximum (i.e. the highest point of the curve).
In the late eighties Rosenberg and Sejnowski developed a system for mapping English textual words into corresponding speech sounds. This system, named NetTalk, uses a neural network to convert a machine-readable text file to streams of symbols that represent phonemes. A speech synthesiser was then used to turn the phoneme representations into speech sounds.
One of the networks used for this system was a feedforward multi-layer perceptron (MLP) with three layers of units and two layers of weighted connections in between them. It was trained with the back-propagation algorithm. Input to the network was a sequence of seven consecutive characters from an English text and the task was to map the middle character (i.e. letter number four out of the totally seven) to a representation of a single phoneme suitable in the context of the three characters on each side of it. The network was trained until it generalized sufficiently and then the connection weights were set to a fix value. The characters used were the Roman alphabet letters plus three punctuation marks, see example below:
Code for foreign and irregular words
stress and syllable info
Figure 4. Representation of written words and phonemes in NetTalk (Sejnowski & Rosenberg, 1986).
For each of the seven letter positions in the input, the network has a set of 29 input units where the current character is represented as a pattern with only the correspondent character unit activated ("on") and each of the others disactivated ("off").
On the output side, there are 21 units that represent different articulatory features such as voicing and vowel height. Each phoneme is represented by a distinct binary vector over this 21 units set. Stress and syllable boundaries are encoded in 5 additional output units.
Figure 5. Input character units and output phonemes of NetTalk (a second output for stresses can be added).
A training set with the 1000 most common English words, was tested with different net sizes, i.e. with from zero up to 120 hidden units. The best performance with no hidden units gave about 82% correct answers. An output was regarded as correct as long as it was more close to the correct output vector than to any other phoneme output vector. The more hidden units that were used, the better were the learning rate and final performance of the net, 120 hidden nodes gave up to 98% correct output. When the trained network was tested on a 20.000 word corpus (without further training) it generated correct answers in 77% of the cases. After five passes through this larger corpus 90% of the outputs were correct. (Sejnowski & Rosenberg, 1986).
Before the work of Michael Kipp (Kipp, 1998) the problem of finding appropriate dialogue acts for given utterances in large speech corpora was solved through symbolic, decision tree and statistical approaches. Even if the latter showed the best performance, problems due to the known sparse data effect of corpora limited its usability. The corpus used in these experiments was a VERBMOBIL subcorpus containing 467 German appointment scheduling dialogues.
An optimal solution would be to assign each word to its proper input neuron but with the many word tokens of the corpus this was proved far to time and power consuming. When determining a dialogue act, information about the items part-of-speech (POS) is valuable, e.g. when processing unknown words and also as it in some cases can reveal a template-like structure: “How about <ADJECTIVE><NOUN>?” (suggest), “That <VERB> okay.” (accept) (Kipp, 1998). The final design of the system comprised a multiple section vector with one segment for each of the used POS-categories, yielding an input vector of 216 components as described in the following. The POS-categories were divided into groups according to their importance regarding the task. This enabled each POS-segment to use its own representations for the words within it. In a POS of high importance each word is represented by one distinct component. Within a medium-important POS all words are represented by the binary value indicating their position in the word list of the category. Words with low importance, finally, where the interesting information is the category itself, cardinal numbers for instance, were grouped to a single vector component lacking information about the specific tokens (1, 20, 765, …etc. are all cardinal numbers but in this context they give no specific individual information).
The network system used, contained feedforward, partially recurrent network modules for each POS and one selector network which gave the final dialogue act output. The input was fed into the net by a sliding window mechanism, this window sequentially scans the input representations, thus allowing an utterance to be fed into the network moving from the first part to the last, see figure on next page.
The choice of solving this problem with the help of neural nets is due to the robustness of these nets, the parallel and incremental processing and also because neural nets automatically can be adapted to other, new, domains through training (without extra hand-coding of new rules). In Kipp’s work there is no comparison, though, with performances of systems using transformation-based learning.
Figure 7. Kipp’s network system showing the sliding input window, the POS-selecting network modules and
the final output selector network (Kipp, 1998).
The Self-Organizing Map (SOM), also called Kohonen Map from its inventor Teuvo Kohonen, is one of the most widely used neural network algorithms. The learning process is competitive and unsupervised, i.e. no desired output are involved in the training of the net. The first layer of a SOM is the input layer and each node in the second layer (often called the Kohonen layer) is the winner for all input vectors in a region of input space. Each jth node in the Kohonen layer is described by the vector of weights to it from the input nodes: wj = (wj1, wj2, …, wjn). Weight vectors have the same dimension as input vectors and can thus be compared to these by a distance measure estimating the similarity of them. A winner is the node whose weight vector (w1, w2, …, wn) is nearest to an input pattern. The weight vector associated with each node is as near as possible to all input samples for which the node is the winner. At the same time it is as far away as possible to very different samples.
A SOM can be visualized as a, most often two-dimesional, coordinate system where the nodes are topologically ordered according to a prespecified topology. The basic idea in its learning process is that, for each sample input vector the winner and also, what is special for the SOM, the nodes in the winners neighbourhood are positioned closer and closer to the sample vector. Neighbours are nodes within a topological distance (the length of the path connecting the nodes) from a node at a certain time, and the training algorithm decreases this distance with time, as the training goes on. (Kohonen, 1997). Thus, in the beginning of the training phase all nodes tend to have similar weights, close to average input patterns, but as the training proceeds individual output nodes begins to represent different subset of input patterns.
(Honkela et al. 1996) used the SOM to cluster averaged word contexts and then used this data to organize documents in large document collections. Their corpus contained 8800 articles from a Usenet newsgroup, with a total of 2 000 000 words. Non-textual information was first removed and numerical expression as well as common code words were categorised into a few classes of special symbols, seldom occurring words was removed and treated as empty slots. Common general words were also removed from the corpus.
Figure 8. : The basic two-level WEBSOM architecture. a) The word category map first learns to represent
relations of words based on their averaged contexts. The word category map is used to form a word
histogram of the text to be analyzed. b) The word category histogram is then used as input to the second SOM, the document map. (Honkela et al. 1996)
The word forms are first organized into categories on a word category map. With the help of this map a word histogram of the text is formed. The document representation is based on this histogram and explicitly expresses the similarity of the word meanings. This information is then used to produce an organised document map, which provides a general two-dimensional view of the document collection. This latter map has beeen adapted to A WWW-based environment, so that document collections can be explored by a browser. The user may zoom at any map area by pointing to the map image and thereby view the underlying document space in more detail. The clustering between neighboring nodes is shown by using different shades of gray, if the model vectors are close to each other, the shades are light, if they are further apart they are darker. The WEBSOM browsing interface is implemented as HTML- documents and can thus be viewed by a graphical WWW browser. (Honkela et al.1996).
Figure 9. A part of a map for 20 newsgroups (Honkela et al., 1996-2).
The weaknesses and strengths of statistical and neural network approaches can be further analysed, this to combine the two methods, e.g. in clustering dialogue acts to groups and then specifying the members of them. Ideally, the clustering should be based on automatically detected correlations, which neural nets are good at finding.
By using neural nets for mapping tasks, explicit combination of details and intricacies are avoided. Rules for grammar, pronunciation and intonation are very complex and the mapping from text to phonemes comprises combinatorial regularities with many exceptions. Sejnowski and Rosenberg showed that the most important English pronunciation rules could be captured in a context window of a few characters. Combinatorial structures were captured through a distributed output representation (for articulatory features and stress and syllable boundaries, respectively).
It seems that the use of artificial neural network in language models offers a promising methodology to explore linguistic resources of various kinds. Some applications have been discussed in this paper but there is a lot of other interesting work in this active field, as morpho-syntactic disambiguation, for instance (Vlasseva, 1999), (Simov & Osenova, 2001) or extended versions of the SOM, like token category clustering, developed full-text analysis, information retrieval and also interpretation of sign languages (Kohonen, 1997). An important application of the SOM is to visualize very complex data and, as a clustering technique, to create abstractions. The WEBSOM method has lately been further developed in that the word category maps and also the number of documents classified have been considerably enlarged, The largest map so far contains over one million documents.
C. 2002. School of Computing & Information Systems,
Elman, J.L. 1991. Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195–225.
Honkela, T., Kaski, S., Lagus K. and Kohonen T. 1996. WEBSOM - Self-Organizing Maps of Document Collections. Report.
Honkela, T., Kaski, K. and Kohonen T. 1996-2. WEBSOM - A Status Report. Proceedings of STeP'96. Jarmo Alander,
Timo Honkela and Matti Jakobsson (eds.),
Publications of the Finnish Artificial Intelligence Society, pp. 73-78.
Jacobsson, H. 2002. AI-neural nets, lecture notes: lecture 4. Department of computer Science, University College of Skövde.
Kipp, M. 1998.
The Neural Path to Dialogue Acts. In:
the 13th European Conference on Artificial Intelligence (ECAI).
Kohonen, T. 1997. Self-Organizing Maps. 2nd edition. Springer Verlag, Heidelberg.
Malmgren, H. 2002. Nätverk för naturlig klassifikation. http://www.phil.gu.se/ann (2002-03-03).
C.D. and Schütze, H. 2001. Foundations of
Statistical Natural Language Processing. The MIT Press,
K., Mohan, C.K., Ranka, S. 1997. Elements
of Artificial Neural Networks. The MIT Press,
Negishi, M. 1998. Everything that Linguists have Always Wanted to Know about
Connectionism. Department of Cognitive and Neural Systems,
Sejnowski, T. J. and Rosenberg, C. R. 1986. NETtalk: a parallel network that learns to read aloud, Cognitive Science, 14, 179-211.
K.I. & Osenaova, P.N. 2001. A Hybrid System for Morphosyntactic
Disambiguation in Bulgarian. BulTreeBank Project,
S. 1999. Part-Of-Speech Disambiguation in Bulgarian Language via Neural
Networks. Master’s Thesis. Faculty of Mathematics and Computer Science, st.
Kl. Ohridsky University,
1974. Beyond regression: new tools for
prediction and analysis in the behavioural sciences. PhD thesis,
 Artificial neural networks are also referred to as “parallel distributed processing systems” or “connectionist systems” (Mehrotra et al., 1997:1).
 In Elementary Graph Theory a directed graph consists of a set of nodes and a set of connections between pairs of nodes. In a labelled graph each connection is associated with a label identifying some properties of the connection.
 In the XOR problem the desired output is the "exclusive-or" function of the input; the output is 1 (“on”) if either (but not both) of the two inputs is 1; and is a zero (“off”) if the two inputs are the same: either (1,1) or (0,0).
 A feedforward network is an acyclic network in which a node in layer i ONLY is allowed to be connected to nodes in layer i+1. These networks are among the most common neural nets in use (Mehrotra et al., 1997:20).
 „A hidden node is any node that is neither an input nor an output node” (Mehrotra et al., 1997:18)
 The error is the difference between desired (d) and actual (y) system output. One often used error measure in the BP algorithm, is the Summed Squared Error: Ek=(dk1 - yk1)2 + … + (dkn - ykn)2 , where k represent each presented example and 1 to n represent the different input nodes.
 Small variations in the net weighted input cause correspondingly small variations in the output (Mehrotra et al., 1997:12).
 Some researchers dislike the use of the term MLP because of its inappropriate reference to the simple perceptron of Rosenblatt.